NORMALIZATION AND FAMILY FUNCTIONING IN

FAMILIES WITH A CHILD WHO IS TECHNOLOGY DEPENDENT

by

VALERIE A. BOEBEL TOLY

Submitted in partial fulfillment of the requirements

For the degree of Doctor of Philosophy

Dissertation Advisor: Dr. Carol M. Musil

Frances Payne Bolton School of Nursing

CASE WESTERN RESERVE UNIVERSITY

May, 2009

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CASE WESTERN RESERVE UNIVERSITY

SCHOOL OF GRADUATE STUDIES We hereby approve the thesis/dissertation of Valerie A. Boebel Toly candidate for the Doctor of Philosophy degree * (signed) Carol M. Musil

(chair of the committee) John C. Carl Donna A. Dowling Susan Tullai-McGuinness (date) March 12, 2009 *We also certify that written approval has been obtained for any proprietary material contained therein.

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DEDICATION

This dissertation is dedicated in loving memory to my paternal grandmother,

Hedwig Matthias Boebel, whose compassionate care for others, keen perceptiveness and

ability to truly listen left an indelible impression on me and to my father, Carl Paul Boebel,

a true scientist who continually serves as a pattern and inspiration for my life. Also, this

work is dedicated to my husband Louis Toly and sons Joshua and Jason for their

unwavering love and support throughout life’s journey. You are my past, present and

future!

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TABLE OF CONTENTS

LIST OF TABLES……………………………………………………………………..…viii LIST OF FIGURES……………………………………………………………………..…ix ACKNOWLEDGEMENTS……………………………………………………………...…x ABSTRACT…………………………………………………………………….…...….....xii CHAPTER ONE ....................................................................................................................1

Background and Significance ............................................................................................1 Statement of the Problem...................................................................................................3 Purpose of the Study ..........................................................................................................8

Research Questions ........................................................................................................9 Significance of the Study to Nursing ...............................................................................11 Theoretical Framework ....................................................................................................12

Severity of Illness.........................................................................................................22 Depressive Symptoms..................................................................................................24 Normalization...............................................................................................................25 Family Functioning ......................................................................................................28

Definition of Terms..........................................................................................................32 Assumptions.....................................................................................................................34 Relationship of this Problem to the Nursing MetaParadigm ...........................................35 Summary ..........................................................................................................................37

CHAPTER TWO – REVIEW OF LITERATURE ..............................................................39 Introduction......................................................................................................................39 Chronic Illness Within A Family Context .......................................................................40

Necessary Adjustments ................................................................................................40 Positive Gains ..............................................................................................................41 Critical Times...............................................................................................................42 Father’s Perspective .....................................................................................................42 Family Response to Chronic Illness.............................................................................43 Needs of Parents...........................................................................................................44 Unpredictability of Symptoms .....................................................................................46 Theoretical Foundations for Chronic Illness Research ................................................47 Summary of Chronic Illness Within a Family Context................................................49

Psychological Effects of Caring for a Child with Chronic Illness ...................................49 Depressive Symptoms..................................................................................................49 Depressive Symptoms in Parents of Children who are Technology Dependent .........50 Correlates of Depression..............................................................................................51 Depressive Symptoms and Severity of Illness .............................................................52 Summary of Psychological Effects on Parents of a Child with Chronic Illness..........60

The Child Who is Technology Dependent.......................................................................61 Long-Term Home Care ................................................................................................62 Coping..........................................................................................................................62 Cost ..............................................................................................................................62

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“Mothering” Role.........................................................................................................74 Process of Care.............................................................................................................76 Effective Management Factors ....................................................................................84 Social Support ..............................................................................................................86 Home Health Care Nursing Support ............................................................................88 Lack of Discharge Preparation.....................................................................................91 Positive Gains ..............................................................................................................92 Summary ......................................................................................................................93

Normalization as a Management Strategy .......................................................................94 Definition and Attributes .............................................................................................94 Normalization Management Behaviors........................................................................96 Family Management Style Framework........................................................................98 Goals of Normalization..............................................................................................100 Effects of Normalization on Families ........................................................................101 Threats to Normalization ...........................................................................................101 Strategies to Promote Normalization .........................................................................103 Child’s Psychosocial Adjustment ..............................................................................105 Normalization and the Child who is Technology Dependent ....................................107 Summary ....................................................................................................................108

Family Functioning ........................................................................................................109 History of the Concept ...............................................................................................109 McMaster Model of Family Functioning...................................................................109 Feetham Model of Family Functioning .....................................................................110 Wright and Leahey Model of Family Functioning ....................................................111 Structure and Process of Family Functioning ............................................................112 Cultural Context of Family Functioning....................................................................113 Family Development and Family Functioning ..........................................................114 Comprehensive Definition of Family Functioning ....................................................114 Preterm Infants and Family Functioning ...................................................................115 Maternal Mental Health and Family Functioning......................................................115 Severity of Illness and Family Functioning ...............................................................117 Unpredictability of Illness and Family Functioning ..................................................117 Factors that Promote Family Functioning..................................................................117 Normalization and Family Functioning .....................................................................118 Family Functioning and Psychosocial Adjustment of the Child................................119 Summary of Family Functioning ...............................................................................120

Theoretical and Empirical Linkages ..............................................................................120 Child’s Severity of Illness and Maternal Depressive Symptoms...............................120 Severity of Illness and Normalization........................................................................122 Severity of Illness and Family Functioning ...............................................................125 Family Functioning and Maternal Depressive Symptoms.........................................127 Family Functioning and Normalization .....................................................................128 Maternal Depressive Symptoms and Family Functioning.........................................129 Maternal Depressive Symptoms and Normalization..................................................130 Summary of Conceptual Linkages .............................................................................131

CHAPTER THREE - METHODS.....................................................................................132

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Introduction....................................................................................................................132 Study Design ..................................................................................................................133 Sampling ........................................................................................................................134

Criteria for Selection..................................................................................................134 Recruitment of Subjects .............................................................................................135 Sample Size................................................................................................................136

Procedure for Data Collection........................................................................................138 Instruments.....................................................................................................................138

Functional Status........................................................................................................140 Level of Technology Dependency .............................................................................142 Level of Depressive Symptoms .................................................................................144 Normalization.............................................................................................................146 Family Functioning ....................................................................................................149 Demographic Characteristics .....................................................................................150

Data Management ..........................................................................................................153 Data Analysis .................................................................................................................154

Independent Variables/Covariables: ..........................................................................155 Dependent Variable:...................................................................................................155

Research Questions ........................................................................................................156 Protection of Human Rights...........................................................................................164

CHAPTER FOUR - RESULTS .........................................................................................167 Description of Participants.............................................................................................167 Children of Participants..................................................................................................171 Description of Study Variables ......................................................................................175 Description of Measurement Results for Independent/Dependent Variables ................176 Description of Caregiving Variables..............................................................................179 Preliminary Data Analysis .............................................................................................180 Testing Assumptions for Multiple Regression...............................................................181

Secondary Regression Assumptions: .........................................................................183 Research Questions and Hypothesis Testing .................................................................184

Regression Analysis – Family Functioning ...............................................................189 Testing Assumptions for Analysis of Variance (ANOVA) ...........................................191

Mediation Testing ......................................................................................................195 Multiple Regression Analysis - Normalization..........................................................203

CHAPTER FIVE - DISCUSSION.....................................................................................207 Introduction....................................................................................................................207 Summary ........................................................................................................................207 Findings..........................................................................................................................210

Characteristics of Participants....................................................................................210 Characteristics of Participant’s Children ...................................................................211 Caregiving Variables..................................................................................................212 Depressive Symptoms................................................................................................213 Normalization.............................................................................................................217 Family Functioning ....................................................................................................226 Mediator Testing ........................................................................................................228

Instruments.....................................................................................................................229

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Study Limitations ...........................................................................................................231 Study Implications .........................................................................................................233

Clinical Implications ..................................................................................................233 Theory Implications ...................................................................................................236 Policy Implications.....................................................................................................237

Recommendations for Future Research .........................................................................239 Conclusions....................................................................................................................241

APPENDICES ...................................................................................................................244

Appendix A Functional Status II (Revised)....................................................................244 Appendix B Level of Technology Dependency Questionnaire......................................246 Appendix C Normalization Scale ...................................................................................247 Appendix D Center for Epidemiological Studies- Depression (CES-D)........................249 Appendix E Feetham Family Functioning Survey (FFFS) ............................................250 Appendix F Demographic Questionnaire ......................................................................254 Appendix G Introductory Letter .....................................................................................257 Appendix H Telephone Script for Recruitment..............................................................258 Appendix I Resources for Mental Health......................................................................259 Appendix J Consent Form.............................................................................................260 Appendix K HIPPA Release Form .................................................................................263 REFERENCES…………………………………………………………………………...265

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LIST OF TABLES

Table 3.1. Empirical Indicators and Reliability Coefficient .............................................139 Table 4.1. Location of Participant Recruitment (N=103) .................................................168 Table 4.2. Descriptive Statistics – Individual Characteristics of Mothers (N=103) .........170 Table 4.3. Descriptive Statistics – Individual Characteristics of Children (N=103) ........172 Table 4.4. Number of Technologies Used ........................................................................174 Table 4.5. Descriptive Statistics – Type of Technology Used (N=103) ...........................174 Table 4.6. Means, Standard Deviations, Ranges and Skew for Major Study Variable

(N=103)......................................................................................................175 Table 4.7. Functional Status II - Revised (N=103) ...........................................................176 Table 4.8. Depressive Symptoms. Center for Epidemiological Studies – Depression Scale

(CES-D) (N=103).......................................................................................177 Table 4.9. Normalization Scale. “Actual Effect of Chronic Physical Disorder on the

Family” Subscale (N=103).........................................................................178 Table 4.10. Feetham Family Functioning Survey (N=103) ..............................................179 Table 4.11. Descriptive Statistics – Caregiving Variables (N=103).................................180 Table 4.12. Correlations Among Study Variables (N=103) .............................................184 Table 4.13. Summary of Regression Analysis for Family Functioning (N=99)...............190 Table 4.14. Means, Standard Deviations and One-Way Analysis of Variance (ANOVA)

for Three Levels of Technology Dependence and Three Dependent Variables ....................................................................................................194

Table 4.15. Mediating Effect of Depressive Symptoms between Functional Status and Normalization (N=103)..............................................................................196

Table 4.16. Mediating Effect of Depressive Symptoms between Functional Status and Family Functioning (N=101) .....................................................................198

Table 4.17. Mediating Effect of Normalization between Functional Status and Family Functioning (N=101)..................................................................................200

Table 4.18. Mediating Effect of Normalization between Depressive Symptoms and Family Functioning (N=101)..................................................................................201

Table 4.19. Summary of Regression Analysis for Normalization (N=99) .......................204

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LIST OF FIGURES

Figure 1.1. Family Management Style Framework ............................................................14 Figure 1.2. A conceptual model of family functioning in families with a child who is

technology dependent. .................................................................................16 Figure 1.3. Theoretical Substruction...................................................................................21 Figure 4.1. Mediating effects of depressive symptoms between functional status and

normalization .............................................................................................197 Figure 4.2. Mediating effects of depressive symptoms between functional status and

family functioning......................................................................................199 Figure 4.3. Mediating effect of normalization between functional status and family

functioning .................................................................................................200 Figure 4.4. Mediating effect of Normalization between depressive symptoms and family

functioning .................................................................................................202 Figure 5.1. Study Model for Normalization and Family Functioning in Families with a

Child who is Technology Dependent.........................................................216

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ACKNOWLEDGEMENTS

I will be forever grateful for the guidance, encouragement and support from the

members of my dissertation committee. Each member played a significant role in helping

to build the bridge between today and tomorrow for this population of children and their

families. Dr. Carol M. Musil, the chair of my committee, served as a wise counsel

throughout the process of completing my dissertation and encouraged me to strive for

nothing less than excellence. Our thoughtful discussions regarding the conceptual pieces of

this work will not soon be forgotten. Dr. John C. Carl who has long been a real champion

for this population of children and their families was a true collaborator and helped to

demonstrate the strength that can result from interdisciplinary research. I greatly appreciate

his expertise and insight as well as his willingness to take the time out of his already busy

schedule to assist with recruitment efforts for the study. Dr. Donna A. Dowling was

instrumental in encouraging me to pursue doctoral studies and provided valuable expertise

as a pediatric nurse who had worked with this population. Dr. Susan Tullai-McGuinness

was the voice of public policy for the committee and helped to direct attention towards

potential policy changes based on the study results. Also, a thank you to Dr. Gail McCain

who provided much needed thoughtful council at the beginning of proposal development.

I would also like to acknowledge the sources of funding for this study: Alpha Mu

Chapter of Sigma Theta Tau, Frances Payne Bolton School of Nursing Alumni

Association, Society of Pediatric Nurses and the Research ShowCASE Grand Prize Award.

Each of these funding sources was instrumental in being able to conduct this study. The

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Dahms Clinical Research Unit (Grant UL 1RR024989) provided much needed support by

making space available for some of the participant interviews.

My deepest appreciation to the staff from the many clinics and departments at

Rainbow Babies and Children’s Hospital who enthusiastically supported this study and

assisted with identifying potential participants: Technology Dependency Clinic,

Pulmonology, Gastroenterology, Otolaryngology, Surgery, Preterm Infant Follow-up, the

Family Learning Center and the Leroy W. Matthews Cystic Fibrosis Center.

A special thank you to all of the mothers who generously gave of their time to

share their stories thereby giving a window into their world. My deepest admiration for all

of the work that they do. These special mothers are truly profiles of courage.

Finally, I would like to acknowledge and thank all of my family, friends and

colleagues who steadfastly provided encouragement and emotional support to continue

forging ahead on this adventure. I greatly appreciate the flexibility of my colleagues Linda

Boseman, Kathleen Montgomery and Dr. Sister Rita McNulty who willing switched

assignments at crucial times during the completion of my dissertation. I am deeply

indebted to my husband, Louis Toly, my mother Marilyn Boebel and my mother-in-law

and father-in-law Elisabeth and Michael Toly who many times willingly cared for my

children and other family responsibilities so that I could devote time to my studies.

Without their continued support, the completion of this goal would not have been possible.

xii

Normalization and Family Functioning in

Families with a Child who is Technology Dependent

Abstract

by

VALERIE A. BOEBEL TOLY

The purpose of this study was to examine the relationship between child/maternal

factors (child’s functional status, level of technology dependence, mother’s depressive

symptoms, length of caregiving duration, amount of home health care nursing hours, race,

family income and age of the child) and (a) family functioning, as well as (b)

normalization in families with a child who is technology dependent. Additionally, this

study examined whether there are differences in family functioning, normalization and

mother’s depressive symptoms based upon the child’s level of technology dependence

(mechanical ventilation, intravenous nutrition/medication, respiratory/nutritional support).

A descriptive, correlational design was used in this cross-sectional study. Data were

collected using the Functional Status II-Revised, Center for Epidemiological Studies-

Depression, Feetham Family Functioning Survey and a subscale of the Normalization

Scale in face-to-face interviews. Mothers of 103 children who are technology dependent

and living at home comprised the sample. The sample of mothers aged 21-66 years were

73% Caucasian and 27% women of color, who cared for their technology dependent child

aged 7 months-16 years; 73% received solely respiratory/nutritional support. Pearson

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correlations revealed that greater depressive symptoms and less use of normalization were

significantly associated with poorer family functioning. In the hierarchical regression

analysis, 35% of the variance in family functioning was explained, primarily by level of

depressive symptoms. Conversely, several independent variables/covariates were found to

be significant predictors and explained 34% of the variance in normalization. Better child’s

functional status, less depressive symptoms, fewer hours of nursing care, older child and

Non-Caucasian race or Hispanic ethnicity were related to greater normalization efforts.

Statistical analyses for mediation reveal that a mother’s depressive symptoms are a

mediator between the child’s functional status and normalization. ANOVA analysis

showed no statistically significant differences in outcomes based upon the child’s level of

technology dependence. Mothers of children who are technology dependent are at high risk

for psychological distress that can affect overall family functioning. This work will be

pivotal in designing interventions to assist families in the home management of this

vulnerable and growing population of children.

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CHAPTER ONE

Background and Significance

Technological and scientific advances over the past 20 years have increased the

survival of infants and children, particularly those born prematurely. However, the

survival of these children has resulted in a dependency on technology and home care for

continued survival (Guyer, Mac Dorman, Martin, Peters, & Strobino, 1998; Jackson Allen,

2004; Madigan, Youngblut, & Haruzivishe, 1999). Technology assists patients to resume

a more “normal” life yet paradoxically constrains them in their daily lives (Sandelowski,

1993) and results in adverse consequences for the child and family related to social-

emotional, economic and care burdens (Kuster, Badr, Chang, Wuerker, & Benjamin, 2004;

Wang & Barnard, 2004). Additionally, the technology dependency and the chronic

illnesses of these children have a profound impact on the educational and health care

systems (Newacheck & Halfon, 1998).

The Office of Technology Assessment (1987) estimated that 11,000 to 68,000

children, dependent on technology such as mechanical ventilation, intravenous total

parenteral nutrition, respiratory or nutritional support and apnea monitors were receiving

home care in the United States. Subsequent to that report, between 1988 and 1996, there

was an eight-fold growth in home health care due not only to the aged population, but to

technological improvement which saved the lives of infants and children (Madigan, et al.,

1999). Therefore, the number of children dependent on technology and receiving home

care has exponentially increased over the last 20 years.

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There are no current estimates of the number of children who are technology

dependent in the United States today. One survey conducted in 2002 and 2003, randomly

sampled 478 children who were participating in the State of Ohio Home Care/Transitions

Waiver Program. This survey provided projections of children under the age 18 years who

were dependent on technology such as mechanical ventilation, intravenous total parenteral

nutrition and respiratory or nutritional support. Findings include that approximately 38%

of these children or 910 of the 2,395 children participating in the State of Ohio Home

Care/Transitions, were classified as dependent on the aforementioned technology (Personal

communication, J. Nickel, Ohio Department of Jobs and Family Services, December 2,

2004). In another survey, a total of 1,914 children (0.22%) receiving Medicaid in North

Carolina were classified as medically fragile, technology dependent (Buescher, Whitmire,

Brunssen, & Kluttz-Hile, 2006). These surveys, when compared to the OTA estimate from

1987, support the notion that the population of children who are technology dependent has

increased exponentially over the last 20 years.

Equally compelling as the increase in number of children who are technology

dependent is the fact that a disproportionate amount of health care dollars are being

relegated to the care of children with special health care needs. Children who are

technology dependent comprise the high cost segment of children with special health care

needs due to the expenses related to their technological equipment, treatment, therapies and

home health care and hospitalization. Although children with special health care needs

comprise only 15.6% of all children under the age of 18 years, they accounted for 42.1% of

the medical expenditures (excluding dental) in 2000 (Newacheck & Kim, 2005). Children

with special health care needs had statistically significant more physician visits and non-

3

physician visits, medications, and out of pocket costs than other children (p=<.01)

(Newacheck & Kim, 2005). For example, one study found that medically fragile,

technology dependent children receiving Medicaid had medical expenditures of $69,906

per child as compared with $3,181 for a healthy, typically developing child receiving well

child care (Buescher et al., 2006).

Statement of the Problem

Families experience a tremendous cost burden related to at-home care for a child

who is technology dependent. This cost burden includes lost time, social isolation,

increased monetary expenditures, and a toll on psychological health. Furthermore, families

who bring their child who is technology dependent home following hospitalization

undergo major lifestyle changes that can effect family functioning.

Cost Burdens of Caring for a Child who is Technology Dependent at Home

Time

One major factor in the cost burden, is the tremendous amount of time parents often

need to spend providing technical care and administering a variety of treatments, unless the

child has 24-hour home nursing care. Time required for the care and the multitude of

treatments necessary to sustain the child who is technology dependent is daunting

(Heyman et al., 2004; Kirk & Glendinning2004; Wilson, Morse, & Penrod, 1998), and

requires considerably more time than given to healthy children or children who are

chronically ill but not technology dependent. Beyond the time required for technical

treatments, much time is required to manage practical issues related to having a child who

is dependent on technology such as dealing with insurance companies, ordering supplies

and the increased time required to pack necessary supplies for an outing (Torok, 2001).The

4

time factor also includes lost time for family, work, leisure and social activities (Kuster et

al., 2004; Neuss, 2004; Miller Rice, DeVoe, & Fos, 1998; O’Brien, 2001; Torok, 2001).

Social Isolation

Social isolation is another great cost burden related to caring for a child who is

technology dependent at home (Boland & Sims, 1996; Carnevale, Alexander, Daves,

Rennick, & Troini, 2006; Kirk, 2004; Neuss, 2004; O’Brien, 2001; Wang & Barnard,

2004). Frequently, the life of the caregiver, who is in most cases the mother, revolves

around the technology and the necessary care routines. Friends are noted to withdraw;

there is less contact with work colleagues and extended family and fewer social outings

(Boland & Sims, 1996; Kirk & Glendinning, 2004; Tommet, 2003). Often mothers of

children who are technology dependent, experience loneliness and a sense that their lives

are on hold (Neuss, 2004). Many report a lack of available respite care (Carnevale et al.,

2006; Heaton Noyes, Sloper, & Shah, 2005; Kirk, 2004).

Not only do families of children who are technology dependent encounter social

isolation as a cost burden, most families also face exorbitant expenses due to the fact that

not all of the medical bills for medicines, supplies, physician visits, and home health care

are covered by insurance (Newacheck & Kim, 2005). Additional expenditures that

families incur are due to increased use of electricity, heat, telephone, insurance, and

laundry, as well as travel (Kirk & Glendinning, 2004; Glendinning, Kirk, Guiffrida, &

Lawton, 2001). Economic costs also include lost income; often times in a dual income

family, one of the parents, typically the mother, is compelled to quit their job in order to

manage the demands of coordinating all of the visits to physicians, and therapists, and to

5

provide the technical care for the child (Case-Smith, 2004; Miller et al., 1998). Frequently,

there are other siblings as well as other household demands, in addition to the child who is

technology dependent, that make it a necessity for one parent to be a stay-at-home parent.

Often, to make up for the lost income, the working parent must take on a second job or

work overtime (Cavanagh, 1999; Miller et al., 1998; Case-Smith, 2004). Therefore,

economic hardships that the majority of families of children who are technology dependent

face, place increased hardship, worry and strain on families that can potentially effect

family functioning.

Psychological Distress

Psychological distress, another cost burden, is prevalent in parents of children who

are technology dependent. In as many as 75% of families, one or both parents experienced

distress symptoms, suggesting a possible need for psychological intervention (Leonard,

Brust, & Nelson, 1993). Cavanagh (1999) found that about half of caregivers of children

who are technology dependent experienced depressive symptoms that required either

counseling or antidepressant medication. In a longitudinal study of 67 mothers of preterm

infants who were technology dependent, Miles, Holditch-Davis, Burchinal, and Nelson

(1999) found that 45% at discharge and 36% at 12 months had Center for Epidemiological

Studies- Depression (CES-D) scores above 16, indicating risk for depression. Therefore,

research indicates that a large percentage of parents of children who are technology

dependent have significant depressive symptoms and are at risk for clinical depression.

While research indicates that a high percentage of parents of children who are

technology dependent are at risk for depression, it is also important to note the correlates

of depression. Extensive research conducted regarding depression in parents of children

6

with chronic illness, found overwhelming evidence that a key correlate of depression is

functional limitations in the chronically ill child (Silver, Westbrook, & Stein, 1998; Silver,

Bauman, & Ireys, 1995; Frankel & Wambolt, 1998; Lustig, Ireys, Sills, & Walsh, 1996;

Weiss & Chen, 2002). Children who are technology dependent have a variety of

functional limitations that require increased care and monitoring thus contributing to the

findings related to increased incidence of depression in these parents. Other correlates of

depression in these parents, according to the research, are decreased resources (Silver, et

al., 1995), decreased family income (Drotar, Agle, Eckl, & Thompson, 1997; Canning,

Harris, & Kelleher, 1996; Shore, Austin, Huster, & Dunn, 2002), increased number of

health care visits (Ireys & Silver, 1996), unpredictability of symptoms and need to watch

for sudden changes in condition (Ireys & Silver, 1996), and mother unemployed outside

the home (Thyen, Kuhlthau, & Perrin, 1999). Each of these correlates of depression,

described in past research, is present in the lives of parents of children who are technology

dependent. Thus, it is not surprising that such a large percentage of these parents have or

are at risk for significant depression.

In addition to the toll that caring for a child who is technology dependent takes on

psychological health, mothers also face major lifestyle changes that can effect family

functioning. Living with a child is technology dependent also means living with

uncertainty and unpredictability in their lives—described as “living in a house of cards”

(O’Brien, 2001). Loss of privacy, another lifestyle change, is frequently an issue for

families whose child receives pediatric home health care as numerous health care providers

enter and leave the family home at all times of the day disrupting the typical family routine

and dynamics (Cohen, 1999; Koblen, Beier, & Danzer, 2000; Lee, 1996; Murphy, 1997;

7

O’Brien & Wegner, 2002; Torok, 2001; Wang & Barnard, 2004). Additional problems

encountered by parents with a child who is technology dependent are housing adaptations

that must be made in order to accommodate the necessary care equipment (permanent

modifications such as an of addition to house or making a first floor room a mini-ICU)

(Kirk, Glendinning & Callery, 2005). Other modifications include changes in family goals

and activities, modified school experiences and strained family relationships (Kiernan,

1995; Neuss, 2004; Torok, 2001; O’Brien, 2001; Kirk & Glendinning, 2004; Allen,

Simone, & Wingenback, 1994).

There is a dearth of quantitative research studies that have examined the impact of

technology dependency on children living at home, on their families and on society. The

majority of studies have used qualitative methodology. Home care for children who are

technology dependent is encouraged as the result of anticipated cost savings by managed

care in the United States and universal health care plans in the United Kingdom and

Australia (OTA, 1987; Wang & Barnard, 2004). However, the lack of research provides

little evidence for guiding home health care or for establishing effective and efficient

social, fiscal and health care policies. Additionally, there is a lack of evidence regarding

how to improve the lives of children who are technology dependent and their families once

they are discharged home. This information is vitally important for maintaining the mental

health of caregivers as well as positive family functioning.

The Principal Investigator became interested in how families of children who are

technology dependent manage once they are discharged home while working as a pediatric

home health care nurse. This experience led to observations that families who were most

successful providing home care for their child easily integrated the child into family life,

8

normalizing their day to day experience despite all the challenges of the technology and

increased medical care needs, whereas other families struggled with the identical

challenges. The role of families in a child’s adjustment to illness is of paramount

importance. The entire family, as well as individual family members are impacted by the

presence of a child who is technology dependent (Knafl, Breitmayer, Gallo, & Zoeller,

1996). Children who are chronically ill as well as technology dependent create unique,

special challenges for families because they require considerable amounts of time and care.

Nurses are best suited to assist these families by enabling and empowering families to

function optimally in the management of the child who is technology dependent by

identifying and augmenting family strengths.

Purpose of the Study

The purpose of this study was to explore how mothers respond to and manage the

special challenges of children who are technology dependent after they are discharged

from the hospital to home. This descriptive, correlational study explored the relationship

between child/maternal factors (child’s functional status, level of technology dependence,

mother’s depressive symptoms, length of caregiving duration, amount of home health care

nursing hours, race, family income and age of the child) and (a) family functioning as well

as (b) normalization (how a family manages both family life and the child’s complex

medical needs/treatments) in families with a child who is technology dependent.

Additionally, this study examined whether there are differences in family functioning,

normalization and mother’s depressive symptoms based upon the child’s level of

technology dependence (Group 1 mechanical ventilation, Group 2 intravenous

9

nutrition/medication, Group 3 respiratory/nutritional support) using the Office of

Technology Assessment (1987) rubric.

No quantitative research could be located that examined efforts at normalization

and family functioning as reported by mothers with children who are technology

dependent. More research is needed to examine the relationships among child’s severity of

illness, mother’s depressive symptoms, normalization and the resultant impact on family

functioning in mothers of children who are technology dependent after adjusting for

caregiving duration, amount of home health care nursing hours, income, race and age of

the child who is technology dependent. This research is an essential foundation for the

design of effective interventions to assist families in the home management of this

vulnerable and growing population of children.

The long term goals of this study are threefold; to provide evidence to guide

pediatric home health care nursing practice, to provide evidence for intervention

development that would promote healthy outcomes for the family and ill child alike and, to

provide evidence to guide policy development for the child who is technology dependent

in the United States.

Research Questions

Five research questions will be addressed in this study:

1a. What are the relationships of mother’s depressive symptoms, child’s

severity of illness (functional severity and level of technology dependence)

and normalization efforts, with family functioning in families with a child

who is technology dependent? 1b. Do these relationships hold after

adjusting for length of caregiving duration, amount of home health care

10

nursing hours, race, family income and age of the child who is dependent on

technology? (Correlation Matrix, Multiple Regression)- F statistic

2. Are there differences in a) family functioning and b) normalization efforts

and c) mother’s level of depressive symptoms based on the child’s level of

technology dependence (3 levels)? (MANOVA)-F statistic

3. Do a mother’s depressive symptoms have a mediating effect on the

relationship between the child’s severity of illness (functional status) and

(a) normalization and (b) family functioning in mothers with a child who is

technology dependent? (Correlation Matrix, Mediation using

Hierarchical Multiple Regression)-F statistic

4. Does normalization have a mediating effect on the relationship between (a)

child’s severity of illness (functional status) and family functioning, (b)

depressive symptoms and family functioning in mothers with a child who is

technology dependent? (Correlation Matrix, Mediation using

Hierarchical Multiple Regression)-F statistic

5 a. What are the relationships among mother’s depressive symptoms, child’s

severity of illness (functional status, level of technology dependence),

family functioning on normalization efforts in families with a child who is

technology dependent? 5b. Do these relationships hold after adjusting for

length of caregiving duration, amount of home health care nursing hours,

race, family income and age of the child who is dependent on technology?

(Correlation Matrix, Multiple Regression)- F statistic

11

Significance of the Study to Nursing

Children who become dependent on technology are a significant concern for

nursing and society in general. According to Newacheck and Halfon (1998), a medical

disability profoundly impacts the child, the educational system, and the health care system.

This phenomenon is of particular significance to nursing not only due to its impact on the

child who is technology dependent but on the family as a whole. In 1994, registered

nurses were responsible for 73.2% of the pediatric home health care visits (Madigan et al.,

1999). As such, nurses interface in the home with families of children with complex health

care needs. Furthermore, children requiring home health care are often technology

dependent and cared for by their families who are then at significant risk for psychological

distress (Leonard et al., 1993). For example, preterm infants who are technology

dependent, particularly those with depressed mothers, are at risk for altered parent-child

interaction with possible deleterious effects on cognitive development (Miles et al., 1999).

Families often require assistance in the day to day management of the child’s

chronic condition. Nurses, therefore, are well situated to assist these families in the

management process once the child who is technology dependent is discharged from the

hospital to home. One way that nurses can best help these families is by enabling and

empowering families to function optimally in the management of the child who is

technology dependent by identifying and augmenting family strengths.

To date, the preponderance of studies regarding parents of children who are

technology dependent have used qualitative methodology. Few studies, using quantitative

methodology, have addressed how the family responds to and manages the special

challenges of a chronically ill child who is dependent on technology. Furthermore, no

12

studies have been located that have addressed the use of normalization within the Family

Management Style Framework (Knafl & Deatrick, 2003) as a means to assess which

families with a child who is technology dependent, are at greatest risk for dysfunction.

Additionally, no studies have simultaneously examined both depressive symptoms and

normalization as they relate to mothers of children who are technology dependent. The

study proposed here will address these gaps by systematically assessing the response of

mothers to the experience of caring for a child who is technology dependent.

Theoretical Framework

The orienting framework guiding this study is the Family Management Style

Framework developed by Knafl and Deatrick (2003) (Figure 1.1). The Family

Management Style Framework “describes how a family manages both family life and a

child’s serious health problems” (Deatrick et al., 2006, p. 20), in essence, how the “family

incorporates the demands of childhood illness into family life” (Knafl & Deatrick, 2003, p.

234) and how the family as a unit consistently responds to a child’s chronic illness

(Deatrick et al., 2006). The framework considers patterns of family management in a

systematic manner and allows for the planning of individualized care for families based on

their type of family management style. This study explored how the severity of a child’s

chronic illness (functional severity and level of technology dependence), mother’s

depressive symptoms, and efforts at normalization impact family functioning. The family

management styles reflect varying degrees of normalization used by families and are

impacted by a combination of the family’s definition of the situation, management

behaviors, and perceived consequences as well as relate to such outcomes as individual

13

functioning (depressive symptoms) and family functioning (Deatrick et al., 2006), some of

the concepts that are included in this study.

Most families who have children with serious illnesses eventually view their

children and their lives as normal and manage the illness-related demands successfully

(Deatrick et al., 2006). However, not all families see their lives as normal, and those who

do reach this point often use a variety of strategies over time as they undergo a continual

process of adjustment. The Family Management Style Framework describes ways families

define and manage illness-related demands and the resultant consequences for family life

(Deatrick et al., 2006). “Within the Family Management Style Framework, the general

issues/dimensions common to all families compose the major interacting dimensions of the

model. These dimensions or components include how family members define and manage

their situation, as well as what consequences they perceive for family life” (Deatrick et al.,

2006, p. 20).

The Family Management Style Framework is comprised of three components that

include the following (Figure 1.1): 1) definition of the situation, or the identification of

significant events and the subjective meaning family members attribute to the child’s

identity (normal and capable versus tragic and vulnerable), illness view (life goes on versus

serious/hateful), parent mutuality (shared versus different views of the illness and illness

management) and management mindset (ease or difficulty with treatment regimens); 2)

management behaviors or the behavioral accommodations made by family members to

manage on a daily basis such as the parenting philosophy (parent’s goals, priorities, and

values that guide the approach and strategies for illness management), and management

approach (parent’s assessment of development of a routine and strategies for managing the

14

illness and incorporating the illness into family life) and 3) perceived consequences or

whether the illness is in the foreground or background in family life such as the family

focus (balance between illness management and other aspects of family life) and future

expectations (parental assessment of illness implications for child’s/family’s future) (Knafl

& Deatrick, 2003; Deatrick et al., 2006). All three components of the Family Management

Style Framework; definition of the situation, management behaviors and perceived

consequences comprise a pattern of family response (Family Management Style) to a

child’s chronic illness and explicates how families incorporate the illness into family life

(Knafl & Deatrick, 2003).

Figure 1.1. Family Management Style Framework

The Family Management Style Framework also includes socio-cultural context.

The socio-cultural context are family members’ perceptions of contextual factors that

influence how the family defines and manages the illness, as well as any consequences the

15

family perceives the illness has; in other words, perceived influences on the family’s

management of the chronically ill child (Deatrick et al., 2006). Examples of contextual

factors include cultural practices and beliefs, as well as finances, personal/individual

factors, the health care provider’s level of support or the supportiveness of the health care

system (Deatrick et al., 2006). Therefore, The Family Management Style Framework takes

a holistic view of how the family manages family life as well as the child’s chronic illness

including the family’s definition of the situation, management behaviors, perceived

consequences and the socio-cultural context.

The Family Management Style Framework has guided a number of previous

studies of children with chronic conditions such as cancer, diabetes, asthma, cystic fibrosis,

juvenile rheumatoid arthritis, gastrointestinal and renal conditions and ventilator

dependence (Knafl & Deatrick, 2003) however all used qualitative methodology with the

exception of Murphy (1994) and Fleming et al. (1994). There is currently no tool available

to measure family management styles nor is there any model that depicts this study’s

identified concepts as well as their relationships; therefore, it was necessary for the purpose

of this study to develop a new conceptual framework (Figure 1.2). This conceptual

framework is based on a synthesis of the theoretical and empirical literature and

specifically incorporates theoretical underpinnings from several sources; the Family

Management Style Framework (Knafl & Deatrick, 2003), the Normalization Scale

(Murphy, 1994) and Feetham’s conceptualization of family functioning (Roberts &

Feetham, 1982).

The Family Management Style Framework (Knafl & Deatrick, 2006) (Figure 1.1)

was used as an orienting framework because it provides perspective on the idea of

17

These goals help to direct the efforts that will be used to manage the child’s illness

and ultimately affects their use of normalization and thus the Family Management Style

employed (Murphy, 1994). These management behaviors are another major component of

the Family Management Style Framework.

Therefore, a parent’s response to a chronically ill child who is technology

dependent is shaped in large part by their definition of the situation. A mother’s depressive

symptoms and the child’s severity of illness (functional severity, level of technology

dependence) (Figure 1.2) affect their definition of the situation and perceived

consequences and thus management behaviors, ultimately affecting normalization efforts.

Finally, as depicted in the Family Management Style Framework (Figure 1.1)

normalization efforts affect family functioning, an outcome.

In summary, parents who showed high emotional distress such as an increased

number of depressive symptoms have reported that the child’s illness is disruptive and had

a major impact on family life (Frankel & Wamboldt, 1998). Depressive symptoms,

therefore, can affect a mother’s definition of the situation, one of the major components of

the Family Management Style Framework that helps determine normalization efforts in a

Family Management Style and ultimately affects family functioning. Additionally,

according to the proposed theoretical framework, the severity of a child’s illness also

affects a mother’s definition of the situation, influence normalization efforts (Murphy,

1994) and ultimately impact family functioning (Frankel & Wamboldt, 1998).

The Normalization Scale (Murphy & Gottlieb, 1992) also influenced the conceptual

framework and was chosen for this study as it is currently the only instrument available to

quantitatively measure normalization. This instrument was primarily derived from

18

attributes of normalization as conceptualized by Knafl and Deatrick (1990). Items for this

scale were developed based on the original conceptualizations of Knafl and Deatrick that

includes four domains of normalization: 1) acknowledgement of the existence of the

impairment; 2) definition of family life as normal i. e. parents define and perceive their

child/family as similar to families without a chronically ill child, the child’s health

treatments are integrated into family life, the child with a chronic illness and their health

treatments are not the central focus of the family; 3) family minimizes the social

consequences of their situation i. e. parents perceive that others define their child and

family as basically normal, others treat the family and chronically ill child as normal; 4)

family engages in behaviors that demonstrate the family’s normality i. e. the family carries

out activities to demonstrate to others that their family behaves and acts like other families

who do not have a chronically ill child.

The conceptual model guiding Murphy’s (1994) study included the Family

Management Style Framework. Questions from the Normalization Scale reflect the major

components (definition of the situation, perceived consequences, management behaviors)

of the Family Management Style Framework. Therefore, the Normalization Scale (Murphy

& Gottlieb, 1992) is guided by the conceptualizations of normalization and the Family

Management Style Framework by Knafl and Deatrick (1990) and is congruent with the

concept of normalization that will be examined in this study.

The conceptual framework for this study also draws from Feetham’s

conceptualization of family functioning (Roberts & Feetham, 1982). Until the Feetham

Family Functioning Survey (FFFS) was developed, measures of family functioning only

addressed the relationship between individual members of the family such as the

19

parent/child or husband/wife. However, family functioning is greater than the

relationships between the immediate family members. Feetham identified and measured

two additional relationship aspects of family functions: the relationship between the family

and subsystems (relatives, friends and neighbors) and the relationship between the family

and the broader community (schools and place of employment) (Roberts & Feetham,

1982). Thus, the FFFS assesses intra-family relationships, relationships between the family

and external social units and the functional task-oriented subsystems of the family.

Feetham’s work drew from the family ecological framework (Bronfenbrenner,

1993) that identifies the family as the basic unit and includes the nested parts that make up

the system, the family’s relationships, the environment and the tasks performed by the

family resulting from the relationships of the parts. Central to Bronfenbrener’s ecological

framework is a view of development as an evolving process of development as an evolving

process of organism-environment interaction. The family system is thus dynamic and

continually in a state of change and adaptation whereby change in one layer ripples

throughout and affects the other layers i.e. subsystems and the broader community

(Paquette & Ryan, 2001). Therefore, Feetham’s conceptualization of family functioning

influenced the view of family functioning for this conceptual framework, particularly her

belief that the family system is dynamic, in a continual state of change and adaptation and

can be affected by all the subsystems with which the family interacts.

Based on a synthesis of the theoretical and empirical literature, the Family

Management Style Framework (Figure 1.1), the Normalization Scale (Murphy, 1994) and

Feetham’s conceptualizations of family functioning, this study proposed (Figure 1.2) that

the child’s severity of illness, mother’s depressive symptoms and the efforts at

20

normalization all directly affect family functioning. It is also proposed that there is a direct

correlation between child’s severity of illness (functional status and level of technology

dependence), in particular functional status, and mother’s depressive symptoms (Canning,

Harris, & Keller, 1996; Lustig et al., 1996; Silver et al., 1995; Silver et al., 1998; Frankel

& Wamboldt, 1998; Weiss & Chen, 2002). The child’s severity of illness, which includes

the child’s functional status and level of technology dependence, affects the mother’s level

of depressive symptoms, and is hypothesized to have direct effects on normalization efforts

and family functioning. Mother’s depressive symptoms are conceptualized as a mediator

between child’s severity of illness and family functioning as well as a mediator between

child’s severity of illness and normalization.

Normalization is proposed to mediate the relationship between child’s severity of

illness and family functioning as well as mother’s depressive symptoms and family

functioning. It is hypothesized that greater severity of the child’s illness, greater depressive

symptoms in mothers will result in lower levels of normalization. Lower levels of

normalization are hypothesized to result in poorer family functioning. Knafl and Deatrick

(2003) conjecture that normalization effort is antecedent to family functioning outcomes as

depicted in Figure 1.1. Normalization differs from family functioning conceptually

because it does not include the inter-relationship between the family and the subsystems

(relatives, friends and neighbors) or community (Figure 1.3). Additionally, normalization

differs from family functioning because it presupposes that there is an alteration in health.

Figure 1.3 provides a theoretical substruction of constructs, concepts, variables in the

theoretical system as well as the empirical indicators and scores/values for the operational

system in this study.

Theoretical

System Constructs Child’s Health Mother’s Mental

Health Family Management

Concepts

Severity of Illness

Depressive Symptoms

Normalization Family

Functioning

Variables

Technology Functional Dependence Severity

Level of Depressive Symptoms

Level of Satisfaction Normalization with Relationships

Operational System

Empirical Indicators

Level of Functional Technology Status II (R) Dependence OTA Groups (1987)

CES-D

Normalization Feetham Family Scale Functioning Survey

Scores/ Values

Nominal Interval

Interval

Interval Interval

Figure 1.3. Theoretical Substruction

21

22

Severity of Illness

While the main model guiding this study is the Family Management Style

Framework, the underpinnings of the theoretical framework come from a variety of

sources. The first concept in this study, the child’s severity of illness, is based on work

by Stein et al. (1987). The concept of severity of illness is challenging to quantify,

according to Stein et al. (1987), because clinical manifestations of an alteration in health

are an interaction between biological, genetic and environmental components. Illness

severity includes physiological severity, functional severity, technology dependence, and

burden of illness however, in this study, only two will be examined; functional severity

and technology dependence.

Functional Severity

Functional severity, which reflects the direct effect of an alteration in health on a

person, is the effect of a chronic health condition on the child’s ability to perform age-

appropriate daily life activities (Stein et al., 1987). Functional severity is manifestations

of a chronic health condition that interfere with a child’s performance of the full range of

age-appropriate behaviors such as communication, mobility, energy, play, sleep, eating

and toileting pattern (Stein et al., 1987). It is hypothesized that functional severity will

affect mother’s depression, normalization, and family functioning. Few studies

examining the impact of caring for a child who is technology dependent on parents have

used a standardized instrument to measure functional severity (Kuster et al., 2004;

Heyman et al., 2004). Most studies have used unstandardized measures with no reported

reliability, or those developed for adults (Leonard et al., 1993; Patterson, Leonard &

Titus, 1992; Teague et al., 1993; Miles et al., 1999). The Functional Status II-Revised

23

(FSII-R) was used in this study because it was specifically developed to assess the health

status of children with chronic illness. This revised instrument was developed using

children with significant chronic illness, as well as children with and without ongoing

health conditions. Therefore, use of a standardized instrument to measure functional

severity will strengthen the findings regarding relationships found between the variables.

Technology Dependence

Technology dependence is defined as the “short-or long-term reliance on devices

and techniques to evaluate or to satisfy or resolve health-related needs or problems”

(Sandelowski, 1993, p. 37). The Office of Technology Assessment (1987) provides

further detail in its definition of technology dependence. Their definition includes the

need of “substantial and ongoing nursing care to avert death or further disability. This

definition is independent of the setting of care or the particular credentials of the

caregiver” (p. 3) and may be provided by either a professional nurse, a trained parent or

trained lay caregiver (OTA, 1987). Children, who are technology dependent, have one or

more chronic illnesses and frequently have functional limitations that can be considered a

disability. The term, children who are technology dependent, encompasses those children

who are chronically ill, technology dependent and possibly disabled.

Technology dependence is hypothesized in this study to affect mother’s

depression, normalization, and family functioning. Few quantitative studies have closely

examined these variables together. One study by Fleming et al. (1994), found that

depression did not vary significantly among the four groups of technology dependence as

delineated by the Office of Technology Assessment (OTA, 1987). A study by Leonard

and colleagues (1993), concur with these findings that there were no statistically

24

significant differences in parental scores for psychological distress among the four groups

of technology dependence (OTA, 1987). Miles et al. (1999) did not find any difference

in the correlation between level of technology dependence and maternal depressive

symptoms, however a newly developed tool to measure technology dependence was

used.

Most studies used the OTA (1987) rubric to define four groups of technology

dependence based on the types of technological equipment used (Fleming et al., 1994;

Leonard et al., 1993). However, Miles et al. (1999) developed a tool that examined not

only the level of technology dependence but the acuity of the child as well as the burden

of caregiving. This scale is published only as part of a grant report; the psychometric

properties are not published in the literature. This scale used the OTA (1987) rubric for

technology as a guide and added technologies as well as 10 categories of medications.

Depressive Symptoms

An important factor related to the impact of a child’s illness on the family may be

the general emotional health and stability of the parents, especially the mother. Mothers’

functioning seems to impact the general functioning of other members of the family

(Frankel & Wamboldt, 1998). How sick the child actually is and how much a family’s

lifestyle is limited by the disease affects a parent’s perception of the illness’ and therefore

impacts family functioning (Frankel & Wamboldt, 1998). Parents who showed high

emotional distress reported that the child’s illness is disruptive and had a major impact on

family life (Frankel & Wamboldt, 1998). Therefore, emotional distress in the form of

depressive symptoms and severity of illness, more specifically, functional severity and

25

technology dependence, play an important role in perception of impact of illness on

family functioning.

As alluded to above, a mother’s depressive symptoms are hypothesized to directly

effect normalization and family functioning. The concept of depression is an alteration in

mental health that results from ineffective responses by the person to changes in the

environment (Foreman, 1997). Depression refers to a range of symptoms from a

subclinical “blue mood” state and general feelings of hopelessness to a major depressive

disorder (Foreman, 1997). The depressive symptoms then represent dimensions of the

concept of depression, and include depressed mood, feelings of guilt and worthlessness,

helplessness and hopelessness, psychomotor retardation, loss of appetite and sleep

disturbance (Radloff, 1977). Many studies examining the level of depressive symptoms

among parents of children who are technology dependent used the Center for

Epidemiological Studies- Depression Scale (CES-D) (Fleming et al., 1994; Miles et al.,

1999; Teague et al., 1993; Heyman et al., 2004; Kuster, 2002). Miles et al. (1999) found

that mothers with decreased satisfaction with family (family functioning) using the

Family Apgar Scale had higher levels of depressive symptoms (p=<.05) on the CES-D.

The CES-D will be used in this study because it was specifically designed to measure

depressive symptoms in the general population, it is quick and easy to administer and

therefore well suited for use with mothers who have children who are technology

dependent.

Normalization

Normalization is a concept that was developed and refined by Knafl, Deatrick and

colleagues since the 1980s (Deatrick, Knafl, & Murphy-Moore, 1999; Knafl & Deatrick,

26

1986; Knafl & Deatrick 2003) and is the foundational concept of the Family Management

Style Framework (Deatrick et al., 2006). Normalization includes cognitive and

behavioral dimensions and is defined as a pattern of family response to a child with a

chronic illness, an ongoing process of accommodating to the child’s evolving social,

emotional and physical needs (Deatrick, Knafl & Walsh, 1988; Morse, Wilson & Penrod,

2000). This includes “adjusting the environment to provide normal life experiences that

will meet the child’s evolving social, physical, intellectual and emotional needs”

(Murphy, 1994, p. 10) while at the same time managing family life and activities so that

they can lead as close to “normal” family life as possible (Murphy, 1994). Attributes of

normalization include that a family “(a) acknowledges the condition and its potential to

threaten their lifestyle, (b) adopts a ‘normalcy lens’ for defining the child and family, (c)

engages in parenting behaviors and family routines that are consistent with ‘normalcy

lens’, (d) develops a treatment regimen that is consistent with a ‘normalcy lens’, and (e)

interacts with others based on a view of child and family as normal” (Deatrick et al.,

1999, p. 211).

Therefore, based on the above attributes of normalization, a family’s definition of

“normal” is guided by a philosophical approach and not necessarily the reality of the

situation, primarily because parents choose to attend to what is “normal” and disregard

what is abnormal about their situation (Murphy, 1994). Families, therefore, modify and

redefine “normal” over time to fit their current circumstances and ongoing uncertainty

(Rehm & Franck, 2000). Consequently, the child with a chronic illness, or in the case of

this study, the child who is technology dependent, is treated as “normal”, living as close a

life like others in the outside world without illness so as not to be seen as deviant from

societal expectations; thus enabling them to “fit in” (Morse et al., 2000; Young, 1995).

27

Therefore, reality is reconstructed (“normalcy lens”) to emphasize those aspects of life

that remain unchanged despite the chronic illness. In conclusion, normalization is used to

manage the disparity between the preferred views of their family life as “normal” and the

problems and challenges they face in their everyday life (Rehm & Franck, 2000).

Qualitative studies of families with children who have a chronic illness, have

found that “families often come to view their child and their lives as normal. They

manage illness-related demands using family management styles that sustain usual

patterns of family and child functioning” (Deatrick et al., 2006, p. 19). Therefore,

families’ efforts at normalization have much to do with family functioning. In a

triangulated study of families of children with chronic conditions, Knafl and Zoeller

(2000) found an association among family management styles and family functioning

using the Feetham Family Functioning Survey. Families who used the Floundering

Family Management Style, the lowest level of normalization, had significantly less

satisfaction with family life (poorer family functioning) than families using the other

styles with higher levels of normalization (Knafl & Zoeller, 2000).

Paterson’s “Shifting Perspectives Model of Chronic Illness” (2001) also guided

this study’s conceptualization regarding the presence of a chronic illness in a family

member and their subsequent efforts at normalization. This model is also reflected in the

Family Management Style Framework particularly in one of the major components,

perceived consequences. Paterson’s Model (2001) posits that living with a chronic illness

is an ongoing, continually shifting process that includes an individual’s or family’s ever

changing perspectives about the disease including elements of wellness and illness.

Living life with chronic illness is an ongoing, continually shifting process whereby

illness or wellness-in-the foreground perspectives have specific functions. This model

28

helps explain variations in attention to symptoms over time. Furthermore, in this model,

perception of reality is how the individual with chronic illness and family members

interpret and respond to illness.

An illness-in-the-foreground perspective of chronic illness focuses on sickness,

loss, burden and suffering. The chronic illness is viewed as destructive to self and others

and individuals are absorbed by the illness experience. In a wellness-in-the-foreground

perspective, chronic illness is viewed as an opportunity for a change in relationship with

the environment and others. The fact that some describe their health as good or excellent

despite impaired physical functioning is not distortion of reality, but rather, revisioning of

what is possible and “normal” (Paterson, 2001). This model also fits well with views

held in the Family Management Style Framework regarding perspective in one of the

dimensions “definition of the situation” and helps to explain how families develop the

“normalcy lens” as described in the attributes of normalization.

Family Functioning

The final component of this model is the outcome variable of family functioning.

In this study, it is hypothesized that a child’s severity of illness, mother’s depressive

symptoms and efforts at normalization will effect family functioning. Clawson (1996)

examined children with chronic illness following discharge from the hospital. These

children are cared for by their families at home, placing increased demands on the family

due to the numerous treatments, health care appointments and disruptions in the every

day routine. This disruption requires a substantive change in the structure and

functioning of the family frequently requiring a redistribution of responsibilities and roles

(Clawson, 1996) ultimately affecting family functioning.

29

The concept of family functioning was greatly influenced by Roberts and

Feetham’s (1982) work. When initially developed in the 1950s, the concept of family

functioning and the ensuing McMaster Model of Family Functioning had structural-

functional theory as its base. It placed emphasis on the family meeting member’s

physical and psychological needs of food, clothing and socialization. It also emphasized

family structure, relationships, and organization as well as transactional patterns

(Goldenberg & Goldenberg, 1991).

Suzanne Feetham extended the work on the concept of family functioning in the

1970’s and developed The Feetham Family Functioning Survey (FFFS). According to

Feetham, family functioning refers to “those activities and relationships among and

between persons and the environment which in combination enable the family to

maintain itself as an open system” (Roberts & Feetham, 1982, p. 231). Her contributions

to the concept of family functioning was the inclusion of the family ecological

framework (Bronfenbrenner, 1979), general systems theory and Duvall’s model of family

development.

The family ecological framework identifies the family as the basic unit, and

includes the nested parts that make up the system, the family’s relationships, the

environment, and the tasks performed by the family resulting from the relationships of

the parts. The focus of the ecological framework is the interdependence of family

members with the environment and with one another. According to the ecological

framework, the family system is dynamic and continually in a state of change and

adaptation. Change in one layer ripples throughout the other layers. Layers or subsystems

of the ecological framework include microsystems (family, child care environment,

school), the relations between the individual and his or her immediate setting,

30

mesosystems (connects structures such as parents and teachers), the relations among

various settings in which the individual is involved, exosystem (larger social system that

is an extension of the mesosystem such as the mass media, government agencies,

educational or employment systems that influence microsystems) and macrosystem

(cultural, economic, social, political philosophy that influence other layers)

(Bronfenbrenner, 1993; Paquette & Ryan, 2001).

There is some overlap in the theories used for the concept of family functioning in

the FFFS. The structural-functional theory, family ecological framework and general

systems theory all use systems theory as its base. Duvall’s family development model

intersects with systems theory due to the fact that each family is a system where “the

whole is greater than the sum of its parts” (von Bertlannfy, 1968). In summary, the FFFS

is based on the McMaster Model of Family Functioning and includes components of

structural-functional theory, general systems theory, the family ecological framework and

Duvall’s model of family development.

Until the FFFS was developed, measures of family functioning only addressed the

relationship between individual members of the family such as the parent/child or

husband/wife. Feetham’s work influenced the view of family functioning for this study

because family functioning is greater than the relationships between the immediate

family members. Feetham identified and measured two additional relationship aspects of

family functions: the relationship between the family and subsystems (relatives, friends

and neighbors) and the relationship between the family and the broader community

(schools and place of employment).

The FFFS will be used in this study because it measures the parent’s perception

and level of satisfaction with relationships between individuals in the family, the family

31

and subsystems and between the family and the community. While this instrument was

designed to be administered to either parent, the mother was chosen to be the participant

because she is most often the primary caregiver and also typically accompanies the child

to clinic visits and would therefore be more accessible for recruitment purposes. This

measure is optimal because it uses the Porter Format (Roberts & Feetham, 1982) that

allows for a measurement of a discrepancy score between achieved and expected levels

of family functioning. The discrepancy score is the critical measure of overall family

functioning (Hock-Long, 1997) and will be used to conduct statistical analysis in this

study. The Porter format also decreases the risk of social desirability and controls for

cultural and ethnic diversity since each item is valued by the participant. An importance

score is also obtained along with the discrepancy score, and can be used to gain insight as

to the importance of particular aspects of family functioning (Roberts & Feetham, 1982).

The FFFS is also the optimal measure to use because it was developed for and

first tested on parents who had a child with myelomeningocele, a chronic condition that

involves long term, complex care. Children with myelomeningocele require a variety of

treatments that is somewhat similar to, but not as complex, in most cases, as children who

are technology dependent and generally not life-threatening. The FFFS was used in a

prior study of mothers with children who were dependent on mechanical ventilation

(Hock-Long, 1997).

Theoretically, family functioning can be viewed as a process that can be measured

at a particular point in time. Bauman (2000) described family functioning as a

phenomenon related to the reciprocity between the family and illness/wellness that is

linked with family structure and process. A description of activities related to family

functioning suggests that family functioning is a process. The process-based view of

32

family functioning includes a broad range of behaviors and implies an inter-relationship

between the individual and the family and the individual and the environment. These

behaviors have been described as those activities that are essential to family survival such

as socialization, protection, procreation, education and economic concerns (Roberts &

Feetham, 1982).

Definition of Terms

The conceptual and operational definitions for the study variables are as follows:

Functional Severity

Conceptual definition: the effect of a chronic health condition on the child’s

ability to perform age-appropriate behaviors, daily life activities and is applicable where

multiple conditions coexist (Stein et al., 1987).

Operational definition: behavioral manifestations of illness that interfere with a

child’s performance of the full range of age appropriate activities such as communication,

mood, energy, play, sleep, and eating patterns as measured by the Functional Status II-

Revised (Stein & Jessop, 1990).

Technology Dependence

Conceptual definition: “short-or long-term reliance on devices and techniques to

evaluate or to satisfy or resolve health-related needs or problems” (Sandelowski, 1993, p.

37) and includes the need of “substantial and ongoing nursing care to avert death or

further disability...independent of the setting of care or particular credentials of the

caregiver” (OTA, 1987, p. 3) and may be provided by either a professional nurse, a

trained parent or trained lay caregiver (OTA, 1987).

33

Operational definition: The level of technology necessary to satisfy or resolve

health-related needs or problems as categorized by the Office of Technology Assessment

(OTA, 1987): Group 1: children dependent at least part of the day on mechanical

ventilation; Group 2: children requiring prolonged intravenous administration of

nutritional substance or medications; Group 3: children requiring daily device-based

respiratory or nutritional support. Group 4: children with prolonged dependence on other

medical devices such as apnea monitors, urinary catheters. Groups are designed to be

mutually exclusive, however, if the child requires technology from more than one group,

he/she is considered as part of the applicable group with the lowest number (OTA, 1987).

Level of Depressive Symptoms

Conceptual definition: an alteration in mental health state that results from

ineffective responses to change in the environment based on negative appraisal of

internal/external stimuli. Symptoms can range in magnitude from a mild subclinical

“blue mood” state and general feelings of hopelessness to a major depressive disorder

(Foreman, 1997; Clark, Beck, & Alford, 1999).

Operational definition: current level of depressive symptomatology, including

depressed mood, feelings of guilt and worthlessness, helplessness and hopelessness,

psychomotor retardation, loss of appetite and sleep disturbance with emphasis on the

affective component as measured by the Center for Epidemiologic Studies Depression

Scale (CES-D) (Radloff, 1977).

Level of Normalization

Conceptual definition: a way that families respond to and manage a child with a

chronic condition. It is a process that families use over time that redefines what “normal”

34

is to fit their current circumstances (Rehm & Franck, 2000). Normalization includes

cognitive and behavioral dimensions and “adjusting the environment to provide normal

life experiences that will meet the child’s evolving social, physical, intellectual and

emotional needs” (Murphy, 1994, p. 10).

Operational definition: the adjustment families make over time when they have a

child who is technology dependent to manage family life and activities so that they can

lead as close to “normal” of a family life as possible as measured by the “Actual Effect of

the Chronic Physical Condition on the Family” subscale of the Normalization Scale

(Murphy & Gottlieb, 1992).

Family Functioning

Conceptual definition: an ongoing, dynamic process that changes with

developmental stages and crisis events and is comprised of inter-relationships and inter-

dependent parts, is influenced by cultural context, member’s expectations and perceptions

and has the goal of balancing equilibrium and disequilibrium such that individual and

family growth can occur and health is promoted (Toly, 2003).

Operational definition: parent’s perception and level of satisfaction with

relationships between the family and individuals, the family and subsystems and between

the family and the community as measured by the Feetham Family Functioning Survey

(Roberts & Feetham, 1982).

Assumptions

An assumption of this proposed study is that the child who is technology

dependent will stimulate the normalization process to begin and that the mother will find

the long term care of her child to be a chronic strain.

35

Relationship of this Problem to the Nursing MetaParadigm

The nursing metaparadigm is concerned with person, health, nursing and

environment. In this study, the concept of person is represented by the family. The role of

families in a child’s adjustment to a chronic illness is of paramount importance. The

entire family, not just individual family members are impacted by the presence of a child

who is technology dependent (Knafl, Breitmayer, Gallo, & Zoeller, 1996). Children who

are chronically ill as well as technology dependent create unique, special challenges for

families because they require considerable amounts of time and care. Technology assists

patients to resume a more “normal” life, yet, paradoxically constrains them in their daily

lives (Sandelowski, 1993) and results in increased physical, social-emotional, economic

and care burdens for the child and their families (Kuster et al., 2004; Wang & Barnard,

2004).

Bringing the child who is technology dependent home changes family functioning

due to the increased care demands placed on families. The discharge of a child who is

technology dependent may result in individual and family growth or to system

dysfunction, lack of homeostasis and possible physical and mental demise of the

members (Clawson, 1996; Miles et al., 1999).

Closely associated with family functioning is the concept of normalization.

Normalization is defined as how the family as a unit consistently responds to a child’s

chronic condition, taking into account the configuration of responses of individual

members. Normalization is comprised of definition of the situation, management

behaviors, and perceived consequences (Knafl & Deatrick, 2003). It differs conceptually

from family functioning in that it does not include the interrelationships of the family

with the other layers of the environment such as the school, community, society and

36

culture. Additionally, normalization differs from family functioning because it

presupposes that there is an alteration in health.

Research indicates that the home environment is better than an institutional

environment because of the positive effects on a child’s emotional, developmental and

social growth as well as improved health (Kuster, 2002; Murphy, 1994; O’ Brien, 2001;

Wang & Barnard, 2004). Benefits of bringing the child home for the family include less

disruption and expense related to travel to the hospital, missed work, absences from the

typically developing siblings and costs incurred with hospitalization (Miller et al., 1998).

Families however, who bring the child who is technology dependent home must

often modify the family home in order to accommodate the extra equipment and supplies

and to allow home health care providers a more convenient access to the child who is

technology dependent as well as to allow the family to monitor the child’s condition

throughout the day. Modifications may be as simple as turning a first floor room into the

child’s room—a mini ICU or may require more extensive modifications in the form of

home additions, wheel chair ramps and more. Bringing the child who is technology

dependent home also means changes for the family’s home environment, most notably

the loss of privacy that typically comes with visits by home health care providers. Some

children who are technology dependent may require up to 24 hours of skilled nursing care

in the home which changes family dynamics. In addition to home health care providers,

extended family members and friends may be more frequent visitors to the family home

to assist with instrumental tasks thereby changing the typical dynamics of the home.

Although this is frequently seen as welcomed help, it comes at a cost of family privacy

and a disruption to inter-personal relationships among family members.

37

Therefore, the identified research problem directly relates to the nursing

metaparadigm. Children who are chronically ill as well as technology dependent create

unique, special challenges for families because they require considerable amounts of time

and care. While the home environment is optimal in terms of a child’s emotional,

developmental and social growth as well as improved health, families frequently incur

high cost burdens. Nurses are best suited to assist these families when they are discharged

home by enabling and empowering families to function optimally in the management of

the child who is technology dependent by identifying and augmenting family strengths.

Summary

In summary, the purpose of this study was to explore how parents respond to and

manage the special challenges of a child who is technology dependent following

discharge from the hospital to home. This descriptive, correlational study explored the

relationship between child/maternal factors (child’s functional status, level of technology

dependence, mother’s depressive symptoms, length of caregiving duration, amount of

home health care nursing hours, race, family income and age of the child) and (a) family

functioning as well as (b) normalization in families with a child who is technology

dependent. The following study questions were addressed:

1a. What are the relationships of mother’s depressive symptoms, child’s

severity of illness (functional severity and level of technology

dependence) and normalization efforts, with family functioning in families

with a child who is technology dependent? 1b. Do these relationships hold

after adjusting for length of caregiving duration, amount of home health

care nursing hours, race, family income and age of the child who is

38

dependent on technology? (Correlation Matrix, Multiple Regression)-

F statistic

2. Are there differences in a) family functioning and b) normalization efforts

and c) mother’s level of depressive symptoms based on the child’s level of

technology dependence (3 levels)? (MANOVA)-F statistic

3. Do a mother’s depressive symptoms have a mediating effect on the

relationship between the child’s severity of illness (functional status) and

(a) normalization and (b) family functioning in mothers with a child who

is technology dependent? (Correlation Matrix, Mediation using

Hierarchical Multiple Regression)-F statistic

4. Does normalization have a mediating effect on the relationship between

(a) child’s severity of illness (functional status) and family functioning, (b)

depressive symptoms and family functioning in mothers with a child who

is technology dependent? (Correlation Matrix, Mediation using

Hierarchical Multiple Regression)-F statistic

5 a. What are the relationships among mother’s depressive symptoms, child’s

severity of illness (functional status, level of technology dependence),

family functioning on normalization efforts in families with a child who is

technology dependent? 5b. Do these relationships hold after adjusting for

length of caregiving duration, amount of home health care nursing hours,

race, family income and age of the child who is dependent on technology?

(Correlation Matrix, Multiple Regression)- F statistic

39

CHAPTER TWO – REVIEW OF LITERATURE

Introduction

Children with a chronic illness and their parents, particularly mothers, have been

examined extensively in the literature. Within the last 10 years, more focus has been

devoted in the literature to a segment of this population known as children with special

health care needs. Although children with special health needs comprise only 15.6% of

children under the age of 18 years, they accounted for 42.1% of the medical expenditures

(excluding dental) in 2000 (Newacheck & Kim, 2005). A large portion of these children

with special health care needs are technology dependent due to the technologic and

scientific advances over the past 20 years (Guyer et al., 1998; Jackson Allen, 2004;

Newacheck & Kim, 2005). Although technology assists patients to lead a more “normal”

life it also paradoxically constrains them in their daily lives (Sandelowski, 1993) and

results in adverse consequences for the child and family related to physical, social-

emotional, economic and care burdens (Kuster et al., 2004; Wang & Barnard, 2004).

As the trend toward the use of pediatric home care is increasing (Madigan et al.,

1999) there has been a corresponding increase in the number of studies regarding the

family impact of caring for a chronically ill child at home. Concomitantly, there has been

an increase in literature written over the last 10 years regarding children who are

technology dependent and their families. The following review includes studies of

parents of children with chronic conditions, particularly children who are technology

dependent. Studies in this review of the literature report the psychosocial effects on

parents as well as their needs and concerns. The review of literature is presented in four

40

sections: (a) chronic illness within a family context; (b) psychological effects of caring

for a child who is technology dependent on parents; (c) normalization as a management

strategy for families; and (d) family functioning.

Chronic Illness Within A Family Context

Stein (1992) defined chronic conditions in children as conditions that will produce

long-term sequelae such as dependency on medications or a specialized diet for normal

functioning or control of a chronic condition, disfigurement, limitation of functions more

than those expected for the child’s age and development, dependence on medical

technology for body functions, need for medical care/services more than usual for the

child’s age or special ongoing treatment at home or school. Much research has been

conducted regarding children with chronic illness. Some studies examined the effects of

chronic illness on parents, children or both. Researchers studied children with specific

chronic illnesses while others used a non-categorical approach and included

children/parents with various chronic illnesses. Studies regarding chronic illness in

children have been influenced by research on chronic illness in adults.

Necessary Adjustments

Changes and adjustments that take place in the lives of families when a child is

diagnosed with a chronic illness are numerous and have been studied extensively in the

literature. These numerous changes may include a change in career to allow more time

for caregiving (Case-Smith, 2004) or in some cases, the need to quit work altogether

(Case-Smith, 2004; Thyen, Terres, Yazdgerdi, & Perrrin, 1998), the need to take on

second or even third jobs by the working parent to help make ends meet (Case-Smith,

2004), an increase in the time needed for planning activities outside the home (Case-

41

Smith, 2004), cancellation of activities due to unpredictable symptoms (Dodgson et al,

2000) or feeling a need to always be there with the child (Case-Smith, 2004). Parents also

experience a change in their social lives as they frequently find maintaining a social life a

challenge due to the fact that friends and relatives don’t understand limitations placed on

them related to the child’s illness care needs (Case-Smith, 2004). Additionally, parents

may experience trouble building friendships because of a loss of experience with

socialization (Case-Smith, 2004). Adaptation responses range across a continuum from

optimal to ineffective (Clawson, 1996). Adaptive tasks including instrumental, emotional

components and growth responses are used to manage chronic illness with the goal of

satisfaction, approval and success with later tasks (Clawson, 1996). Therefore, many

parents of children with chronic illness experience a multitude of changes and

adjustments in their work and social life in order to care for their child.

Positive Gains

Positive gains experienced by parents are also noted in the literature. Due to their

experience with chronic illness in their child, parents frequently become an advocate for

the child/others (Case-Smith, 2004; Paterson, 2001), and experience personal growth

(Clawson, 1996; Chernoff, List, DeVet, & Ireys, 2001), increased family cohesion (Case-

Smith, 2004), decreased family conflict (Knafl & Gillis, 2002), greater appreciation of

life (Paterson, 2001; Case-Smith, 2004), and acquire a sensitivity and tolerance of

individual differences (Paterson, 2001; Case-Smith, 2004).

One study exemplifies the positive gains that parents of children with chronic

illness experience. In a study of 190 mothers of children 7-11 years with a chronic illness,

Chernoff and colleagues (2001), found that 88% felt better about themselves because

42

they learned to manage their child’s chronic illness, 70% thought their family was

stronger and 80% felt the family benefited in some way from having a child with a

chronic illness. Those mothers with an educational level of High School graduate or less

were more likely to report feeling better about the management of a child with a chronic

illness than those who finished college (p=<.05). Therefore, research has shown that for

some parents, particularly those with less education, effectively managing the care of a

chronically ill child has led to positive gains such as a sense of personal pride and

growth. Additionally, parents of children with chronic illness have reported that there

family is stronger and have benefited in a positive way from having a child with a chronic

illness.

Critical Times

Critical times in the chronic illness trajectory were also noted in the literature.

Times of particular disequilibrium include the time of diagnosis, or when the child

experiences increased physical symptoms, has an increase in physical care needs,

ominous physical health changes or rehospitalization (Clements, Copeland, & Loftus,

1990). Furthermore, new developmental needs in the child create a trigger point for

increased uncertainty for parents (Dodgson et al., 2000).

Father’s Perspective

While most studies of children with chronic illness have included mothers, a few

have included fathers as the major focus of study. In one such study conducted in Israel,

Katz and Krulik (1999) compared 80 fathers of children with chronic illness age 6

months-7 years who were receiving some type of daily treatment to 80 fathers of age

matched healthy children. Findings include that fathers of chronically ill children

43

experience a greater number of stressful life events (personal events p=<.05; stressful

family/social events p=<.001) and were more likely to have feelings of lower self-esteem

(p=<.005) than fathers of healthy children. The researchers found a positive correlation

between self-esteem and social support in fathers of chronically ill children. Both groups

however, had decreased marital satisfaction with increased life events (p=<.001). No

differences were found between fathers of chronically ill children and those with healthy

children on marital satisfaction and involvement in the care of the child. Important

findings in this study are that fathers with a greater number of stressful life events or less

satisfaction with social support were identified as a high risk group.

Family Response to Chronic Illness

Other literature focused on the family’s response to a member’s chronic illness. In

a meta-analysis and synthesis of current research regarding families and chronic illness,

Knafl and Gillis (2002), focused on the family’s response to a member’s chronic illness

as well as the contribution of the family to a member’s response to chronic illness. A total

of 66% of the articles were descriptive studies (48 articles reporting on 38 studies) of

family life in the context of a chronic illness. Findings from this synthesis of descriptive

studies include that the process of family adaptation to illness includes rising to the

challenge and gaining positive meaning and incorporating illness management into every

day routine so that every day life assumes a taken-for-granted quality. Furthermore,

families construct their own subjective illness meanings and develop strategies for

managing the chronic illness, as well as learn to master treatment and incorporate illness

management into everyday family life. Mastery and routine were found to be important

aspects of family life (Knafl & Gillis, 2002). The remaining 34% of studies were

44

explanatory (25 articles; 24 studies). Stress was most frequently studied in these

explanatory articles. Major findings from these articles include that few stressors are

consistently associated with better functioning. Gaps include the need to look at the

family’s response related to chronic illness, the role of health care professionals in

shaping the family illness response, and the development of nursing interventions to

address family strengths and weaknesses. Another crucial research gap according to

Knafl and Gillis (2002) was the identification of variables that are linked with levels of

individual/family functioning. Finally, another research gap was identification of

different patterns of response to illness so that interventions can be developed and tested

(Knafl & Gillis, 2002).

Needs of Parents

Literature related to chronic illness in children also identified needs of parents in

caring for these children. Specific needs identified by parents of children with chronic

illness include the need for professional support. Pelletier, Godin, Lepage, and Dussault

(1994) found that parents have a need to share personal feelings and worries about their

child as well a need to be encouraged and praised for their ability to care for their child.

Other needs of parents of chronically ill children addressed in the literature include the

combination of personal guidance and good quality information in the form of booklets

or concise directories using plain and simple terms that are accurate, up to date, and easy

to read (Mitchell & Sloper, 2002). Parents reported that information would be most

helpful at key times such as diagnosis, the start of school and at puberty.

Literature regarding families of children with chronic illness also examined unmet

needs. One study examined the unmet needs identified by 83 caregivers of chronically

45

children ages .2-17.4 years many of whom were preterm and had more than one diagnosis

(Farmer, Marien, Clark, Sherman, & Selva 2004). The researchers found that the

caregivers wanted more information about services and the best way to promote their

child’s health and development, as well as caregiver supports, community services and

help with family relationships and financial concerns. A total of 87% were happy with

the health care services their child received but were concerned with the lack of mental

health services for themselves. Another, 52% wanted help with care coordination.

Interestingly, there was a significant difference (p=<.03) in the number of unmet needs

related to ethnicity, with Caucasians reporting fewer unmet needs when compared to

families of other races/ethnicity. Social support and appraisal of family burden were

significant predictors of unmet needs. Additionally, the number of unmet needs also

varied somewhat by complexity of the child’s illness. The researchers concluded that

with adequate professional and informal community support, families are able to access

more resources, and as a consequence functioned more effectively, thereby promoting

health in the child while at the same time decreasing overall family needs (Farmer et al.,

2004). Additional findings were that a mother’s increased personal strain and burden

interferes with a mother’s ability to access necessary services and supports that

subsequently has a negative effect on child and family outcomes. Appraisals of the

impact of the child on the family were thought to be associated with the mother’s

depression. In conclusion, the researchers proposed that caregivers may benefit from

psychosocial interventions in primary care to increase social support and positive

appraisals of the impact of the child on the family. Additionally, an intervention that

would include dissemination of information regarding how the condition affects growth

46

and development, how to support learning and social integration, self care for caregivers

and how to support siblings would be greatly beneficial (Farmer et al., 2004).

Unpredictability of Symptoms

Parents of children with chronic illness not only identified unmet needs as

troublesome but often find the unpredictability of the child’s symptoms very challenging

as well. In a study of 173 mothers and 150 fathers of young children 12-30 months

diagnosed with a chronic physical health impairment within the past year, Dodgson et al.,

(2000) examined the effect of predictability of symptoms and the degree of certainty in

life expectancy on the level of family distress. A majority of those studied lived in two

parent homes, had an average family size of four and were Caucasian. Instruments used

in the study were the Impact on Family Scale, Characteristics of Chronic Condition

Questionnaire, and the Function Status II-Revised (FS II-R). Dodgson et al. (2000) found

that parents of children who had intermittently unpredictable symptoms experienced

significantly more family distress than those with more predictable symptoms. On the

other hand, there was no difference between mothers and fathers as to uncertain life

expectancy and family distress. For mothers and fathers, unpredictable symptoms was

significantly associated with increased family and social disruption, (p=.001) and

(p=.002) respectively. Unpredictable symptoms were also associated with emotional

strain (p=.001) and financial burden (p=>.008) for mothers (Dodgson et al., 2000). The

researchers concluded that parents of children with unpredictable symptoms are at

increased risk for negative outcomes.

47

Theoretical Foundations for Chronic Illness Research

Research regarding chronic illness in children has also drawn from foundations of

research regarding chronic illness in adults. Twenty years of research regarding chronic

illness in adults was examined by Thorne and Paterson (2000). The researchers found

commonalities and variations in chronic illness experiences but found little research that

included culturally/ethnically diverse groups. Thorne et al. (2002) found that most studies

used white, educated, middle class females. Gaps in the research include lack of diversity

in age, gender, socio-economic status, and ethnicity, outlook on life or geographical

location. In conclusion, gaps and therefore the limitations of prior research are that it has

mostly been studied in isolation from sociocultural and psychological factors that may

impact the chronic illness experience (Thorne et al., 2002).

Thorne and Paterson (2000) examined the sociocultural and psychological factors

associated with the experience of chronic illness in adults. They proposed that chronic

illness is often experienced in an uneven trajectory. At times, the disease is put in the

background of consciousness and other times it is overwhelmingly significant and

dominates daily life. Shifts in perspective are due therefore to personal and sociocultural

factors (Thorne & Paterson, 2000). Paterson's (2001) model, “Shifting Perspectives

Model of Chronic Illness” proposes that living life with chronic illness is an ongoing,

continually shifting process whereby illness or wellness-in-the-foreground perspectives

have specific functions for the chronically ill child/family. This model helps explain

variations in attention to symptoms over time and was derived from a metastudy of 292

qualitative research articles related to chronic physical illness published from 1980-1996.

Previously, researchers proposed that individuals with chronic illness follow a predictable

trajectory and implied that an end goal exists. The Shifting Perspectives Model posits that

48

living with a chronic illness is an ongoing, continually shifting process that includes the

individual’s ever-changing perspectives about their disease including elements of both

illness and wellness. As the illness experience, as well as personal and social context

changes, a corresponding shift occurs in the degree that illness shifts to the foreground or

background of their “world”. Perception of reality is how individuals with chronic illness

and family members interpret and respond to their illness (Paterson, 2001).

In an illness-in-the-foreground-perspective of chronic illness, the individual

focuses on sickness, loss, burden and suffering (Paterson, 2001). The chronic illness is

viewed as destructive to self/others and individuals are absorbed by the illness

experience. In a wellness-in-the-foreground perspective, chronic illness is appraised as an

opportunity for a change in their relationship with the environment and others. There is

congruence between self-identity, identity created by the disease, construction of illness

by other individuals/family members and life events. The fact that some individuals

describe their health as good or excellent despite impaired physical function is not a

distortion of reality, but rather, re-visioning of what is possible and normal (Paterson,

2001).

Shifts in perspective related to health for individuals with chronic illness may

occur for various reasons. A major factor in shifts from wellness to illness-in-the-

foreground is threats to the individual’s control. Threats may include signs of disease

progression, lack of skill in disease management, and interactions with health care

providers that emphasize hopelessness and dependence. On the other hand, a shift from

illness to wellness-in-the-foreground includes the ability of the individual to “bounce

back” with renewed hope and optimism when the individual identifies the need to return

to wellness or chooses to focus attention away from illness (Paterson, 2001).

49

Summary of Chronic Illness Within a Family Context

In summary, literature related to chronic illness within a family context has

addressed changes and adjustments in the lives of caregiver and families, positive gains

experienced by parents of these children, critical time periods in the illness trajectory,

needs/unmet needs of parents who are caring for chronically ill children and

predictability of the child’s symptoms and its relationship with family distress. The

Shifting Perspectives Model by Paterson (2001) was originally developed to help

elucidate the life of an adult with chronic illness but is apropos for children with chronic

illness and their families. This model proposes that chronic illness is experienced as an

uneven trajectory regarding the emphasis placed of the chronic illness in daily life. At

times the disease shifts from the background to the foreground of consciousness and vice

versa depending on a variety of factors such as disease progression or exacerbation and

disease management. Gaps related to research of chronic illness in children within a

family context include a lack of culturally/ethnically diverse participants in research

studies as well as a dearth of studies regarding the sociocultural/psychological factors

related to chronic illness, family response to chronic illness, the role of health care

professionals in shaping family illness response, variables linked with individual and

family functioning and development of nursing interventions to address family strengths

and weaknesses.

Psychological Effects of Caring for a Child with Chronic Illness

Depressive Symptoms

The literature regarding chronic illness in children has described the existence of

depressive symptoms in mothers of children with chronic illness and its relationship with

50

other variables. Many researchers studied the concept of depressive symptoms in parents

of preterm infants. Thompson, Oehler, Catlett, and Johndrow (1993) studied 90 mothers

of very low birthweight infants and found that rates of significant maternal psychological

distress varied over time from 48% at birth, 33% at 3 to 6 weeks postpartum, and up to

41% at 6 months corrected age as measured by the Symptom Checklist-90-Revised. This

instrument has excellent reliability and includes symptoms related to anxiety and

depression. Over time, mothers perceived less stress related to the infant but more stress

related to daily hassles, less family support and more conflict. Miles et al. (1999) in a

longitudinal study of 67 mothers of medically fragile, technology dependent, preterm

infants found that 45% at discharge and 36% at 12 months had Center for

Epidemiological Studies- Depression Scale (CES-D) scores above 16, indicating risk for

depression. Variables significantly related to an increase in depressive symptoms were

lower educational level, lower mastery, higher satisfaction with family, and more worry

about the child’s health (Miles et al., 1999). Therefore, studies of mothers of preterm,

medically fragile infants have found significant levels of psychological distress indicating

a risk for depression.

Depressive Symptoms in Parents of Children who are Technology Dependent

Research has also been conducted regarding depressive symptoms in parents of

children who are technology dependent. Significant findings include that in 75% of

families, one or both parents experienced distress symptoms indicating need for

psychological intervention (Leonard et al., 1993). Cavanagh (1999) found that six of 13

primary caregivers of children who are technology dependent had experienced depressive

symptoms that required either counseling or antidepressant medication. Fleming et al.

51

(1994) found that depression did not vary significantly among the four groups of

technology dependence established by the Office of Technology Assessment (OTA,

1987). Teague et al (1993) found that higher CES-D scores correlated with higher means

of the Family Inventory of Life Events and Changes (FILE) family stress subscales of

intra-family strains and marital stress. Therefore, studies of parents with children who are

technology dependent found significant levels of depressive symptoms in a large

percentage of participants that indicated a need for psychological intervention regardless

of the type of technology that the children were dependent on.

Correlates of Depression

Many studies have been conducted to investigate the correlates of depression in

the parents of children with chronic illnesses. Correlates of depression include functional

limitations in the chronically ill child (Silver et al., 1995; Silver et al., 1998; Frankel &

Wamboldt, 1998; Lustig et al., 1996; Weiss & Chen, 2002), decreased resources (Silver,

et al., 1995), decreased self esteem (Silver et al., 1995), low maternal self-efficacy (Silver

et al., 1995), decreased family income (Drotar et al., 1997; Canning et al., 1996; Shore et

al., 2002), decreased satisfaction with family relationships (Shore et al., 2002; Weiss &

Chen, 2002), increased child behavior problems (Shore et al., 2002), mother’s negative

perception of chronically ill child’s impact on the family(Ireys & Silver, 1996), increased

number of health care visits (Ireys & Silver, 1996), unpredictability of symptoms and

need to watch for sudden changes in condition (Ireys & Silver, 1996), increased anxiety

in the chronically ill child (Frankel & Wamboldt, 1998), maternal role restriction (Silver,

Bauman, & Weiss, 1999), lack of family support (Thyen et al., 1998), low social support

appraisal (Thyen et al., 1998), mother unemployed outside the home (Thyen et al., 1999),

52

young age of caregiver (Canning et al., 1996), perception of increased burden by

caregiver (Canning et al., 1996), unresponsiveness of the chronically ill child (Weiss &

Chen, 2002), increased life stress (Weiss & Chen, 2002), decreased perceived emotional

support (Weiss & Chen, 2002), decreased family cohesion (Weiss & Chen, 2002), and

the lack of a partner in the home (Weiss & Chen, 2002).

Many of the studies related to psychological distress included large sample sizes

with a variety of chronic illnesses. Although many of the studies used the term mental

distress, most were specific at some point in the article to mention depression. A majority

of the studies used mostly Caucasian subjects with the exception of Silver et al. (1995),

Ireys and Silver (1996), and Weiss and Chen (2002). Therefore, a major gap in research

regarding the correlates of depression in parents of children with chronic illness is that

the samples lacked racial diversity thus affecting generalizability of the studies.

Depressive Symptoms and Severity of Illness

Some studies examined the relationship between maternal depression and the

child’s severity of illness (Affleck, 1987; Affleck, Tennen, Rowe, & Higgins, 1990;

Miles, et al., 1999; Thompson, et al., 1993). Miles et al. (1999) found no significant

relationship between CES-D scores and severity of illness as measured by level of

technology dependence and multisystem diagnosis at discharge and 12 months after birth.

However, no reliability and validity data were reported for the tool that measured the

child’s disease acuity. Thompson et al. (1993) had similar findings but used birth weight,

gestational age and the Neurobiologic Risk Score to measure severity of illness. The

instrument was reported to be strongly predictive of developmental outcome however no

reliability was reported. Affleck et al. (1987) also found no significant relationship with

53

depression however rough estimates of severity of illness were used such as length of

hospital stay, gestational age at birth and perinatal complications. Affleck and colleagues

(1990), in a study of 94 mothers of preterm infants, also found no relationship between

severity of illness and depression as measured by the Profile of Mood States at discharge

and 6 months later. The literature therefore reports no significant relationship between the

mother’s level of depression and severity of illness in the chronically ill child. However,

a variety of instruments were used that were either un-standardized or had poor reliability

ratings.

Functional limitations were implicated as the major contributor to psychological

distress in parents and were for the most part measured via the Function Status II-Revised

(FS II-R) developed by Stein and Jessop (1990). Since the FS II-R is an instrument that

requires the parent to answer questions regarding the chronically ill child’s health, Dadds,

Stein and Silver (1995) conducted a study of 365 mothers of children 5-9 years old with a

chronic condition to examine whether a mother’s own mental health effected her

perceptions of the child’s health and general behavioral adjustment. The researchers

concluded that the FS II-R is not influenced by the mother’s psychological adjustment.

Researchers explored the correlates of psychological distress to ascertain if the

strongest relationship was severity of illness or, more specifically, functional status

limitations. Canning et al. (1996) in a study of 116 children age 9-18 years found that

when family income, gender, caregiver age were controlled, the child’s functional

limitation predicted increased caregiver distress while reported severity of illness did not.

Additionally, Canning et al. (1996) found that caregiver burden was a significant

predictor of a caregiver’s mental distress (p=<.01) using the Brief Symptom Inventory

For Caregivers. Theyen et al. (1998) proposed that functional disability is a greater factor

54

in family management of the chronic condition more than disease severity because it

describes the child’s ability to perform activities of daily living and participate in family

life, play, and school.

Lustig and colleagues (1996) conversely, found that both biological and

functional limitations accounted for nearly half of the variance of maternal mental health.

In this study, a total of 53% of mothers scored in the high range of depressive symptoms

using the Psychiatric Symptom Index (PSI). The child’s functional status was found to

have both direct and mediated associations with maternal mental health. It is thought the

mothers have increased responsibility for the ill child and may consequently experience

increased distress when the child’s functional status declines. Furthermore, it is proposed

that greater maternal demands increases the burden on emotional and financial resources

needed to meet other family task and may lead to mental health problems (Lustig et al.,

1996). The researchers also found that maternal appraisal of impact of child’s illness on

the family partially mediated effects of the child’s functional status on maternal mental

health (Lustig et al., 1996). It is important to note however that although the findings are

different than that of all other research, it was conducted on only 53 mothers from a

pediatric rheumatology clinic.

Further studies regarding the correlates of psychological distress in parents of

children with chronic illness also examined the amount of family resources and the

presence of role restriction. Silver et al. (1995) studied 365 low income, urban dwelling

mothers of children 5-9 years of age with diverse chronic illnesses to examine the extent

of maternal mental distress using the Psychiatric Symptom Index (PSI). Using multiple

regression to control for sociodemographics, the researchers found that functional

limitations in the chronically ill child as measured by the FS II-R and decreased family

55

resources were associated with increased psychiatric symptoms. In a later article

regarding the same study, Silver et al. (1999) examined role restriction (role strain such

as inability to pursue other interests or social activity due to parental demands) using the

Parenting Stress Index Form. Findings include that in a multiple regression model,

functional limitations predicted maternal depression. Furthermore, adding role restriction

to this model significantly increased explained variance in maternal depression scores

indicating that role restriction is directly related to a mother’s distress symptoms even

when other sociodemographic variables were controlled. High role restriction was related

to lower level of education, no other adult in the home, decreased social support,

mother’s own health, work status, receipt of welfare, and number of children in the home.

The conclusion of this study was that functional limitations and increased role restriction

were independently related to maternal depression (Silver et al., 1999).

In a subsequent study to further investigate parental mental health issues, Silver et

al. (1998) used random digit dialing to conduct a telephone survey of parent’s self-

reported distress related to their child’s chronic illness in 380 inner city parents and 398

parents from a population based national sample. The researchers administered the PSI

instrument to parents whose child had a chronic illness longer than one year to examine

the intensity of 29 common psychiatric symptoms in the parents and frequency over the

previous two weeks. The highest mean PSI scores were found in a sub-group of parents

whose child had functional limitations particularly for the national sample. A post-hoc

analysis of the data showed that the pattern continued even when parent gender, child age

and family income were controlled using analysis of covariance (ANCOVA). Increased

parental distress was equal in both mothers and fathers of children with functional

limitations. Interestingly, post-hoc analysis of the inner-city sample showed that having

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any chronic health condition not just functional limitation was related to parental distress

(Silver et al., 1998). Therefore, functional limitations particularly for those living in inner

city areas were significantly related to increased parental psychiatric symptoms.

Ireys and Silver (1996) conducted a longitudinal study at 3 data points over 18

months to examine correlates of mental health in 169 mothers of children 5-8 years of

age with a chronic illness. The sample was demographically quite diverse with 46%

Hispanic, 32% Caucasian, 49% on welfare and 50% in two parent households. Findings

include that there is a mediating role of perception processes on mental health. A

mother’s perception of impact of the illness attenuated the relationship between the

number of hospitalizations and the mother’s mental health. Demographics, type of illness

and service use were found to be unrelated to a mother’s mental health and modestly

related to mother’s perception of illness impact on the family at one year. The researchers

concluded that how a mother perceives events such as hospitalization plays a role in how

she functions psychologically. Additionally, the authors proposed that uncertainty or

unpredictability of symptoms effects appraisal by reducing perceived control over the

condition course and outcome. Furthermore, they proposed that the reason increased

service use was linked to increased mental health symptoms was that visits can be

disruptive and may be a marker for mothers who have more difficulty responding to

challenges inherent with caring for a chronically ill child (Ireys & Silver, 1996).

In another study examining the correlates of depression, Thyen et al. (1998)

studied 65 mothers of children who were technology dependent and a control group of 54

mothers of children who were hospitalized for acute illness 6 months following

discharge. Instruments used in this study included the SF-36 to assess physical and

mental health concepts, the CES-D for depression, the FS II-R to examine the functional

57

status of the children, Family Environment Scale (FES) and a social support scale. The

purpose of the study was to examine the health outcomes of mothers of children

dependent on technology and associations with severity of illness and family/social

support. Mothers of children who were technology dependent had impaired health related

to pain, vitality, social function, mental health and greater depressive symptoms than

controls (p=<.001). Almost half the mothers of children who were technology dependent

had scores suggestive of clinical depression. Also, these mothers had lower family

income, were less likely to be employed and had higher out of pocket medical expenses

than controls. Additionally, increased scores on the CES-D, indicative of risk for

depression were significantly related to increased functional limitations in the chronically

ill child and increased out-of-pocket medical care expenses. Noteworthy is the fact that

the FS II-R, a measure of functional status, explained 34% of the variance in maternal

mental health status. The researchers found that mothers of children who were

technology dependent reported higher levels of depressive symptoms, poorer physical

health, less family support and fewer recreational and cultural activities. Other important

findings were that even 6 months after diagnosis or major hospitalization, functional

status was highly associated with mother’s emotional well being. Family support and

social support appraisal had moderate independent positive effects on mother’s emotional

well-being. In conclusion, a greater number of mothers of children who were technology

dependent reported significantly higher levels of depressive symptoms than controls

particularly those whose children had a lower functional status.

In a subsequent article published regarding the same study, Thyen et al. (1999)

examined how children who are technology dependent are cared for at home and how

having a child who is technology dependent affects maternal employment and child care.

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Additionally, the study examined the way care at home and how the decision to leave

employment affects maternal mental health. Important findings include that the primary

effect of caring for a child assisted by technology on mental health was significant

(p<.001) and employment was a protective factor for mental health in the study group.

Moreover, there was significant interaction with maternal mental health, employment and

group membership (p=<.01) (Thyen et al. 1999).

Frankel and Wamboldt (1998) conducted an ambitious study to examine how

family functioning, coping, and child anxiety are related to parental psychological

distress in 70 chronically ill children and their parents using the Impact on Family Scale

(IOF) Scale, Psychiatric Symptom Index, Coping Health Inventory for Parents (CHIP),

Family Assessment Device (FAD), and Functional Disability Inventory. Findings include

that those parents with increased psychological distress and low perceived support report

that the child’s illness is disruptive and has a major impact on the family (Frankel &

Wamboldt, 1998). The researchers proposed that how a child’s illness interferes with and

impacts a family is determined by the psychological health of the main caretaking parent.

The psychological health of the parent is in turn affected by the age and gender of the

child, number of adults in the household, family functioning, and the functional

limitations, and level of anxiety of the child. The Psychiatric Symptom Index (PSI)

predicted the Impact on Family Scale, a measure of family functioning, accounting for

39% of the variance in this variable. Additionally, having a child who has functional

limitations as well as high anxiety was associated with higher PSI scores. Based on these

findings, the researchers propose that the major mediating factor between aspects of the

child/family and family functioning and the management of the chronic illness is the

caregiving parent’s psychological adaptation (Frankel & Wamboldt, 1998). Kuster et al.

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(2002) concur with these findings and state that the Impact on Family Scale was

positively related to level of depression in mothers of ventilator dependent children.

Therefore, when the primary caretaker is psychologically distressed, the family is unable

to effectively manage the illness and the quality of life for the entire family is decreased.

In particular, mothers’ distress seems to impact the general function of all the other

family members. In conclusion, a mother’s mental distress (depressive symptoms)

impacts general family functioning and may contribute to how much the family is

impacted by the child’s chronic illness (Frankel & Wamboldt, 1998).

Another contribution to a parent’s mental health is the responsiveness of the child

to the caregiver. In a longitudinal study of 125 low birth weight infants and mothers

recruited within the first 2 weeks after birth, Weiss and Chen (2002) examined family

functioning during the first 3 months and collected data on maternal functioning and

infant health at 6, 9 and 12 months using Family Coping Strategies (F-COPES), DSM-IV,

Nursing Child Assessment Satellite Training (NCAST) feeding scale, Family

Adaptability and Cohesion Scales (FACES) to measure adaptation and cohesion of the

family and Emotional Support and Life Stress. The researchers found that infants who

were more responsive and interacted more with caregivers had mothers with better

mental health and families who were more cohesive and adaptable. It was proposed that

when the infant is less responsive there is less mutually satisfying interactions. Other

significant findings were that perceived emotional support was positively correlated with

degree of family cohesion (p=<.0001).

Another correlate of depression in parents of chronically ill children is the

presence of behavior problems. Shore et al. (2002) found that child behavior problems

were the strongest predictor of maternal depressive symptoms (p=<.0001) in a sample of

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115 mother-child dyads. Other factors that helped to explain 32% of variance in maternal

depressive symptoms were income (p=.004) and low satisfaction with family

relationships (p=<.008).

Other researchers examined the magnitude of depression and mental distress in

caregivers of children with chronic illness and more specifically children who are

dependent on technology. Miles et al. (1999) found that at discharge, 45% of mothers

with children who are technology dependent had CES-D scores above the cutoff of 16

indicating risk for depression and 36% had scores above the cutoff at 12 months after

discharge. Kuster (2002) found that 45% of female caregivers of ventilator dependent

children were experiencing depressive mood symptoms. Interestingly, social support

(emotional, informational, and instrumental) was the only significant predictor of

depression (Kuster,2002).

Summary of Psychological Effects on Parents of a Child with Chronic Illness

In summary, many factors have been found to be associated with depression in

parents of children with chronic illness. Most notably, impaired functional status,

unpredictability of the child’s symptoms, decreased family income, decreased satisfaction

with family relationships and decreased family cohesion, increased number of health care

visits, maternal role restriction, maternal unemployment outside of the home, perception

of increased burden by the caregiver, child’s lack of responsiveness, child’s behavior

problems, increased anxiety in the child, residence in an urban area and perceived lack of

support contribute to increased depressive symptoms in the primary caregiver,

particularly mothers. Functional limitations were implicated as the major contributor to

psychological distress in parents of chronically ill children. Increased psychological

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distress in caregivers, especially mothers, was also noted to have a negative effect on

family functioning as well. Noteworthy is the fact that large percentages of parents of

children who are dependent on technology, up to 45%, have depressive symptoms that

are indicative of the need for psychological intervention (Miles et al, 1999; Kuster, 2002).

The Child Who is Technology Dependent

Relatively few research studies have been conducted regarding the child who is

technology dependent or their families even though these children have been increasingly

cared for at home over the past 20 years. Some researchers have examined the level of

depression experienced by parents, impact on the family with regard to the four groups of

technology assistance (OTA, 1987), and severity of the child’s illness and more recently

the health promotion activities of mothers. Others have studied the issues and the cost in

terms of time, physical health of parents, careers, relationships, loss of sleep, loss of

privacy, recreational activities, as well as financial costs of providing home care. Still

others have researched the dual role of nurse and parent that is often assumed while

providing home care to the child who is technology dependent as well as the experience

of mothering these children. Another area of research regarding the child who is

technology dependent and their families is the process that parents journey through while

learning to manage the child at home. Last, researchers have examined the positive gains

experienced by parents managing the child who is technology dependent at home as well

as the expressed needs of parents. Parents reported that the following would be most

helpful to them: a true trusting partnership with health care professionals, social support,

and assistance with accessing resources and help. The majority of sample sizes for

quantitative studies were large with mean age of the child being 4-7 years. Use of

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functional status instruments for the child who is technology dependent that were not

psychometrically tested was problematic. A majority of the studies done with children

who are technology dependent and their families, particularly those done recently, have

been qualitative.

Long-Term Home Care

Family experiences of long-term home care of the child who is technology

dependent (age 3-12 years) was examined in a qualitative study of 15 families (O’Brien,

2001). Parents identified uncertainty and unpredictability in their lives, describing “living

in a house of cards”. Parents attempted to increase the stability in their lives by using

advocacy, reframing and vigilance. Balance was required to manage daily life with

technology, maintain a functioning family and make sense of life (O’Brien, 2001).

Coping

Coping was also explored with of families with children who are technology

dependent. Youngblut, Brennan, and Swegart (1994) found that mobilizing family and

acquiring social support were the coping strategies used most often in a triangulated

study of 10 families. Stephenson (1999) found that higher levels of coping were

associated with lower levels of distress within families.

Cost

Cost- Time

Many studies noted the costs associated with caring for a child who is technology

dependent at home. One such cost associated with the care of these children is time. The

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time required for the care and multitude of treatments necessary to sustain the child who

is technology dependent is daunting (Kirk & Glendinning, 2004; Wilson et al., 1998) and

is considerably more than the time required for children who are chronically ill. Heyman

et al. (2004) interviewed a group of maternal caregivers of 101 children who had a

chronic illness with (n=50) and without (n=51) a gastrostomy tube (GT) three times; at

three month intervals. Findings include that children with gastrostomy tubes required two

times the total care time from the primary caregiver as children without a gastrostomy

tube (484.5 versus 197.8 minutes (p=<.0001) particularly for technical care.

Beyond the time required for technical treatment, much time is required to deal

with practical issues related to the child who is technology dependent such as dealing

with insurance companies, ordering supplies, and packing up the necessary equipment

and supplies for outings (Torok, 2001). In a qualitative study of 36 families of children

who are technology dependent, Heaton and colleagues (2005) examined the care routines

of children who were technology dependent to study what was involved with this care

and how it affects family members. The researchers discovered that there is an

incompatibility between technology time frames and other social and institutional time

frames that contributes to social exclusion of families with children who are technology

dependent from many aspects of social life. Time demands related to care routines and

lack of compatibility with other social and institutional time frames were found to limit

the family’s participation in school, employment and social activities. School days were

particularly difficult because there was less time to deal with medical devices and

administration of various treatments prior to school therefore the child had to get up

extraordinarily early (Heaton et al., 2005). Therefore, the time required to care for the

child who is technology dependent is considerably more than the typically developing

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child and the incorporation of the technology time frames can lead families to alienation

from participation in school, employment and social activities.

Cost- Physical Health

Another cost examined related to caregivers of the child who is technology

dependent is that of physical health. Ostwald et al. (1993) found that 19.2% of parents

caring for a child who is technology dependent reported that their physical health was fair

or poor. In a study of 89 infants who were technology dependent, Miller and colleagues

(1998) found that a total of 21.6% of their caregivers had physical problems that

interfered with daily activity that were directly related to caring for the child. Many

caregivers of children who are technology dependent experience physical exhaustion

(Kirk & Glendinning, 2004), fatigue (Neuss, 2004; Torok, 2001; Wilson et al., 1998) and

burnout (Wang & Barnard, 2004) mostly due to sleep disruption and deprivation because

of the constant vigilance necessary to monitor the alarms, perform treatments and oversee

the technology at night (Heaton et al, 2005; Kuster, et al., 2004; Neuss, 2004; Wang &

Barnard, 2004). Kuster et al. (2004) found that in 38 mothers with children who were

ventilator dependent, 60.5% never or only occasionally were able to get adequate sleep.

Along with sleep deprivation, mothers experience irritability, slowing of cognition and

altered memory (Neuss, 2004). Therefore, the costs associated with caring for a child

who is technology dependent on the caregiver’s physical health are high and sleep

deprivation, fatigue and burnout can consequently lead to difficulty with performing care

tasks.

Time constraints, due to care required by the child who is technology dependent,

can lead mothers to neglect health promotion activities such as recreation (Kuster et al.,

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2004; Neuss, 2004; Miller et al., 1998; Torok, 2001), adequate nutrition, exercise,

relaxation, and general health promotion (Kuster et al., 2004) that will ultimately affect

their physical and mental health. In a study of 89 mothers of infants who were technology

dependent, Miller et al. (1998) found that 75.7% decreased the amount of recreational

activity they participated in beyond those associated with caring for a typical infant.

Furthermore, in a study of 38 maternal caregivers of children who were ventilator

dependent, Kuster et al. (2004), found that the functional status of the child and maternal

coping were positively related to perceived general health and health promotion activities

of the mother but the impact of the child on the family (IOF) was inversely related to

health promotion activities. Interestingly, 82% of the mothers never or only occasionally

planned exercise and 73.7% never or only occasionally get together with friends. Mothers

scored low on nutrition, exercise, relaxation and general health promotion subscales of

the Personal Lifestyle Questionnaire (Kuster et al, 2004). More specifically, the two

largest contributors to the 25% of the variance in the mother’s participation in health

promotion activities were functional status and impact on family. Decreased functional

status, out of pocket money paid for the child’s medical expenses and number of home

health care nursing hours was related to increased physical and psychological morbidity

(depression) for these mothers (Kuster et al., 2004). Additionally, the researchers

proposed that if the mother perceives that she is unable to meet all the needs of her child

and family an alteration in sense of mastery, self esteem, and psychological dysfunction

(depression) may occur (Kuster, 2002).

Therefore, factors that are proposed to impact a mother’s physical and

psychological well being include the child’s severity of illness, frequency of

hospitalization or exacerbations, amount of time to provide treatment and care, functional

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and developmental status of the child, the level of care that the child requires and

available resources such as finances and number of home health care nursing hours as

these all directly effect the amount of time mothers must devote to caregiving (Kuster,

2002).

Cost- Alteration in Relationships

An alteration in relationships previously enjoyed was another cost associated with

caring for the child who is technology dependent. In a qualitative study of 12 mothers of

children who were technology dependent conducted by Neuss (2004), 10 said that their

life revolved around the child’s care at home and that they were no longer “dating” their

husbands. Some said this inability to spend time as a couple put a strain on the marital

relationship and some eventually divorced due to the stress of caring for a child who is

technology dependent (Kirk & Glendinning, 2004; Neuss, 2004; O’Brien, 2001). Others

parents reported marital discord due to the uneven work load that mothers perceived

(Wang & Barnard, 2004). Mothers also felt that friendships were now chosen by

convenience i.e. neighbors and home health care nurses and not necessarily by

compatibility. They felt a divide between themselves and others their own age because of

the difference in life views and the change in responsibilities. They could no longer relate

to others’ carefree attitude and mothers felt unable to leave for social events (Neuss,

2004). Furthermore, Allen and colleagues (1994), noted that according to the Impact on

Family (IOF) scale, illness of the child who is technology dependent had a negative effect

and was related to disruption of social relationships. In summary, alterations in

relationships with spouses, friends and other social contacts was a cost associated with

caring for the child who is technology dependent.

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Cost- Alteration in Family Dynamics

An additional cost of caring for the child who is technology dependent is a change

in family dynamics. Mothers noted changes in family dynamics and were concerned they

were not spending adequate time with other family members (Torok, 2001). Finding time

together as a family was difficult and frustrating (O’Brien, 2001). Having a child who is

technology dependent influenced some parent’s decision regarding having more children

(O’Brien, 2001). Some voiced concern regarding the other siblings in the family

receiving less attention due to the time spent delivering required care to the child who is

technology dependent (Kirk, 2004; Allen et al., 1994). All 12 mothers in a study by

Neuss (2004) said that the relationship with the healthy child suffered. Parents noted

behavior changes in siblings (Wang & Barnard, 2004) and were concerned about the

healthy sibling’s mental health due to the unpredictable life at home (Heaton et al., 2005).

Some of the siblings were given more domestic responsibilities and/or had to look after

other siblings. A particularly difficult time for siblings was when the child who is

technology dependent was ill especially when they had to try and study for exams

(Heaton et al., 2005). Therefore, parent’s time with healthy siblings is reduced when

caring for a child who is technology dependent and causes parental concern regarding not

only their relationship with the child but the healthy sibling’s mental health as well.

Cost- Social Isolation

Many parents expressed feelings of social isolation, a cost associated with caring

for the child who is technology dependent (Boland & Sims, 1996; Carnevale et al., 2006;

Kirk & Glendinning, 2004; Neuss, 2004; O’Brien, 2001; Wang & Barnard, 2004). Their

lives revolve around the technology and the necessary care routines. Friends are noted to

68

withdraw; there is less contact with work colleagues and extended family and less social

outings according to parents (Boland & Sims, 1996; Kirk, 2004; Tommet, 2003). Often

women experience not only social isolation but also loneliness and a sense that their lives

are on hold (Neuss, 2004). There is a lack of spontaneity due to the need to coordinate

outings with availability of a home health care nurse. Many reported the lack of available

respite care (Carnevale et al., 2006; Heaton et al., 2005; Kirk, 2004) or did not share the

care with others if they perceived they were not skilled enough to care for the child due to

worry that the care would suffer (Boland & Sims, 1996). In conclusion, parents,

particularly mothers, experience social isolation, and loneliness often due to the care

required by the child who is technology dependent. Furthermore, parents report lack of

respite and others whom they believe could adequately care for their child.

Cost- Alteration in Employment Opportunities

A change in career or leaving employment (quitting job) was frequently

mentioned by parents as a cost of caring for the child who is technology dependent

(Neuss, 2004; Torok, 2001; Miller et al. 1998; Kirk & Glendinning, 2004). Other parents

decreased their hours of employment to meet the care needs of their child (Cohen, 1999;

Glendinning et al., 2001). Miller et al. (1998) found that 73% of 89 primary caregivers

were employed before the child who is technology dependent was born and of these, 63%

quit due to illness and/or care required by the baby. Neuss (2004) found that 6 of 12

mothers in her qualitative study were employed prior to having the child who is

technology dependent however 5 of 12 gave up work not by choice but due to care needs

of the child. The one mother who still worked full time said there was no opportunity for

69

upward mobility due to the need for a flexible schedule. Cavanagh and colleagues (1999)

found that only 4 of 13 primary caregivers worked outside the home mostly due to

problems with flexibility of the job if the child got sick, a home care nurse called off or

frequent medical appointments. Others said they missed career opportunities (Cohen,

1999; Torok, 2001) or were unable to return to school and further their education (Lee,

1996; Miller et al., 1998) unless they had a large number of home health care nursing

hours (Lee, 1996). Therefore, a major cost for mothers related to caring for a child who is

technology dependent at home was changing careers or leaving gainful employment

outside the home due to the child’s frequent illnesses, lack of dependable home health

care nursing coverage and frequent medical appointments.

A mother’s change in employment status as a cost of caring for the child who is

technology dependent held true over and above those mothers who had healthy, typically

developing children. Thyen et al. (1999) examined how children who are technology

dependent and cared for at home affect maternal employment and child care compared to

a control group of mothers with children hospitalized for acute illness, matched for age

and gender, 6 months post discharge. Findings for the study group of mothers were that

46% worked fewer hours following discharge and 23% took a different job in order to be

able to care for the child, 46% earned less because they cared for the child and 42% were

unable to change jobs due to potential loss of insurance. A total of 92% took time off to

care for the child who was technology dependent compared to 35% of controls, 35.7% of

study families had family incomes <$30,000 compared to 19% of controls 6 months

following discharge (p=<.05) and study families had greater out of pocket medical

expenses than controls; $5062 versus $265 respectively. The researchers do not report if

family income at discharge is significantly different however, mothers who left

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employment had larger families, were single parents and had less child care assistance.

Using logistic regression, controls were three times more likely to work than the study

group. Mothers with college education were 2.5 times more likely to work. A total of

35.7% of the children who were technology dependent received home health care

nursing. Therefore, the cost of caring for child who is technology dependent in terms of

change in career and cessation of gainful employment held true even when these mothers

were compared to matched controls.

Cost- Alteration in Family Finances

Often, due to a mother’s cessation of employment outside the home in order to

care for the child who is technology dependent, total family income plummets. In a

qualitative study of children who are technology dependent, Cavanagh (1999) found the

continuous financial struggle that these families faced was a dominant theme. This

financial concern was present despite the money received from government programs and

family (Lee, 1996). Neuss (2004) found that 10 of 12 mothers reported a change in

financial status after the child who is technology dependent was discharged home. A

number of families were severely impacted financially from lost income when one of the

parents quit work in order to stay home and care for the child who is technology

dependent. Also, there were additional expenditures for the family due to increased use of

electricity, heat, phone, insurance, laundry as well as travel (Kirk & Glendinning, 2004;

Glendinning et al., 2001). In order to deal with this financial crunch, some parents used

savings to pay for the extra expenses (Miller et al., 1998) or even “spent down” all of

their savings so they would be eligible for state money (Cohen, 1999) while other parents

borrowed money (Miller et al., 1998). Miller et al. (1998) found that 40.5% of parents

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who were employed (typically the father) worked overtime while 16.2% took on a second

job to meet expenses. Cavanagh (1999) found that some secondary caregivers (fathers)

worked 50-100% overtime and depend on the grandparents for supplies or used their 401

K to make ends meet. Some parents reported getting behind on house payments,

experienced foreclosure and had to use a food bank to get by (Cavanagh, 1999).

Therefore, after a child who is technology dependent is discharged home, many families

experience decreased family income with a concomitant rise in expenses related to the

child’s care that necessitates accessing additional financial resources in the form of

overtime work for the secondary caregiver, tapping savings and retirement monies and

borrowing from relatives.

The financial pressures that parents of children who are technology dependent

spoke of in a number of qualitative studies are very real. In a study of data from the 2000

Medical Expenditure Panel Survey (MEPS) for children, Newacheck and Kim (2005)

found that 949 of the 6965 children (15.6%) in the sample were identified as children

with special health care needs (CSHCN) of whom children who are technology

dependent are a part. It was found that CSHCN had 3 times higher health care

expenditures than healthy children ($2099 vs. $628; p=<.01). Families of CSHCN had

the best coverage for in-patient services. Families of CSHCN with out-of-pocket

expenses over 5% of family income were about 11 times more likely to have incomes

below 200% of the federal poverty level than families at or above 400% of federal

poverty level. The total cost and out-of-pocket cost for CSHCN was noted to be skewed,

therefore, the median total expense for health care was $558 but the upper decile of

CSHCN was $4304. Home health care and hospital costs were major cost items for upper

decile kids. Prescription medications and home health care comprise 1/3 of health care

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expenses for CSHCN but only 1/20 for other children in the sample (Newacheck & Kim,

2005). Therefore, based on this survey of randomly selected children, those who are

technology dependent were more likely to come from families who were below 200% of

the federal poverty level and have a significantly greater amount of out of pocket medical

expenses that contribute to an already financially strained family.

Cost- Privacy

Parents of children who are technology dependent said that receiving home health

care nursing hours was extremely beneficial in many regards; however, many said that

this was at the cost of their privacy (Cohen, 1999; Lee, 1996; K. Murphy, 1997; O’Brien,

2002; Torok, 2001; Wang & Barnard, 2004). Cohen (1999) on the contrary, stated that

families who were socially marginalized (meaning that they were from low income,

minority families) did not experience this but, rather, enjoyed the extra company and

contact with someone from outside their social circle.

For some families, receiving home health care nursing changed the meaning of

home (Wang & Barnard, 2004). Kirk and colleagues (2005) found that the meaning of

home for many parents included thoughts of privacy, safety, intimacy, control and liberty.

The home was thought to embody the personal identity of the family and space and daily

routines have personal meaning. Technology transforms the home then so it is

consequently “medicalized” by equipment and is constantly being invaded by home

health care professionals. Additionally, the home is invaded and dominated by all the

equipment and supplies required to care for the child who is technology dependent (Kirk

et al., 2005). Often one of the rooms on the first floor is transformed into a mini-intensive

care unit (ICU). The home is now a public space and the family’s life is lived in front of

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strangers. All family interactions are subject to inspection and judgment therefore many

families expressed fear of showing affection or having arguments. Many felt awkward

entertaining because it had to be conducted in the presence of the home health care

professionals (Kirk et al., 2005). Parents had conflicts regarding home care nurse

involvement in discipline (O’Brien & Wegner, 2002). Mothers often assumed the

leadership position with the home care nurses, identifying roles, protecting the privacy of

the other children, setting up family boundaries and a good working environment for the

nurses (Cavanagh, 1999). Therefore, having home health care nurses help in caring for

the child who is technology dependent comes at a cost; the loss of privacy and a

transformation of the meaning of “home” as well as a transformation of the physical

environment into that of a “mini-ICU”.

Cost- Alteration in Parenting Role

Another cost associated with caring for the child who is technology dependent is a

change related to the typical parenting role. Many qualitative studies describe the

discomfort parents experience when they need to assume the dual role of nurse and parent

while caring for their child who is technology dependent (Kirk & Glendinning, 2004;

Kirk et al., 2005; Wang & Barnard, 2004). They experience conflict regarding

performing procedures that may inflict pain on their child due to the fact that the main

role of the parent is typically that of protector (Wilson et al., 1998; Kirk et al., 2005).

Parents prefer to see themselves as primarily the parent and do not want the relationship

with their child to be defined by the nursing activities performed and resent how the

nursing role often dominates their parenting experience. In other words, parents are

seeking to have primarily a parent-child relationship (Kirk et al., 2005).

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Not only do parents of children who are technology dependent assume the dual

role of nurse and parent but many times assume multiple additional roles as well. Parents

often manage the child’s condition, organize and advocate for services, access funds and

deal with nurses, equipment vendors, and insurance companies as well as perform a

multitude of procedures all the while monitoring the child’s progress in terms of health,

growth and development (Kirk, 2004; Allen et al., 1994). Additionally, parents frequently

have to make complex judgments regarding the dosing of medications depending on the

status of the signs and symptoms related to the condition (Kirk, 2004). Due to this

heightened responsibility, parents express worry that they will overlook problems and

that the child will suffer. Consequently, parents often look to home health care

professionals for reassurance that they are doing a good job (Torok, 2001). All 12

mothers in a study by Neuss (2004) had recurrent, intrusive anxiety regarding the child’s

death. Studies by Carnevale et al. (2006) and Lee (1996) concur with this finding.

Therefore, the multiple roles and the great responsibility that parents of children who are

technology dependent assume is daunting and can lead to worry and intrusive anxiety

regarding the child’s health and safety while they are caring for the child at home so look

for reassurance from home health care professionals that they are doing a good job.

“Mothering” Role

Mothers described their challenges in the role of “mothering” a child who is

technology dependent. Despite all the difficult and challenging work related to caring for

a child who is technology dependent, 11 of 12 mothers in a study by Neuss (2004) had

strong feelings of attachment to the child. Mothers frequently defined themselves

through their child. Though the mothers thought that they lost pieces of their prior self, it

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was replaced by who they became; one who experienced positive growth and a greater

sense of self esteem due to competency they developed in mastering the techniques

required to care for the child who is technology dependent (Neuss, 2004; Lee, 1996). In a

qualitative study of two, single, African American mothers of infants who were

technology dependent, Lee (1996) found that their experience of mothering was no

different in KIND but different in DEGREE. Being able to show off their child helped

reinforce their identity as mother. Positive feelings regarding themselves as mother

increased as the baby became more responsive. The mother’s daily schedule was dictated

by the baby and was monotonous, fast paced, socially isolating leaving little personal

time for the mother. It was labor intensive to perform the care, go on multiple trips to the

doctor and get all equipment ready to leave the house. The first month was the scariest,

then the mothers felt much better about caring for the child who is technology dependent

as they developed increased confidence with the routine particularly when they were able

to handle novel situations in the first months after discharge. Rehospitalization decreased

a mother’s confidence as they second guessed if they could have prevented the illness.

The mothers felt like they were under house arrest and could not “pick up and go”

however they felt guilty if they did leave. Taking the baby out for car trips helped them

feel more normal. The two mothers got very little help mostly due to fear of relatives and

friends that something might happen that they would not know how to handle.

Previously, they could go out and socialize but now the telephone was the primary tool

for socializing (Lee, 1996).

The mothers in the study had a need to normalize the situation so they would not

be excluded from the experience of motherhood. The mother’s ability to do similar things

for the baby that all mothers do and have the infant respond was reassuring that the child

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who is technology dependent is a baby first and foremost. Therefore, parents did things

so that they could display the normal aspects of the child such as hide the trach and

remove the oxygen temporarily. The mothers tried to show how care of the child who is

technology dependent was experienced as normal and everyday using phrases such as

“easy as changing a diaper”(Lee, 1996). In summary, “mothering” a child who is

technology dependent is labor intensive and often socially isolating yet affords an

opportunity for increased confidence, personal growth and satisfaction particularly when

the mothers found ways to normalize the child’s experience; viewing them as a child first

and foremost.

Process of Care

Process of Care- Goals

In addition to the costs of caring for the child who is technology dependent and

the plethora of changes in roles experienced by parents, research studies also examined

the process of care as experienced by the parent, most frequently, the mother. Some

researchers studied the process in terms of stages of adaptation while others looked at

themes. Still others looked at goals of managing the child who is technology dependent.

O’Brien (2001) described the goals for families of children who are technology

dependent as managing daily life with technology; maintaining a functioning family by

achieving some sense of stability while attending to multiple, complex issues, prioritizing

and making compromises that would not jeopardize health. Additional goals were to

make sense of life as well as to continually restructure family life. This restructuring is

due to the constantly changing needs of the child who is technology dependent and is

based on the child’s health, schedule, and routine as well as other family roles and

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responsibilities (O’Brien, 2001). Therefore, important goals for families of children who

are technology dependent are managing the balance of daily life with technology to keep

the child stable and healthy while concomitantly maintaining a functioning family.

The process of caring for the child who is dependent on technology has also been

described as “phases of protective care” (Judson, 2004). In a study of 19 mothers of

children dependent on parenteral nutrition, Judson (2004) elucidated three phases that

mothers pass through in the journey of caring for their child who is technology

dependent. The first phase was gaining control that included committing to care by

preparing to go home, organizing chaos and establishing a routine. The next phase, taking

control, included watching over the child and developing expertise and vigilance in care,

challenging the system and promoting normalcy by being flexible, accepting uncertainty

and fostering acceptance. The last phase, maintaining control, includes putting life in

perspective, finding respite and support and celebrating the positive (Judson, 2004). The

researcher noted that in the process of care, the key to normalization for the child was for

parents to have clear expectations for behavior and discipline.

Bull (1992) identified the process of care as “phases of mastery” in the care of the

child who is technology dependent. Phases included the following: identify problems,

take action, modify the environment, change roles, and establish routines. In the

identification of problem phase, the worry parents experience is a symptom of underlying

problems such as new skill they had to learn, role changes or changes in family

functioning. In the take action phase, families sought information, practice new skills,

solve problems and access resources. This phase requires persistence, organization,

willingness to try new things and to get help from home care professionals. Parents must

also modify the environment in order to function and change roles; many times having to

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give up former activities and take on new functions. Last, by two months after discharge,

parents establish routines and move to mastery where new routines are established and

the family is in control. Barriers to mastery include low income, lack of awareness of

resources, few network and informal supports and gaps in community services (Bull,

1992). Therefore, Bull (1992) found that within the process of care there are phases of

mastery that eventually led to a goal of establishing a routine and control of family

functioning by two months following discharge.

Process of Care- Organization

Organization was noted to be an important component in the process of care for

the technology dependent child. Torok (2001) found that within the first 4 weeks at home

some mothers of infants who were technology dependent had become experts while

others were floundering but with time and experience all progressed to managing

effectively. However, all 8 mothers included in this qualitative study were Caucasian,

with a mean age of 30 years, and all but one was married with a college education so it is

hardly representative of the population of mothers of children who are technology

dependent. Important findings from this study were that mothers moved from learning

care to making judgments regarding the infant’s health and getting to know the infants

likes/dislikes as well as health related behaviors. Mothers recognized changes in family

dynamics and met family needs by adjusting roles, responsibilities and lifestyles. Also, a

big part of caring for these infants was changing priorities so they no longer did things

that were previously important. Mothers learned to manage the increased work and time

required for infant care and navigated the ever changing emotions and concerns to

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achieve near normalcy in the family. Mothers also noted that while they recognized the

differences, they also saw similarities to other infants. They also realized that they need

to be vigilant in care of the infant. Therefore, the main point related to effectively

managing the child who is technology dependent is to BE ORGANIZED AND HAVE A

SCHEDULE and also achieve a balance for family functioning that includes changing

priorities to maintain the child’s health while at the same time meeting family needs by

adjusting roles and responsibilities (Torok, 2001).

Mothers reported improvements in the infant and family since the infant came

home (Boland & Sims, 1996; Torok, 2001). In a qualitative study of 17 families (5

families with a child who is technology dependent and 12 families with a spouse or

parent who was technology dependent), Boland and Sims (1996) described caregiving as

a solitary journey. All families interviewed valued home as a healing place that provided

meaning and reason for participation in caregiving. Moderating the sense of burden was

their commitment to care and the view that home was a healing place. Families felt their

loved ones would recover faster and that home was a more “normalizing” environment.

Families said they had no one else to help and had continuous responsibility with no

respite. The constant stress included financial concerns, lack of support, burden of 24

hour care, fatigue, fear of the unknown and the need to balance other family demands.

Therefore, while home was viewed as a healing place for an ill family member,

caregivers often paid the price in terms of a 24 hour burden of care with no respite while

at the same time balancing other family demands.

Murphy (1997) identified stages of adaptation families of children who are

technology dependent experience to achieve “normalcy”. The first month was described

as “awful” due to the anxiety- no matter how well they were educated prior to discharge.

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The next five months were critical but tolerable- things could “heat up” at any moment.

Parents felt it took 4-6 months to leave the house and resume other family and work

activities without major anxiety, and if they were unable to leave the house regularly

during this time they became socially isolated. The next 12-18 months were relatively

calm but families experienced increased difficulty with the home care nurses. The two

year point in home care was a critical point that included increased discord in the

marriage and/or with the home care nurses, possibly due to realizing that the situation

was permanent. At this time there were explosive firings of nurses or home care agencies.

Finally, the family reached the point of resolution and reorganized themselves to “get on

with life”. Families made a conscious effort to do normal everyday activities with the

child and came to accept that family life had changed but they did not need to be

consumed by it (Murphy, 1997). The researcher concluded that the adaptation process to

reach normalcy in family life occurs over time and can take over 2 years for many

families (Murphy, 1997).

Tommet (2003) also found that families of children who are technology

dependent undergo a process over time whereby they achieve an “order out of chaos”. In

a qualitative study of 5 couples (mother-father pairs) with a child who was technology

dependent ages 5-7 years, Tommet (2003) found that 2 patterns emerged over time-living

in uncertainty and order in chaos. Parents live in uncertainty with a period of intense

disruption and disorganization that was the dominant pattern for the first 2 ½ to 3 years

after their child was born. During this time, the family struggled to survive and gain

control of an uncontrollable situation. The time was characterized by intense medical,

caregiving and personal crisis. Families experienced an evolving relationship with home

care providers and isolation from family and friends. Order in chaos lasted from 3 to 5-6

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years where they enjoyed relative medical stability. This period was characterized by

personal growth, greater family cohesion, a letting go of the past life and living in the

present and development of advocacy skills. Old friends often did not have the same

common interests so they developed a new network of support that included health and

educational providers (Tommet, 2003).

Process of Care- Ensure a Childhood

In another qualitative study of 23 caregivers of children age 1 to 13 years from 13

families, Cavanagh (1999) discovered 5 core processes that were similar across all

diagnoses, ages, and types of technology the children required. These processes were

accepting, learning, taking back the child, reshifting the everyday, and seeking validation.

Accepting meant coming to terms with the situation. Learning was more than

knowledge that can be gleaned from a textbook and was an essential element that allowed

the parent to regain authority and become an expert of their child’s condition. Taking

back the child gave meaning to life and required commitment and involvement of the

family to bring the child home. “Reshifting the everyday”, required family lifestyle

changes to fine tune the household in order to meet the physical and social needs of the

family. Seeking validation was the long term work of caregivers to show others why

caregiving is valuable and that the price they pay increases the quality of life for the child

who is technology dependent.

Parents strove to have their child recognized as a valued member of society and

worthy of a childhood. The process that families underwent sought to merge everyday

family routines and care for the child who is technology dependent to ensure a childhood

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and normal family life. The motivating factor for caring for the child who is technology

dependent at home was to reconnect and bring them a childhood.

Perseverance was seen as important family attribute. The primary concern for all

of the families was to continue to preserve the family. Daily life was different from the

other families but became more predictable as time went on because the child care

became routine and the medical procedure became second nature to them (Cavanagh,

1999). This qualitative research study confirms that families of children who are

technology dependent undergo a process over time to merge the care of the child with

everyday family routines in order to ensure a childhood for the ill child while at the same

time preserving a normal family life. The normalizing of the child’s care and medical

procedures occurs as care becomes routine and second nature to parents.

Wilson and colleagues (1998) concur with these finding reporting that to gain a

sense of control and to stabilize family life the mother had to work with home health care

nurses to set role expectations. The mother’s attention to detail when caring for the child

was to ensure the standard of care. Goals for these mothers were to facilitate day-to-day

family life while giving care to the child who is technology dependent.

In a study of 38 family members of 12 children who were ventilator dependent,

Carnevale and colleagues (2006) found that the main goal families strive for is

STABILITY. This stability however can be undermined by limits in finances, cohesion

and the unpredictability of the child’s chronic illness. When families had things under

control, daily activities became routine. In other families who were unstable or on the

verge of unraveling, every new incident would unbalance the family organization and

every day life was chaos. Carnevale et al. (2006) found that all unstable families in her

study were those with single parents. Additionally, finances were found to be the sole

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factor that differentiated between those families who had things under control and those

who were on the verge of unraveling. Families in both groups however reported living

daily with distress and enrichment.

Process of Care- Summary

In summary, qualitative research that explored the lives of families of children

who were technology dependent found that a family’s primary goal was managing the

balance of daily life with technology to keep the child stable and healthy while

concomitantly maintaining a functioning family (O’Brien, 2001). Additional goals for

these families as described in the literature include stability (Carnevale et al., 2006;

O’Brien, 2001), ensuring a childhood (Cavanagh, 1999), and promoting “normalcy” for

the child who is technology dependent as well as the family (Boland & Sims, 1996;

Cavanagh, 1999; Judson, 2004; Murphy, 1997; Torok, 2001). Many of the researchers

also described the process of caring for the child who is technology dependent in terms of

stages of adaptation. Bull (1992) found that the process of establishing routines took

approximately two months. Other researchers found that the process took much longer;

about 2-3 years (Murphy, 1997; Tommet, 2003).

The process of care, more specifically family management of the child who is

technology dependent, was described in qualitative studies as requiring a variety of skills

such as prioritizing (O’Brien, 2001; Torok, 2001), flexibility (O’Brien, 2001; Judson,

2004), organization (Judson, 2004; Bull, 1992; Torok, 2001; Murphy, 1997), and

establishing a routine/schedule (O’Brien, 2001; Judson, 2004; Bull, 1992; Torok, 2001;

Cavanagh, 1999; Carnevale et al., 2006). The process of managing the child’s care also

required a restructuring of family life to meet the needs of the child who is technology

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dependent as well as the needs of other family members by shifting family roles and

responsibilities (O’Brien, 2001; Bull, 1992; Torok, 2001; Cavanagh, 1999). Gaining

control of the child’s care process by becoming an expert and mastering their child’s care

to the point that care became second nature to them was also noted as an important

feature of effectively managing the care of the child who is technology dependent

(Judson, 2004; Bull, 1992; Tommet, 2003; Cavanagh, 1999; Carnevale et al., 2006).

The final stage in the process of caring for the child who is technology dependent,

typically after approximately 2-3 years, was described as one where families were able to

put life in perspective and celebrate the positive (Judson, 2004). At this stage, many

families felt they had achieved personal growth (Tommet, 2003; Miles et al., 1999) and

increased family cohesion (Tommet, 2003). While they realized and accepted the fact that

their family life had changed, they were not consumed by it (Murphy, 1997). Researchers

in qualitative studies noted that the following were factors associated with difficulties in

managing the care of the child who is technology dependent: lower family income, lack

of community resources, low social support, single parent family, decreased family

cohesion and increased unpredictability of the child’s illness (Bull, 1992; Carnevale et

al., 2006).

Effective Management Factors

Effective Management Factors- Stability

While some qualitative researchers described a process of care related to the child

who is technology dependent, others described factors related to effective care

management. Managing the care of these children requires skill, organization and

creativity (O’Brien, 2001). Care includes frequent and unexpected changes, limited and

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unpredictable parental control as well as fragility and instability. Families often hang

together by a thread and report that it doesn’t take much for it to crumble. Some families

described the experience as like “living in a house of cards” (O’Brien, 2001). O’Brien

(2001) proposed ways to increase stability: vigilance, advocacy, reframing, and humor.

Vigilance includes the close monitoring of health status, treatments and close oversight of

all parts of care. Reframing includes focusing on achievable outcomes, looking for the

positive in people and maintaining hope (O’Brien, 2001). Time management was also an

important component in managing the care of the child who is technology dependent.

Families over time became more organized and planned ahead. It was important to have a

routine and time schedule but the inflexibility and lack of spontaneity was frustrating.

Families frequently had disruptions in child/family routines and needed to continuously

make choices as to priorities (O’Brien, 2001).

Effective Management Strategies- Perspective

Wilson and colleagues (1998) identified four categories of success or lack thereof

in management of care that ranged from mothers who met the self-determined standard of

caregiving to mothers who were unable to recognize success. Mothers who met the self-

determined standard of caregiving were able to manage family needs, had a large social

network and knew they were successful so consequently felt confident, positive and

trusted professionals with the child’s care. Mothers who felt they did not meet a self-

determined standard managed the day to day needs of the family but were always looking

for ways to improve. They knew their limits but did not use process skills or strategies.

Mothers who were not successful in managing day to day life of the family but successful

caregivers were reclusive and dedicated themselves to the care of the child who is

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technology dependent so consequently had little social life. The last group of mothers

was unable to recognize their success to the detriment of the family and saw themselves

as failures, distrusted others, felt they could never do enough, worried, felt lonely and

isolated with little social life (Wilson et al., 1998). This research points out that a

mother’s perspective regarding how successful she is related to her caregiving role and

family management has long range consequences for the mother’s, child’s, and family’s

well being. Of particular importance is the effect of the mother’s perspective of how she

is managing the child and family on mental health.

Social Support

Social Support- Family, Friends and Clergy

Another factor that greatly assists in care management is social support.

Maintaining connections with extended family and friends is important, however,

parental friendships often change over time as the family must devote greater time and

energy to family needs (O’Brien, 2001). Neuss (2004) found however that 11 of 12

mothers had difficulty making friends and maintaining friendships due to lack of energy,

time and inability to leave the child to enjoy social activities. Ostwald et al., 1993 found

that less than 30% of families with a child who is technology dependent receive

assistance from anyone except family and only 15.2% received emotional support from

clergy. Spiritual beliefs comforted the mothers however most felt they received

inadequate spiritual support. Furthermore, 4 out of 12 mothers had a negative experience

with a member of the clergy being unwilling to give spiritual help due to their discomfort

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with disabilities and children who are technology dependent (Neuss, 2004). A few

mothers said that the nurse was their closest non-related relationship (Neuss, 2004).

Most extended family members were uncomfortable providing care to the child

who is technology dependent and mothers reported that fear and misunderstanding of the

disability were reasons that extended family was not more involved (Lee, 1996; Neuss,

2004). On the contrary, half of the mothers had help with the healthy sibling. Neuss

(2004) reported that not one of the 12 grandmothers of the child who is technology

dependent offered consistent emotional care or support. The telephone was found to be an

important tool in maintaining friendships and receiving social support (Lee, 1996).

Furthermore, Cavanagh (1999) found that community involvement was marginal.

Therefore, while social support was viewed by mothers as a tremendous help in their

being able to manage the care of the child who is technology dependent, most did not

receive this social, emotional or spiritual support from friends, relatives, members of the

clergy or their communities. Frequently this lack of involvement by others was due to

fear and discomfort with technology and disabilities.

Alternative Social Support

Going beyond the typical venues of social support, the literature described other

sources of support that were helpful to the mother in the management of care for a child

who is technology dependent. Mothers found that having computer access and on-line

support was very beneficial. Neuss (2004) found that 5 of 12 mothers found computers

helped them feel connected and was a helpful source of information. Computer support

groups would help parents of children who are technology dependent have a sense of

belonging, companionship and feeling of community (Neuss, 2004). Wilson and Morse

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(1998) suggested a discharge hotline as a resource to help families of children who are

technology dependent who may still have management questions after they leave the

hospital.

The hospice model of service that includes family involvement and spiritual care

was suggested by Neuss (2004). Lee (1996) suggested that having other parents who can

relate to their experience as a peer support would help to decrease the isolation and give

them hope that things will get better. Mothers also reported that it was a tremendous help

when nurses set aside a block of time to sit down with them to discuss feelings and

concerns (Kirk et al., 2005; Lee, 1996). In summary, the literature described a variety of

alternative methods of supporting these families. Included in these supports are on-line

support groups that would assist with the challenge of leaving the child for extended

periods of time. Other supports include a hospital discharge hotline, hospice model of

support and one on one time with a nurse. These suggestions offer promise for future

research.

Home Health Care Nursing Support

Another area examined by researchers of families of children who are technology

dependent was the partnership that developed between families and health care

professionals, particularly home health care nurses. Few mothers had a smooth alliance

with nurses and often fought over control of their child (Judson, 2004; Murphy, 1997;

Wang & Barnard, 2004). It was difficult for many mothers to give up the care of their

child to others (Judson, 2004). Mothers reported having difficulty with TRUSTING the

nurses to care for their child as they would (Wang & Barnard, 2004; Kirk & Glendinning,

2004; Neuss, 2004; Wilson & Morse, 1998). Neuss (2004) found that 10 of 12 mothers in

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her study lacked trust in other’s ability to care for their child. They reluctantly let others

care for their child but only for short periods of time and when the mother could monitor

what was happening however, this was often done with much trepidation and worry.

Mothers did not readily accept offers of help to care for the child who is technology

dependent (Neuss, 2004). Therefore, although mothers needed respite due to the long,

exhausting hours of care on a daily basis, researchers discovered an attitude of

ambivalence often due to lack of trust that others would care for the child as they would.

Relinquishing part of the child’s care also meant potential struggles with other caregivers

for control.

In interviews with mothers, Neuss (2004) identified factors that helped with care

management. Home nursing was a valued resource for many however, due to the recent

nursing shortage, shifts cannot be covered. Amazingly, these mothers continue to manage

the care of the child who is technology dependent despite the uncertainty related to the

child’s medical instability. Furthermore, these mothers help their children survive life-

threatening medical complications. Many of the children have an unpredictable life span

so although they prepare themselves for what might happen they make the effort to stop

death (Neuss, 2004).

Findings from research studies include mother’s reports of desirable attributes of

the ideal home health care nurse. What mothers wanted most was a knowledgeable,

medically competent nurse that CARED and would talk to, sing or read to the child as

well as be affectionate towards the child (Judson, 2004; Kirk et al., 2005; Neuss, 2004).

Kirk et al., (2004) found that many mothers felt that nurses only did the technical parts of

care and neglected the emotional care. Mothers felt that the lack of emotional connection

between the nurse and child influenced how they delivered care (Kirk et al., 2005).

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Mothers wanted nurses who came to the home to be honest about limitations in their

knowledge (Kirk et al., 2004). With a competent nurse the mother felt relaxed at home or

able to leave the house (Neuss, 2004). Cavanagh (1999) discovered that mothers wanted

the nurse to be part of “family life” but did not want the nurse to be possessive of the

child who is technology dependent which required the nurse to walk a fine line. In other

words, they wanted the nurse to be there but not be there (Cavanagh, 1999). Therefore,

attributes of the ideal home care nurse were that the nurse would be caring,

knowledgeable, able to incorporate developmentally appropriate activities and sensitive

to family boundaries.

Mothers frequently were the liaison with home health care nurses. A mother’s

role included monitoring family boundaries, communicating and negotiating child rearing

expectations, ensuring that confidence was not betrayed and that the nurse was not over-

protective of the child (Cavanagh, 1999; O’Brien et al., 2002). Nurses in the home were

seen as supportive yet disruptive (Wang & Barnard, 2004) as their presence affected

privacy and family dynamics (O’Brien et al., 2002) such as marital, sibling and parental

relationships (Murphy, 1997). At times, judgments were made by nurses regarding family

lifestyle (Murphy, 1997). Parents also felt that health care professionals did not value

their experiential knowledge. Experiential knowledge with medical knowledge helped

them provide individual care so that they could detect minute changes that signaled that

their child was ill (Glendinning & Kirk, 2000; Kirk et al., 2005). Some health care

professionals however were threatened by the expertise of the parents (Glendinning &

Kirk, 2000).

Conversely, nurses were also seen in a positive light. There were sometimes

viewed as a member of the family therefore provided families with much needed social

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support (Kuster, 2002) and decreased the sense of isolation particularly in socially

marginalized families (Cohen, 1999). Nurses helped mothers to develop increasing self

confidence and satisfaction by giving positive feedback on their skills and strategies used

to care for the child who is technology dependent (Lee, 1996; Wilson et al., 1998).

Having a primary nurse was highly valued from a continuity stand point (Kirk &

Glendinning, 2004) as was high quality and consistent nursing care (Tommet, 2003).

Lack of this consistent, high quality nursing care contributed to family disorganization

(Tommet, 2003). Therefore, while there were issues at times with home health care

nurses over control of the child, family boundaries, and privacy, nurses were also seen as

a valuable asset to families particularly related to social support and positive feedback

that helped to boost the mother’s self confidence.

Lack of Discharge Preparation

Studies of families with children who were technology dependent consistently

found that parents felt they lacked adequate preparation for the experience prior to

discharge. Parents felt there was a lack of preparation for the technical care they would

have to provide (Wang & Barnard, 2004) as well as the emotional strain they would

experience (Judson, 2004). Some had only preparation for the care of the child and not

the “rest” of what home care entails such as working with the nursing agency or the

weight of responsibility for managing home care (Allen et al., 1994; Carnevale et al.,

2006; Murphy, 1997; Torok, 2001).

Neuss (2004) found that all 12 mothers in her study reported that no amount of

training would have prepared them for the care that was required at home. Some mothers

suggested that hospital staff increase the family’s responsibility for the infant’s care and

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equipment prior to discharge so that they can ease into the role (Kirk & Glendinning,

2004; Torok, 2001). Other suggestions were that families have multiple support people

trained for respite and that hospital staff should check up on families 6-12 months after

discharge to assess how they are adjusting to home care (Torok, 2001). Another

suggestion was to tell parents there are demands and hardships but also deep enrichments

and rewarding experiences (Carnevale et al., 2006).

Thyen et al. (1998) proposed that discharge planning and support needs to not

only focus on the child but also the prevention of secondary social and psychological

morbidity of caregivers. Therefore, findings from the literature suggest that increased

preparation and training prior to discharge should include not only technical care but

information on how to manage the many demands related to caring for a technology

dependent child. Furthermore, facilitating access to psychosocial supports would greatly

assist with prevention of social and psychological morbidity.

Positive Gains

Despite all of the challenges and hardships involved in caring for a child who is

technology dependent at home, parents also spoke of the positives. Many reported

satisfaction seeing the emotional, developmental and social growth and improved health

of their child following discharge from the hospital (Kuster, 2002; O’Brien, 2001; Wang

& Barnard, 2004) as well as improvement in the family situation (Torok, 2001). Many

mothers felt they were better able to assess their child’s day to day needs than any

professional (Neuss, 2004). Families, particularly those who are socially marginalized

experienced increased self esteem by learning skills necessary to care for the child who is

technology dependent (Lee, 1996; Neuss, 2004; Cohen, 1999). Mothers felt competent

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and proud of what they had accomplished. These families also had the experience of a

close relationship with a health care professional who could serve as a positive role

model as well as decreased their social isolation (Cohen, 1999).

In a study of 14 families with children who were ventilator dependent, Allen et al.

(1994) found that 93% liked the decreased travel to the hospital, 71% reported improved

emotional condition, 50% thought the situation between siblings had improved, 43% felt

they had greater control, and 36% said that the medical condition of the child who is

technology dependent had improved. Some mothers reported that they had acquired

positive personality traits such as more patience and increased spirituality as a result of

caring for the child who is technology dependent (Neuss, 2004). Others felt that caring

for the child who is technology dependent had brought the family closer together. Still

others wanted to help others in similar situation. Furthermore, almost half of mothers in a

study by Neuss (2004) were thinking of a future career in a medical environment. Parents

living in uncertainty experienced growth when they were able to let go of trying to

achieve predictability and control and learned to live in the present (Tommet, 2003).

Finally, mothers received pleasure from interaction with the infant and their role as a

mother (Lee, 1996). Therefore, despite all the hardship and challenge related to caring for

a child who is technology dependent, mothers reported positive growth for themselves,

their families, and their child.

Summary

This review of the literature regarding the child who is technology dependent and

their families revealed many research gaps. Standardized instruments measuring severity

of illness would be helpful with this population. Other gaps are the absence of

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longitudinal, quantitative studies that address how a culturally diverse sample of families

manage the child who is technology dependent who receives home care, particularly

related to normalization. Another gap in the research is the prospective account of the

first few months following discharge. There is also the need to examine the positive

aspects of mothering the child who is technology dependent in a longitudinal study over

the year following discharge matched with healthy controls. Furthermore, an additional

gap in the research literature is a study that would examine the personal characteristics of

parents, characteristics of child who is technology dependent and illness and their

relationship to distress. This would greatly assist with development of an intervention to

assist families, particularly mothers in the management of the child who is technology

dependent. Last, it is important to study which families are at greatest risk for family

dysfunction and distress so interventions can be developed that can assist with family

functioning.

Normalization as a Management Strategy

Definition and Attributes

Normalization is a concept that includes cognitive and behavioral dimensions and

is defined as a pattern of family response to a child with a chronic condition, an ongoing

process of accommodating the child’s evolving social, emotional and physical needs

(Deatrick et al., 1988; Morse et al., 2000). This includes “adjusting the environment to

provide normal life experiences that will meet the child’s evolving social, physical,

intellectual and emotional needs” (Murphy, 1994, p. 10) and at the same time manage

family life and activities so that they can lead as close to “normal” of a family life as

possible (Murphy, 1994). Attributes of normalization include that a family “(a)

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acknowledges the condition and its potential to threaten their lifestyle, (b) adopts a

‘normalcy lens’ for defining child and family, (c) engages in parenting behaviors and

family routines that are consistent with ‘normalcy lens’, (d) develops a treatment regimen

that is consistent with a ‘normalcy lens’, and (e) interacts with others based on a view of

child and family as normal” (Deatrick et al., 1999, p. 211).

As one can surmise from the above attributes, a family’s definition of “normal” is

guided by a philosophical approach and not necessarily the reality of the situation,

primarily because parents choose to attend to what is “normal” and disregard what is

abnormal about their situation (Murphy, 1994). Families therefore, modify and redefine

“normal” over time to fit their current circumstances and ongoing uncertainty (Rehm &

Franck, 2000). Consequently, the child with a chronic illness is treated as “normal” living

a life like others in the outside world without illness so as not to be seen as deviant from

societal expectations; thus enabling them to “fit in” (Morse et al., 2000; Young, 1995).

Therefore, reality is reconstructed to emphasize those aspects of life that remain

unchanged despite the chronic illness. Normalization is used to manage the disparity

between the preferred views of their family life as “normal” and the problems and

challenges they face in their everyday life (Rehm & Franck, 2000).

Normalization is now seen as a clinical standard in judging family management

effectiveness and health care professionals encourage families who have a child with a

chronic condition to live their lives as close to normal as possible and continue

interactions with others (Deatrick et al., 1999). Other researchers have questioned this

standard and ideology of normalization, viewing it instead as a Western ideology that is

shaped by socio-political, economic and historical factors and not necessarily embraced

by Chinese immigrants to Canada (Elfert, Anderson, & Lai, 1989). Young (1995)

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however, developed a theoretical framework to study the strengths and management

strategies African American families’ use when a member has a chronic illness.

Management strategies for these families included normalization due to the pressures

from society and the hazards of being considered “different” such as the stigma

associated with it (Young, 1995). Therefore, while many families use normalization in

response to a child’s chronic illness this is not always the case with families that are from

non-Western countries.

Some researchers emphasized exploring the childhood chronic illness experience

through the perspective of social stigma (Chisholm, 2000). Joachim and Acorn (2000)

however, encouraged researchers to examine chronic illness through the normalization

lens and the stigma lens. They argue that “in order to capture and understand the dynamic

and evolving experience of people with chronic condition, researchers should consider

the interdependence of the two perspectives and avoid assumptions that derive from

stigma or normalization alone” (p. 37). The authors posit that to study the chronic illness

perspective from a stigma lens overlooks the energy available in the process of

normalization, however, using only the normalization lens “may underestimate the power

of social context” (Joachim & Acorn, 2000, p. 45).

Normalization Management Behaviors

Many have researched management behaviors associated with normalization.

Management behaviors, also called strategies, are “discrete behavioral accommodations

that family members use to manage the illness on a daily basis” (Knafl et al., 1996, p.

316). Clarke-Steffen (1997) developed a model regarding the process of adaptation to the

diagnosis of cancer in a child that included use of management strategies to construct

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reality as well as to construct a new normal. Cohen (1993) found that families dealing

with childhood cancer manage uncertainty by developing strategies to create certainty

and in the process create a “New Normal”. Gravelle (1997) described how parents

manage their child’s illness on a daily basis by “facing adversity” based on a changing

definition of adversity.

Other authors described management strategies within the process of defining the

ill family member as normal (Robinson, 1993), and in relation to parenting (Scharer &

Dixon, 1989). In a qualitative study of parents and children living with HIV, Rehm and

Franck (2000) identified long term goals for normalization and management strategies to

meet the goals. The goal of staying healthy meant making lifestyle decisions and actively

participating in treatment. Facilitating participation in school and social activities meant

managing the stigma and being selective regarding disclosure. Enhancing the social and

emotional health of family members was a goal that was managed by enlisting extended

family members to help (Rehm & Franck, 2000).

Sullivan-Bolyai, Knafl, Sadler, and Gilliss (2004) presented a list of management

responsibilities and activities for parents caring for a chronically ill child. The list was

developed to help address day-to-day management activities and responsibilities. The

four major categories include managing the illness; identifying accessing and

coordinating resources; maintaining the family unit; and maintaining their own

emotional, physical and spiritual health.

Jerrett (1994) and May (1997) in their research of children with Juvenile

Rheumatoid Arthritis and low birth weight infants, respectively, found that normalization

and management strategies were accomplished through learning care required by their

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child. Jarrett (1994) found development of a routine and schedule assisted with

normalization and fostered integration of necessary caregiving into family life.

Gallo and Knafl (1998) in a secondary analysis of 58 families identified three

approaches to illness management: strict adherence; flexible adherence; and selective

adherence. No differences were found in management approaches across disease

conditions. Knafl and Deatrick (2002) found that families who choose to focus on

normalcy use a flexible approach regarding treatment regimens and incorporated the

treatment in the day-to-day routines of the family. Hatton, Canam, Thorne, and Hughes

(1995) concur with these findings that increased confidence in parents of diabetic infants

and toddlers led to more flexibility in the management of routines. Additionally, Thorne,

Radford and McCormick (1997), in a qualitative study of children with long term

gastrostomy tubes found that families adapted by manipulating feeding schedules and

treatments to suit the quality of life for the child and family. Therefore, family

management behaviors to accommodate a child’s chronic condition that incorporated

flexibility in adherence to medical regimen, assisted families in the normalization process

and led to a more desirable quality of life.

Family Management Style Framework

Knafl et al. (1996) comprehensively explored the use of family management

strategies to accomplish normalization. In a 1-year qualitative study of 63 families that

included both parents, the ill child and the siblings, the authors delineated five different

types of family response to childhood chronic illness Family Management Style (FMS)

that reflect the degree of normalization. Normalcy is the overriding theme in the Thriving

FMS and the dominant theme in the Accommodative FMS. The Enduring, Struggling and

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Floundering FMS use consecutively lower degrees of normalization. The FMS

framework developed by Knafl and Deatrick (1990) guided design of this study. No

relationship was found between FMS and disease, ethnicity, or socioeconomic status.

Additionally, 37% of families in the study experienced long term difficulty managing the

child’s illness.

The FMS framework was developed by Knafl and Deatrick and has continued to

be refined since the 1980’s guided by further research by their team as well as others

(Knafl & Deatrick, 1990; Knafl et al., 1996; Knafl & Deatrick, 2006; Deatrick et al.,

2006). Knafl and Deatrick are also responsible for concept development related to

normalization (Deatrick et al., 1999; Knafl & Deatrick, 1986; Knafl & Deatrick, 2002).

FMS is directly related to normalization in that FMS is defined as a pattern of approach

regarding how a family manages both family life and the child’s serious health care needs

(Knafl & Deatrick, 2006).

FMS is comprised of components that include the following: definition of the

situation which is the identification of significant events and the subjective meaning for

the individual; management behaviors which are behavioral accommodations made by

family members to manage on a daily basis; and perceived consequences which includes

whether the illness is in the foreground or background in family life (Deatrick et al.,

2006). Definition of the situation includes whether the parent views the child “normal”

and focus on capabilities or tragic and focus on vulnerabilities; the illness view whether

seen as serious and disruptive or if seriousness is downplayed; mutuality of parent views

regarding child, illness and approach to illness management; and management mindset

regarding the ease or difficulty of the treatment regimen (Deatrick et al., 2006).

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The management behaviors include a parent’s overall approach such as the ability

to develop a routine related to illness management and more specifically the parenting

philosophy regarding goals to ensure a normal life for the child; and the management

approach which is the development of routines and strategies to manage the illness as

well as incorporate the care into family life (Deatrick et al., 2006). Perceived

consequences includes the family focus which is parental assessment of balance between

aspects of family life and illness management ranging from family focus in those who are

using normalization to an illness focus in families who are floundering with illness

management; and future expectations according to both parents whether positive or

negative outlook. The socio-cultural context is those contextual factors that influence

how the family defines and manages the illness and the consequences of the illness they

perceive. These factors include cultural practices and beliefs, financial,

personal/individual and support received from the health care provider/health system

(Deatrick et al., 2006). The FMS Framework is currently in the process of being

translated into a qualitative instrument called the FMS Survey. Field testing is being

completed to test the instrument’s psychometric properties (Knafl & Deatrick, 2006).

Goals of Normalization

In a grounded study of parents of children with perinatally acquired HIV,

Santacroce, Deatrick and Ledlie (2002) found an evolution of Family Management Style

(FMS). Different management strategies were employed to adjust to the change in the

child’s medical condition and the stigma placed on the child from society. Other goals of

normalization described in the literature include that the child with a chronic illness be

integrated into the family without being the central focus. Additional goals are that the

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child’s competencies are maximized; the child develops independence, is well-adjusted

and develops into a functioning member of the family and society (Murphy, 1994).

Effects of Normalization on Families

Findings from research regarding normalization include the impact of the child’s

chronic illness on the family. Dashiff (1993) found an increase in family cohesion,

emotional stress, and parental role function with a concomitant decrease in spousal role in

families of adolescent female diabetics. Hatton et al. (1995) found that half of the

mothers of diabetic infants and toddlers quit gainful employment after the child was

diagnosed. Additionally, parents had to change their lifestyle in order to accommodate

the child’s diabetes and experienced marital strain and miscommunication.

Other studies using qualitative techniques described the experience of parenting a

child with a chronic condition as “parent straddling” (Johnson, 2000), a “balancing act”

(Morse et al., 2000). Both discussed the struggle a parent experiences in living with the

view of their child as “normal” when realizing they are disabled. The parents felt caught

between assisting their child in the “disabled as normal world” and the outside “everyday

as normal world”.

Threats to Normalization

Threats to achieving normalization noted in the literature include:

chronic/sustained uncertainty (Cohen, 1993; Haase & Rostad, 1994); parental conflict;

developmental transition of adolescents; poor illness control (Dashiff, 1993); and the

“problem-saturated perspective” of health care providers (Robinson, 1993). Knafl and

Deatrick (2006) stated that barriers to normalization also include increased treatment

complexity, need for stigma management and a parental focus on ways the child is

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different from peers. Additional factors that can affect the use of normalization are age of

the child (greater difficulty with adolescents), time since diagnosis (decreased

normalization if less than one year since diagnosis), lack of knowledge, visibility of

condition, and cultural issues (Asian populations have greater difficulty with

normalization) (Murphy, 1994).

Knafl and Deatrick (2002) also examined barriers to normalization in a

qualitative, longitudinal study they had conducted with 63 families of children with

chronic conditions. In families who, according to their qualitative grid, had never

experienced normalization, life was viewed as a series of multiple, ongoing difficulties.

Illness was the focus of family life and the treatment regimen was viewed as a significant

burden with behaviors that made the family different from other families. Their parenting

style and view of the child had changed as a result of the illness and conflicts with the

child were mainly regarding adherence to the treatment regimen. The child was viewed as

a tragic figure and parents were consequently protective and indulgent. Parents perceived

that the child’s illness had negative consequences for family life and had contributed to a

decreased quality of life. Illness management was seen as a source of conflict and a

burden. Spousal conflict occurred when parents did not hold a shared view of treatment

management (Knafl & Deatrick, 2002).

Knafl and Deatrick (2002) found that in families who had experienced

normalization earlier in the study but had changed in the later data collection phases did

so due to a change in the disease process. Once there was better control of the illness,

families shifted again to use of normalization. Illness management was part of the family

routine if the illness was in remission or well controlled. Once the illness flared up again

the illness was brought to the foreground. Knafl and Deatrick (2002) also stated that the

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MAJOR BARRIER to normalization is parental conflict and not illness management

because the continual tension keeps the illness a focus of family life.

Strategies to Promote Normalization

Strategies parents use to help promote normalization were discussed in the

literature. Murphy (1994) found that families who use normalization ignore the abnormal

situation and expect others to do the same and prefer to attend to the “normal” aspects of

the child. Everyday life is carried on as usual despite the difficulty and parents reframe

the focus or perspective. Parents engage in usual parenting activities and participate in

normal activities by maintaining a routine. They are willing to allow the child to

experience “normal” developmentally appropriate activities without the parent’s

protection. Parents desensitize others or get people “used to” visible differences. The

child is integrated into the family and is not the central focus so there is less of a feeling

of vulnerability or stigma of being different. Parents foster the child’s independence and

positive self-perception and see their child as less affected than others. These families

tend not to be involved in groups for children with disabilities (Murphy, 1994). Knafl and

Deatrick (2002) found that families recognized the seriousness of the illness but hold the

view that the child and family are unchanged in important ways (normalcy lens). Having

a view that the child and family are “normal” sets the stage for managing the illness and

allows the family to sustain the usual pattern of family and child functioning (Knafl &

Deatrick, 2002).

Morse et al. (2000) studied 17 school age children who were ventilator dependent

and their parents at a day camp to determine “what is normal” from the perspective of the

child. Participant observation of the children in the day camp and interviews of the

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parents were performed. Findings include that these children identified two distinct social

reference groups: the disabled world of disabled as normal and outside world of everyday

as normal. Mothers in the study were able to identify with both groups according to the

child’s ability, needs and risk. The child with a disability who was able to normalize

compared their ability to identify with both worlds according to need. The researchers

propose that normalization includes a process of identification with both the cultural

dominant group that is perceived as “normal” cognitively and the disabled children whom

they could identify with both physically and cognitively (Morse et al., 2000). Strategies

used by these children to fit in with the culturally dominant group was to use talents or

abilities to “fit in” with each world, seek commonalities to be “like them”, develop their

own capabilities and stretch the limits of their disability. Mothers introduced aspects of

the everyday as normal to their children by involving them in activities to heighten their

awareness of what was similar and different from others. The researchers propose that

this process of comparison was a foundation for normalization and gave the children an

opportunity to become independent (Morse et al., 2000). Factors that influenced

identification with both worlds were the age of the child, degree of social isolation,

degree of disability and the family.

Three qualitative studies described factors that promote normalization in families

of children who are technology dependent. Judson (2004) examined the process of

mothering a child who is dependent on parenteral nutrition in a qualitative study of 19

mothers. The researcher developed a theory of protective care comprised of 3 phases:

gaining control, taking control and maintaining control. Judson (2004) found that

normalization was related to the mother’s use of flexibility. Additionally, discipline came

up in interviews as a way to promote normalization. Promoting normalization, however,

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began with efforts at fostering acceptance of the child. Key factors to acceptance were

related to behavior and discipline; the child’s appearance; and the child’s ability to

participate in age-related social activities.

In a study of two African American single mothers of children who are

technology dependent, Lee (1996) found that both mothers did things to make the infant

seem normal or tried to make the experience seem normal. The mothers used strategies

such as camouflaging the tracheostomy or removing the oxygen and moving about the

house so the infant could be seen as normal (Lee, 1996). Carnevale et al. (2006) concur

with these findings. In a qualitative study of 38 family members of 12 children who were

ventilator dependent, the researchers found participants promoted normalization by

normalizing the home. Medical devices and equipment were hidden or camouflaged so

that it would not dominate the home environment. Other strategies included that the

ventilator was covered with a cloth or towel and the oxygen was hidden under a table.

Also, hospital beds were covered with colorful covers so that the controls and special

mattress were not visible.

Child’s Psychosocial Adjustment

Only one study quantitatively addressed the use of normalization by parents. In a

study of 76 mothers and their children with chronic physical disorders (CPD) age 8-12

years, Murphy (1994) found that a families’ use of normalization was related to the

child’s psychosocial adjustment. The researcher specifically developed the Normalization

Scale (Murphy & Gottlieb, 1992) for the study guided by the concept of normalization

and the FMS framework (Deatrick & Knafl, 1990). Two hypotheses were tested in this

study: 1) Families who use high levels of normalization will have CPD children with high

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personal and social adjustment and role skills and 2) Families who use high levels of

normalization will have CPD children with a high sense of self-competence. Specific

findings include that the mother’s perception that the family and other people perceived

the CPD family as normal was strongly related to overall high personal adjustment, good

peer relations and productivity in the child as well as low anxiety, depression, dependent

and hostility.

In this study, normalization was not related to a child’s perception of self

competence. The researcher proposed that this is because normalization is a philosophical

approach and a mother’s perception and appraisal of the situation is important to the

psychological adjustment of her child. To test the study’s first hypothesis, the researcher

found that the total score for the Personal Adjustment and Role Skills Scale and

specifically the anxiety/depression, withdrawal, and productivity subscales were

positively correlated with the “Actual Effect of the CPD on the Family” subscale of the

Normalization Scale (Murphy, 1994). Using regression analysis, it was found that the

best predictor of anxiety, depression, dependency, hostility and productivity was the

Normalization subscale of “Perception of Child and Family by Self and Others as

Normal”. Withdrawal was best predicted by the Normalization subscale “Actual Effect of

the CPD on the Family”. Using regression, mothers who perceived their child to be less

anxious and depressed as well as more productive also reported that their family and

others perceived the family and CPD child to be normal (Murphy, 1994).

The researcher did report a potential problem of wording on the Normalization

Scale. Mothers were asked to compare their family to both normal and CPD families and

a second set of items asked mothers about how others compared their family to these

same two groups. Factor solution showed that the mothers however, did not make the

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distinction. The mothers instead distinguished between normal and CPD family rather

than distinguishing between who was judging the family. The researcher proposed that

this might also be due to the fact that they may not identify themselves as a CPD family

(Murphy, 1994).

Normalization and the Child who is Technology Dependent

One qualitative study examined the use of normalization in families of children

who are technology dependent. Rehm and Bradley (2005) analyzed the applicability of

normalization attributes for families raising a child who is technology dependent. The

study included 30 families of children who were technology dependent and

developmentally delayed of either Caucasian or Hispanic ethnicity; 28% had household

incomes <$10,000. The researchers found that developmental delays compounded the

effects of the chronic condition thereby affecting how families were able to organize and

manage daily life. They concluded that families of children who are technology

dependent do not fit the published attributes of normalization developed by Deatrick and

Knafl.

Rehm and Bradley (2005) reported that denial was not possible, therefore,

families incorporated differences into what they considered normal. Families reframed

changes in lifestyle related to the chronic illness as positives but did not consider them

typical or desirable. They considered family life as “crazy normal” due the need for

frequent intervention and continued vigilance. Families attempted to create a family

routine but with many limitations due to the illness however this did not necessarily

decrease the quality of family life. School helped to normalize family routines and

provide respite; allowing children to participate in a “normal” childhood activity. Due to

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the fragility of the child’s condition, parents were not able to modify the treatment

regimen very much nor were they able to count on a predictable routine. Parents did not

view the child or family as normal; they realized the special needs and differences and

felt conspicuous in public due to the child’s equipment, appearance and behavior at

times. Many were upset regarding how their child was treated by others; their child was

often left out. The researchers concluded that a normalcy lens was impossible because

families needed to constantly attend to the care of the child and could not place the

condition in the background for long. Parents of children who are technology dependent,

the researchers proposed, did not relate to others as if their life was normal but strove

instead to reach a stable, positive state for all family members but not necessarily one the

was “normal”. Therefore one qualitative researcher, concluded that normalization can not

be achieved in families of children who are technology dependent and developmentally

delayed.

Summary

Noteworthy is the dearth of quantitative research regarding normalization,

particularly the relationship between the parent’s use of normalization and their own

psychological well-being. Additional gaps in the research regarding normalization

include the use of small sample sizes of mostly school age chronically ill children.

Sociocultural context and developmental transitions and their effect on family response

and management of chronic childhood conditions are largely unexplored. In particular,

the use of normalization by families of technology dependent children was explored in

only one qualitative study (Rehm & Bradley, 2005). Few studies examined normalization

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from the viewpoint of both parents. Many of the studies did not describe the ethnicity of

the sample and in those that did, the majority was Caucasian.

Family Functioning

Following discharge from the hospital, children who are technology dependent

are cared for by their families at home placing increased demands on the family due to

the numerous treatments, health care appointments and disruptions in the everyday

routine. This disruption requires a substantive change in the structure and functioning of

the family often requiring a redistribution of responsibilities and roles (Clawson, 1996).

History of the Concept

Historically, the concept of family functioning was first discussed in the 1950’s

by a group of psychologists from McGill University and later McMaster University who

were researching ways to classify and assess families, particularly in relation to family

therapy needs. The ensuing model, The McMaster Model of Family Functioning, has

structural-functional theory as its base. Structural-functional theory views the family as a

social system but places greater emphasis on family functions such as meeting member’s

physical and psychological needs of food, clothing and socialization. In addition, this

theory emphasizes family relationships between the family, individual members, family

structure and the ability of the family to perform family functions.

McMaster Model of Family Functioning

The McMaster Model emphasizes family structure and organization as well as

transactional patterns. The focus is on three dimensions that have the greatest impact on

the emotional and physical well-being of the members: basic task area such as food and

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shelter; developmental task area that occurs as a result of change over time in

development of the family such as birth of first child; and the hazardous task area such as

the way the family handles an illness crisis (Goldenberg & Goldenberg, 1992; Neabel,

Fotherfill-Bourbonnais & Dunning, 2000). Dimensions of family functioning related to

the three task areas include family communication, family problem solving, family roles,

affective responsiveness, affective involvement, and behavior control (Goldenberg &

Goldenberg, 1991).

Feetham Model of Family Functioning

Suzanne Feetham used the McMaster Model as a foundation and began her work

with the concept of family functioning, developing The Feetham Family Functioning

Survey (FFFS) in the 1970’s. According to Feetham, family functioning is “those

activities and relationships among and between persons and the environment which in

combination enable the family to maintain itself as an open system” (Roberts & Feetham,

1982, p. 231). The FFFS has as its base the family ecological framework, general systems

theory and Duvall’s theory of the stages of family development. The family ecological

framework identifies the family as the basic unit which involves examining the nested

parts that make up the system, the family’s relationships, the environment, and the tasks

performed by the family resulting from the relationships of the parts. The focus of the

ecological theory is the interdependence of family members with the environment and

with one another. According to the ecological framework, the family system is dynamic

and continually in a state of change and adaptation. General systems theory views the

family as a whole rather than separate parts and in addition as part of a larger supra-

system such as the community (Roberts & Feetham, 1982).

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Up until the FFFS was developed, instruments only addressed the relationship

between the family and each individual such as the parent/child or husband/wife.

Feetham identified and measured two additional major areas of family functions as

relationships: the relationship between the family and subsystems (relatives, friends and

neighbors) and the relationship between the family and the broader community (schools

and place of employment).

Wright and Leahey Model of Family Functioning

Other researchers have explored and developed instruments to measure the

concept of family functioning. Wright and Leahey (1994) developed the Calgary Family

Assessment Model based on general systems theory, cybernetics, communication theory

and change theory. The model includes three assessment categories: structural,

developmental and functional. Structural assessment includes who is in the family, the

connections among the members and the family’s context. The developmental assessment

of families includes examining the stage of family development as delineated by Duvall

and fulfillment of the developmental tasks associated with that stage. The family

functional assessment includes “how individuals actually behave in relation to one

another” (Wright & Leahey, 1994, p. 80).

Two basic aspects of family functioning, instrumental and expressive functioning

were identified by Wright and Leahey (1994). Instrumental functioning refers to routine

activities of daily living such as eating, sleeping, changing dressings, and giving

medications. This area of functioning takes on greater significance for parents with a

child who is technology dependent as they would have to alter the manner in which they

take care of instrumental tasks. Expressive functioning includes nine subcategories:

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emotional communication; verbal communication; non-verbal communication; circular

communication; problem-solving; roles; influence; beliefs; alliances/coalitions.

Therefore, while the Calgary Assessment Model takes into account instrumental and

expressive functioning it does not assess family functioning relative to relationships

outside the immediate family.

Structure and Process of Family Functioning

The literature not only describes family functioning in terms of instrumental and

expressive functioning but in terms of structure and process as well. Bauman (2000), in a

review of concepts germane to family nursing, described family function as a

phenomenon that is related to the reciprocity between the family and illness/wellness that

is then linked with family structure and process. A frequent question therefore, related to

family functioning is “what does it (family) do?” meaning the actual activities which

implies a process. The process-based view of family functioning includes a broad range

of behaviors and implies an inter-relationship between the individual and the family and

the individual and the environment. These behaviors have been described as those

activities that are essential to family survival such as socialization, protection,

procreation, education and economic concerns (Roberts & Feetham, 1982).

Lasky et al. (1985) delineate a structural view of family functioning which

includes aspects such as boundary maintenance, authority and dominance. In addition,

they identified eight bipolar dimensions that characterize family relationships using the

structural view of family functioning; individuation vs. enmeshment; mutuality vs.

isolation; flexibility vs. rigidity; stability vs. disorganization; clear communication vs.

distorted communication; role compatibility vs. role conflict; clear vs. unclear or

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distorted perception; clear vs. diffuse or breached generational boundaries (Lasky et al.,

1985). Family functioning can also be viewed as an outcome based on a systems model.

Outcomes related to family functioning could include the care of dependent family

members, satisfaction with relationships, maintenance of family possessions, regulation

of family members’ behaviors and the transmission of family patterns across generations.

An analysis of the literature reveals a wide variety of definitions of family

functioning. Theoretical foundations for the definitions, models, and descriptions of this

concept include general systems theory, structural-functional theory, ecological theory,

change theory, communication theory, cybernetics and the theory of family

developmental stages. Donabedian’s model of structure, process, and outcome is also

implied in many of the definitions and the instruments developed to date that measure the

concept of family functioning.

Cultural Context of Family Functioning

Cultural contexts that influence the conceptual meaning, definition and

consequently the measurement of family functioning are of paramount importance. Each

culture and each family defines what is considered “family” and furthermore prescribes

norms and values on family functioning such as roles, tasks, communication and division

of labor assignments. A family’s definition of family functioning is influenced by the

broader systems in which they are nested such as the country, region, and neighborhood

and by demographic variables such as ethnicity, race, social class, and religion (Wright &

Leahey, 1994). Therefore, what is expected as far as the performance of various functions

and by whom they are to be performed is culturally influenced. In addition, perception is

an integral component of family functioning and is influenced not only by the cultural

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context but by the individual members’ belief system regarding how a family should

function. The individual’s perception and expectations in turn influence the family

member’s level of satisfaction with family functioning. The ultimate goal of these

functions is to keep the family unit in a state of homeostasis thereby allowing growth of

the individual and family as a unit. Furthermore, by maintaining a state of homeostasis,

the health of all the members is promoted.

Family Development and Family Functioning

Differences in the meaning of family functioning also occur related to the stage of

family development as delineated by Duvall. Duvall’s model delineates the

developmental tasks of the family and is based on the premise that family growth occurs

in predictable stages. Instrumental and expressive functioning differs over time related to

the particular stage that the family is experiencing such that roles and division of labor

assignments may go through changes depending on the instrumental functioning demands

(Dickstein, 2002). In addition, historical context has brought about changes in the

meaning of the concept family functioning. Roles for family members related to

instrumental functioning have changed in many families over the past 25 years with more

male partners performing household and child care duties and an increased rate of women

working outside the home.

Comprehensive Definition of Family Functioning

A comprehensive definition of family functioning is an ongoing, dynamic process

that changes with developmental stages and crisis events and is comprised of inter-

relationships and inter-dependent parts. It is influenced by cultural context, members’

expectations and perceptions and has the goal of balancing equilibrium and

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disequilibrium so that individual growth can occur and health is promoted (Toly, 2003).

Additionally, according to Roberts and Feetham (1982), family functioning is the

parent’s perception and level of satisfaction with the relationship between the family and

individual members, the subsystems and the community.

Preterm Infants and Family Functioning

Some studies reviewed examined the relationship between preterm infants and

family functioning. Thompson et al. (1993) looked at processes related to maternal

distress using the Family Environment Scale. At 3-6 weeks and 6 months after discharge,

family supportiveness decreased while conflict increased. McCain (1990) also used the

Family Apgar Scale and the Family Dynamics measure to determine if risk factors related

to preterm birth were associated with family functioning when the preterm child was 2-4

years old. Using multiple regression analysis, the researcher found that longer neonatal

hospitalizations but not child’s developmental status, length of hospital stay, adequacy of

family finances, parental age and marital status continued to be associated with poorer

family functioning related to role reciprocity and conflict for mothers (McCain, 1990).

Maternal Mental Health and Family Functioning

Weiss and Chen (2002) studied the factors influencing maternal mental health and

family functioning in 125 low birth weight infants and their mothers during the infant’s

first year of life. Specific aims of the study were to determine the effect of infant’s

medical vulnerability and psychosocial context of caregiving on the mother’s mental

health during the infant’s first year of life. Additionally, the researchers examined the

effect of the infant’s vulnerability on family functioning during the first 3 months of life.

The sample was diverse with 28% Hispanic, 19% African-American and 7% Asian or

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Native American with a mean gestational age of 32 weeks. A total of 81% lived with

their partner but not necessarily a spouse; 54% were living below the poverty level; 61%

were employed at least part time. Family functioning was measured with The Family

Adaptability and Cohesion Scales, The Family Satisfaction Scale and the Family Crisis

Oriented Personal Evaluation Scales. Maternal mental health was measured using the

DSM-IV and Brief Symptom Inventory. Measures for infant physical health problems

were unstandardized and measured the infant’s symptoms.

Findings include that the infant responsiveness and severity of the child’s health

problems were associated with a mother’s overall mental health functioning (p<.005).

Family functioning aspects (adaptability and cohesion) had the strongest and most

consistent correlations with maternal mental health as well as perceived emotional

support. Interestingly, family cohesion explained 22% of the variance in severity of

maternal mental health symptoms. Severity of the infant’s illness was correlated with

only one aspect of family functioning-family use of internal coping strategies. Infant

responsiveness was significantly related to family cohesion and adaptability (p<.01)

while clarity of cues were less related to family cohesion and adaptability (p<.05) (Weiss

& Chen, 2002). Therefore, severity of the preterm infant’s illness and the infant’s

responsiveness were significantly related to a mother’s mental health status. Furthermore,

family functioning, particularly family cohesion explained a large percentage of the

variance of the mothers mental health symptoms. Interestingly, the infant’s

responsiveness was significantly related to the family cohesion and adaptability, aspects

of family functioning.

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Severity of Illness and Family Functioning

In the research literature, an increased severity of illness has been associated with

poorer family functioning. Ostwald et al. (1993) studied 151 caregivers of frail elderly

and medically fragile children. Findings include that the ability of the caregivers to

continue managing the care of the child was related to the degree of physical impairment.

Those caregivers with children with severe neurological and respiratory problems had the

most difficulty. A total of 25% of the caregivers felt that they could not manage much

longer with the current arrangement (Ostwald et al., 1993).

Unpredictability of Illness and Family Functioning

Dodgson et al., (2000), in a study of 173 mothers and 150 fathers of children 12-

30 months diagnosed with a chronic physical illness within the last year, examined the

relationship between unpredictability of a child’s illness and family functioning using the

Impact on Family Scale. The researchers found that for mothers, unpredictability of

symptoms was significantly related to greater family/social disruption (p=.001),

emotional strain (p=.001) and financial burden. Fathers had similar findings-

unpredictability of symptoms was related to greater family/social disruption (p=.002).

Therefore, unpredictability of a child’s illness significantly correlated with family and

social disruption and was the source of greater emotional strain than for those with

symptoms that were more predictable.

Factors that Promote Family Functioning

Several factors have been found to promote family functioning. Findings from

past research indicate that a decreased severity of illness, greater predictability of the

child’s symptoms, availability of intimate and peer support, greater proficiency in illness

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management, gender of the child (female), and a trusting relationship with a home health

care nurse were related to better family functioning (Dodgson et al., 2000; Kiernan, 1995;

Drotar, 1997; Kohlen et al., 2000; Ostwald et al., 1993).

Burke, Harrison, Kauffmann, and Wong (2001) examined the effect of a family-

focused intervention on family functioning in a longitudinal study. A sample of 115

children ages 1-15 with complex chronic illnesses were randomly assigned to Stress-

Point Intervention by Nurses (SPIN) or usual care. The researchers were examining

whether the SPIN families would have more satisfaction with family functioning and

better parent coping after hospitalization. The Feetham Family Functioning Survey and

Coping Health Inventory of Parents were completed by parents and results revealed that

SPIN parents were significantly more satisfied with family functioning and had better

coping than parents receiving usual care. Only 4% of SPIN parents reported poorer

family functioning versus 38% in usual care group while 31% of SPIN families had better

family functioning compared to only 19% of the usual care group (p<.0001) (Burke, et

al., 2001). Thus, interventions for families of children with chronic conditions related to

family functioning have shown that families were statistically more satisfied with family

functioning following the intervention than those receiving usual care.

Normalization and Family Functioning

In a longitudinal, triangulated study, Knafl and Zoeller (2000) found an

association among family management styles and family functioning using the Feetham

Family Functioning Survey. Families who used the floundering family management style,

the lowest level of normalization, had significantly less satisfaction with family life

(poorer family functioning) than families using the other styles with higher levels of

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normalization (Knafl & Zoeller, 2000). Therefore, research has shown that family

functioning has been associated with normalization.

Family Functioning and Psychosocial Adjustment of the Child

Family functioning was also noted to be associated with psychosocial outcomes in

the chronically ill child. Murphy (1994) found that poor family functioning was related to

poor psychosocial adjustment in the child with a chronic physical disorder (Murphy,

1994). Stanton (1999) concurs with this finding and found in a meta-analysis of literature

related to families of children with traumatic brain injury that family functioning

influences behavioral adjustment and adaptive function after a traumatic brain injury in

children. Most studies found that family functioning accounted for 20% of variance in

outcome of the head injury (Stanton, 1999). Therefore, family functioning has been

associated with psychosocial adjustment in children with chronic conditions.

Drotar et al. (1997) looked specifically at family cohesion, an aspect of family

functioning, in families of children with chronic conditions. According to a meta-analysis

of 57 published studies, supportive family relationships (cohesion) predicted fewer

behavioral symptoms and greater self esteem in children with chronic conditions. In all

but 4 studies, family/parental functioning was significantly related to the child’s

psychological condition. On the contrary, measures of family conflict generally predicted

higher levels of behavioral symptoms and less competent psychological adjustment. Also,

a greater frequency of maternal psychological distress was reported among those who had

children who were considered psychologically maladjusted Drotar et al.,

(1997).Therefore, family cohesion, an important aspect of family functioning was found

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to be significantly related to the chronically ill child’s behavior, self esteem, and

psychological adjustment.

Summary of Family Functioning

In summary, little research has been conducted related to family functioning in

families who have a child who is technology dependent. Family functioning has been

examined extensively in families with children who have chronic illnesses. Main findings

of these studies are that family functioning has a direct effect on the child’s psychological

functioning. Since family functioning is of paramount importance to the management of

the child who is technology dependent this is an area that requires further study.

Furthermore, no quantitative studies have examined the relationship between

normalization and family functioning as described in the Family Management Style

Framework by Knafl and Deatrick (2003; 2006). Research that can examine the

relationships between maternal depressive symptoms, child’s functional status and level

of technology dependence, normalization efforts on family functioning will help nurses to

identify families who are most at risk for family functioning distress so that interventions

can be implemented and the health of all members of the family can be maintained and

growth of all the family members promoted.

Theoretical and Empirical Linkages

Child’s Severity of Illness and Maternal Depressive Symptoms

The concepts in this study are theoretically and empirically linked (Figure 1.2).

The severity of a child’s illness, and in particular, functional status, has been linked to a

parent’s depressive symptoms (Canning, et al., 1996; Lustig et al., 1996; Silver et al.,

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1995; Silver et al., 1998; Frankel & Wamboldt, 1998; Weiss & Chen, 2002). According

to Radloff (1977), there is a relationship between chronic strain and depressive

symptoms: the more negative the event, the higher the depressive symptoms. According

to the cognitive theory of depression, “affect (or emotion) is a subjective state resulting

from the appraisal or evaluation of internal/external stimuli…the emotional experience

occurs within the context of a dynamic interaction between the person and the

environment” (Clark et al., 1999, p. 76). Caring for a chronically ill, technology

dependent child at home could be assessed by some parents as chronic strain and could

therefore lead to elevated levels of depressive symptoms.

Caregiving strain is viewed as an “array of challenges experienced by families as

a consequence of someone’s illness” (Sales, Greeno, Shear, & Anderson, 2004, p. 212).

Biegel, Sales and Schulz (1991), developed a model that posits that caregiving strain is a

mediating variable that is affected by the severity of the ill family member’s care needs

and thus affects the mental health of the caregiver. In sum, this model portrays that the

relationship between the child’s illness severity and the caregiver’s mental health is

mediated by the caregiving strain experienced (Sales et al, 2004).

Many researchers of caregiver strain have examined and found associations

among a child’s behavioral problems, caregiver strain and maternal depression (Sales et

al., 2004). Few, however, have viewed caregiver strain as the link between the child’s

severity of illness and a mother’s depressive symptoms as most have viewed caregiver

strain as an outcome variable rather than a mediator affecting maternal depression (Sales

et al., 2004). In a study of 222 mothers of children with behavioral problems, mothers

with more severely affected children experienced more caregiving strain. Additionally,

the authors noted a significant relationship between caregiving strain and increased

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depression using the Beck Depression Inventory, and the SF-36 Mental Health subscales

(Sales et al., 2004). In conclusion, Sales et al. (2004) found that caregiver strain was the

strongest predictor of maternal depression and the strongest predictor of caregiver strain

was perceived severity of the child’s problems.

Leonard and colleagues (1993) found that 59% of mothers and 67% of fathers

with children who were technology dependent had psychological distress symptoms at a

level indicating a need for intervention. Gennaro, Brooten, Roncoli, and Kumar (1993)

found a significant relationship between preterm infant morbidity and maternal level of

depression postpartum. Therefore, previous studies have found that severity of a child’s

illness is related to a higher level of parents’ depressive symptoms.

Severity of Illness and Normalization

Increased severity of illness has also been linked to decreased normalization

efforts. Components of the illness that have been found to interfere with efforts toward

normalization include illness control, uncertainty or unpredictability of the illness and

visibility of the condition to others. Dashiff (1993) found that poor illness control of

diabetic adolescents hindered efforts at normalization. Chronic uncertainty of the illness

and unpredictability of symptoms of an illness (Cohen, 1993; Haase & Rostad, 1994;

O’Brien, 2001) and visibility of the condition (Murphy, 1994) have also has been found

to hinder normalization efforts.

Severity of illness was also noted to be a major factor related to change in

normalization efforts in a longitudinal study conducted with families of children with

chronic conditions (Knafl & Deatrick, 2002). In families who experienced this change,

the level of difficulty in incorporating the illness into family life was linked to a change

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in the disease process. With exacerbations and flare-ups of the disease, there was

dissolution of normalization even in parents who described themselves as competent and

confident caregivers and managers of the illness (Knafl & Deatrick, 2002). The illness

exacerbation made the illness management burdensome and the focus of family life; as in

Paterson’s Model of Shifting Perspectives, brought the disease to the foreground.

Conversely, as the illness improved or became better controlled, parents shifted to

normalization in their family life and increased their normalization efforts. While the

illness was in remission or was well controlled, the illness management was taken for

granted and became part of the family routine thus normalizing efforts were increased

(Knafl & Deatrick, 2002).

Morse and colleagues (2000) also linked severity of illness to normalization

efforts however they conceptualized normalization as including the process of

identification to fit into a desired reference group. The extent to which the child was able

to normalize and function in these two reference groups; “everyday as normal” and

“disabled as normal” is related to various factors including the degree of disability

(Morse et al., 2000).

Researchers who studied families with children who were technology dependent

also noted a link between increased severity of illness and difficulty with normalization

efforts. In a study of 12 families of ventilator dependent children, Carnevale and

colleagues (2006) found that normalization efforts were undermined by the

unpredictability of the child’s condition that was complicated and at times overwhelming.

All of these families however devoted significant effort toward normalizing the

experience; creating routines so that their lives would resemble that of “normal” families.

Lee (1996) found that mothers reported increased normalization efforts with decreased

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dependency on technology i.e. less dependent on oxygen because the infant was seen as

less sick and more normal. Therefore, the literature describes an inverse relationship

between severity of illness and normalization efforts.

Rehm and Bradley (2005) examined normalization in 26 families with children

who were technology dependent and concur with the findings of an inverse relationship

with severity of illness and normalization efforts. Furthermore, the researchers found that

the child’s developmental delays compounded the effects of the child’s physical chronic

condition thus affecting how the family organized and managed their daily lives. The

researchers conducted a comparison of these families with the attributes of normalization

and concluded that these families did not fit the established attributes of normalization.

Families reported that it was possible to have a good life but that it was not “normal” by

the usual standard. Most notable was the finding that the “normalcy lens” was

impossible. Because the child’s condition required constant vigilance and frequent skilled

intervention, parents felt that they needed to continuously prioritize the care of the child

and not place the child’s condition in the background. Due to the child’s fragile

condition, families reported that they were unable to modify the treatments or develop a

predictable routine. Families arranged their daily life around the care of the child and

strove for stability for all family members but did not see life as “normal”. Due to the

visibility of the equipment and appearance of the child, parents felt conspicuous in public

and frequently experienced distress due to discrimination. The researchers concluded that

feeling and acting normal is not an all or nothing phenomenon but rather emphasized

aspects of typical family life like activities and traditions that all family members could

enjoy together (Rehm & Bradley, 2005).

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Families of children who are technology dependent experienced many limits in

their routines and activities related to the child’s chronic condition. The equipment, time

required for treatments and care, the need for trained caregivers all affected efforts

normalization and the child’s ability to participate in normal childhood and family

experiences and socialization with peers (O’Brien & Wegner, 2002). The reliance on

technology also affected daily activities due to the time and planning required even for a

simple outing.

Severity of Illness and Family Functioning

The severity of a child’s illness can also be linked to family functioning. Ostwald

et al. (1993) in a study of 151 caregivers of frail elderly and medically fragile children

found that the ability to continuing to managing the care of the child was related to the

degree of the child’s physical impairment. A total of 25% of the caregivers said they felt

they could not continue to manage with the current arrangement. Those families that had

the most difficulty with family functioning were those families of children with severe

respiratory and neurological problems (Ostwald et al., 1993). Thus, the degree of the

child’s physical impairment is linked to family functioning.

Kiernan (1995) found that severity of diagnosis was inversely related to family

functioning using the Family APGAR in families of children dependent on intravenous

infusions receiving home health care. The researcher hypothesized that a there was a

stronger sense of family functioning with the perception that the diagnosis was less

severe.

The predictability of symptoms of an illness adds to the perceived severity of

illness that can thus affect family functioning. O’Brien (2001) found that families with

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children who are technology dependent and receiving long term home health care

identified uncertainty and unpredictability in their lives as “living in a house of cards”.

Balance was therefore required in order to manage daily life with technology while at the

same time maintaining a functioning family. Donnelly (1994), Dodgson et al (2000), Kirk

(1998), Torok (2001), Case-Smith (2004) and Cavanagh (1999) concur with these

findings that the unpredictable nature of chronic disease and the demanding treatment

protocols are variables that negatively affect family functioning. In fact, families learn to

anticipate the unanticipated. Dodgson et al. (2000) found that mothers of children with

intermittently unpredictable symptoms reported significantly more disruption in family

functioning (p=.001). Therefore, severity of the child’s illness, particularly the

unpredictable nature of the illness, had a negative effect on family functioning.

Establishing family routines is one of the components of family functioning that is

affected by a child’s severity of illness. O’Brien (2001) found that families established

the environment and routines for themselves that incorporated and accommodated the

complex aspects of life with a child who was technology dependent. Managing the family

life with technology requires skill, organization and creativity. There were frequent

disruptions in the child and family routines due to the child’s care demands, however,

families incorporated the demands into family life and were not subsumed by them

(O’Brien, 2001). Incorporating illness care into family routines, therefore, promoted

family functioning.

Another component of family functioning that can be affected by the child’s

severity of illness was related to group activities for the family and relationships with

family and friends. O’Brien (2001) found that families of children dependent on

technology were able to meet the physical and health needs of the family however

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enjoying activities together as a group was difficult and frustrating. Families also found

maintaining relationships with family and friends difficult (Allen et al., 1994; Case-

Smith, 2004; Cavanagh, 1999; Neuss, 2004).

The child’s severity of illness can also affect the relationship between the parents

of the child who is technology dependent; an important relationship in family

functioning. Marital strain has been noted due to the high care demands of these children

and the resultant lifestyle change (Hatton et al., 1995). The literature reports conflicting

information regarding marital breakups related to care of the child who is technology

dependent. O’Brien (2001) reports that 3 of 15 couples included in her study divorced

due to care of the child who was technology dependent however Neuss (2004) reported

no break up in marriages. Neuss (2004) hypothesized that this was due to their

compatibility in definitions of parenting roles and shared goals for the child.

Family Functioning and Maternal Depressive Symptoms

Family functioning is linked with a mother’s depressive symptoms. When the

major caregiver, typically the mother, is emotionally distressed, general family

functioning is affected and poor family functioning has been shown to affect the mother’s

psychological functioning (Kuster et al., 2002; Ireys & Silver, 1996; Hock-Long, 1997;

Weiss & Chen, 2002). How the child’s illness impacts the family is determined by the

emotional health of the main caregiver parent but the emotional health of the parent is

affected by family functioning and the child’s severity of illness (Frankel & Wamboldt,

1998).

Severity of the child’s illness and increased depressive symptoms of the mother

affected family functioning (Weiss & Chen, 2002). Ireys and Silver (1996) found that the

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mother’s perception of family functioning due to the child’s illness was related to the

mother’s mental health even after controlling for other variables. Additionally, family

functioning accounted for a large percentage of variance on mental health measure

(p=<.001) (Ireys & Silver, 1996). Hock-Long (1997) found that family functioning was

significantly related to psychological functioning (p=<.001). Caregivers with high

discrepancy scores on the Feetham Family Functioning Scale had higher levels of anger,

confusion, depression, fatigue and low vigor levels on the Profile of Mood Scale (Hock-

Long, 1997). Therefore, the literature supports a relationship between depression and

family functioning.

Family Functioning and Normalization

Family functioning is also noted to be linked with normalization. Knafl and

Zoeller (2000) found that parents who matched on themes related to normalization, thus

sharing the same viewpoint related to normalization, had better family functioning. This

shared view of the illness, illness management and its impact on family life appeared to

downplay the impact of the child’s illness on the family. The researcher hypothesized that

when the shared view is positive regarding normalization efforts; families identify illness

management strategies that help with family functioning.

Family functioning is also linked with normalization related to flexibility of

treatment approaches. Normalization includes a flexible approach to carrying out

treatments so that illness care is incorporated into family routines (Gallo & Knafl, 1998;

Knafl & Deatrick, 2002; Knafl & Gilliss, 2002; Thorne et al., 1997; Wilson et al., 1998).

Additionally, care for the child who is technology dependent, if fit around other family

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activities will facilitate family functioning. Families learned “tricks of the trade” to

facilitate this process of flexibility (Gallo & Knafl, 1998) and routinization of the

treatment regimen (Knafl & Gilliss, 2002). Rules were thus broken to deliver safe care

but would “normalize” childhood by giving experiences similar to other children while at

the same time maintaining family functioning (Wilson & Morse, 1998). Those who did

not normalize were noted to have difficulties related to strictly adhering to the treatment

regimen, saw the treatment regimen as the focus of family life and a burden that made

their family different from other families (Knafl & Deatrick, 2002). Therefore, family

management behaviors to accommodate a child’s chronic condition that incorporated

flexibility in adherence to medical regimen, assisted families in normalization efforts and

consequently led to improved family functioning.

Maternal Depressive Symptoms and Family Functioning

A mother’s depressive symptoms can then be linked to family functioning.

According to Clark et al. (1999), depression, and even sub-clinical depressive symptoms,

is associated with significant impairment in psychosocial and physical functioning and

well being, which could therefore affect family functioning. To date, this have not been

specifically addressed in the literature. Knafl and Zoeller (2000) did measure both family

functioning using the Feetham Family Functioning Survey (FFFS) and psychological

functioning using the Profile of Mood States in a triangulated study of parents of children

with chronic conditions. However, the researchers were reporting on the comparison

between mothers’ and fathers’ experiences and did not report the correlation between

family functioning and psychological functioning. Additionally, there was no significant

difference between scores for mothers and fathers on their overall discrepant or subscale

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scores on the FFFS. Other findings include that the FFFS scores were similar to other

studies of family functioning in families with health care challenges (Knafl & Zoeller,

2000). Futhermore, mood disturbance was similar or lower than the population used to

develop the Profile of Mood States instrument. Mothers had more mood disturbance on

the confusion and fatigue subscales. The researchers attributed the findings to the

emotional costs incurred by mothers who are the parent primarily responsible for

managing the care and treatment of the child (Knafl & Zoeller, 2000).

Maternal Depressive Symptoms and Normalization

Research regarding caregivers found situational depression to be one of the most

frequent reactions to long term caregiving for adults who were technology dependent

(Smith, 1999). Some of the depressive symptoms such as sleep disturbances or inability

to concentrate can affect performance of complex technological procedures (Smith,

1999). Furthermore, definition of the situation, one of the major components of

normalization, can be influenced by the level of depression (Knafl & Deatrick, 2003).

Definition of the situation according to the Family Management Style Model is the

subjective meaning family members attribute to the child’s identity (normal versus

vulnerable), illness view (life goes on versus serious/hateful), parent mutuality (shared

versus different views of the illness and illness management), management mindset (ease

or difficulty with treatment regimens) (Deatrick et al., 2006). According to Frankel and

Wamboldt (1998), families are unable to effectively manage the illness and the quality of

life for families is diminished when the primary caretaker is emotionally distressed.

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Summary of Conceptual Linkages

In conclusion, according to the theoretical and empirical literature reviewed, the

proposed study variables are linked. The purpose of this study is to explore how mothers

respond to and manage the special challenges of children who are technology dependent

after they are discharged from the hospital to home. This descriptive, correlational study

will explore the relationships of child’s severity of illness (functional status, technology

dependence), mother’s depressive symptoms (level of depressive symptoms), efforts at

normalization and family functioning of families with children who are technology

dependent. No quantitative research has been done examining efforts at normalization

and level of family functioning as reported by mothers with children who are technology

dependent. More research is needed to examine the relationships among child’s severity

of illness, mother’s depressive symptoms, normalization and the resultant impact on

family functioning in mothers of children who are technology dependent after adjusting

for caregiving duration, amount of home health care nursing hours, race, family income,

and age of the child who is technology dependent.

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CHAPTER THREE - METHODS

Introduction

The purpose of this study was to explore how parents respond to and manage the

special challenges of a child who is technology dependent after they are discharged from

the hospital to home. This descriptive, correlational study explored the relationship

between child/maternal factors (child’s functional status, level of technology dependence,

mother’s depressive symptoms, length of caregiving duration, amount of home health

care nursing hours, race, family income and age of the child) and (a) family functioning

as well as (b) normalization in families with a child who is technology dependent.

Covariates for this study include length of caregiving duration, amount of home health

care nursing hours, race, family income and age of the child.

This study also examined whether there are differences in family functioning,

normalization and depressive symptoms based upon the child’s level of technology

dependence (Group 1 mechanical ventilation, Group 2 intravenous nutrition/medication,

Group 3 respiratory/nutritional support) using the Office of Technology Assessment

(1987) rubric. Additionally, mediating effects of normalization on family functioning as

well as mediating effects of mother’s depressive symptoms on normalization and family

functioning will be examined.

The Family Management Style Framework (Knafl & Deatrick, 2003) and the

Shifting Perspectives Model of Chronic Illness (Paterson, 2001) was the orienting

framework providing the theoretical foundation for this study. This chapter will describe

the methodology used in this study including the study design, sampling methods,

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procedure for data collection, instruments, data management, data analysis, research

questions, limitations and protection of human rights.

Study Design

A descriptive, correlational study design is proposed. Data were collected a

minimum of two months after the child who is technology dependent was discharged

from the hospital and cared for at home. Data were be collected related to the family

demographics, child’s severity of illness, mother’s depressive symptoms, caregiving

duration, number of hours of assistance the child receives from home health care nurses,

age of the child who is dependent on technology, type of technology, normalization and

family functioning using quantitative instruments. By two months after discharge,

families will have had time to settle into a routine and experiment with ways to manage

the chronically ill, technology dependent child and develop management behaviors (Bull,

1992; Clements et al., 1990).

Few research studies have been conducted regarding the child who is technology

dependent and family functioning (Miles et al., 1999; Hock-Long, 1997). Researchers

looked at the level of depressive symptoms experienced by parents (Fleming et al., 1994;

Miles et al., 1999; Teague et al., 1993; Heyman et al., 2004; Kuster, 2002), impact on the

family with regard to the four groups of technology assistance (Office of Technology

Assessment, 1987) (Fleming et al., 1994; Leonard et al., 1993) and severity of the child’s

illness (Silver et al., 1995; Silver et al., 1998; Frankel & Wambolt, 1998; Lustig et al.,

1996; Weiss & Chen, 2002). Most studies used functional status instruments that were

not psychometrically tested or those developed for adults to measure a child’s health

status (Leonard et al., 1993; Patterson et al., 1992; Teague et al., 1993; Miles et al.,

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1999). Two studies examining the impact of caring for a child who is technology

dependent on parents have used a standardized instrument to measure functional severity

(Kuster et al., 2004; Heyman et al., 2004). The vast majority of studies of families of

children who are technology dependent, however, used qualitative designs (Allen et al.,

1994; Carnevale et al., 2006; Cavanaugh, 1999; Cohen, 1999; Heaton et al., 2005;

Judson, 2004; Kirk, 1999; Kirk & Glendinning, 2004; Kirk et al., 2005; Lee, 1996;

Murphy, 1997; Neuss, 2004; O’Brien, 2001; O’Brien & Wegner, 2002; Rehm & Bradley,

2005; Torok, 2001; Wilson et al., 1998). Furthermore, no quantitative research has been

done examining the use of normalization and level of family functioning as reported by

mothers with children who are technology dependent. This study will use quantitative

methodology to address this gap.

Sampling

Criteria for Selection

The subjects for this study will be mothers who are caring for a child who is

dependent on technology at home. Inclusion criteria are mothers who are: 1) 18 years of

age and older; 2) caring for a child who is technology dependent (OTA, 1987 Groups 1-

3), with a stable course of illness, 16 years of age or younger; 3) caring for the child who

is technology dependent at home for a minimum of 2 months prior to participation in the

study; and 4) able to speak and understand English. Exclusion criteria include mothers

whose child has the diagnosis of cancer or is in the terminal stage of illness.

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Recruitment of Subjects

The convenience sample of mothers with a child who is technology dependent

was obtained from Rainbow Babies and Children’s Hospital Pediatric Out-patient

Departments (Pulmonlogy, Gastroenterology, Otolaryngology, Surgery) as well as the

Preterm Infant Follow-up Program and the Family Learning Center. Convenience

sampling was chosen because it is time efficient, inexpensive, and more readily

accessible than random sampling.

The investigator will contact the Rainbow Babies and Children’s Hospital

Nursing Administration and the Chief Attending Physicians for the Pediatric

Pulmonology, Gastroenterology, Surgery and Otolaryngology Outpatient Departments as

well as the Preterm Infant Follow-up Program to explain the purpose of the study,

information related to the inclusion and exclusion criteria as well as telephone numbers to

contact the researcher. Approval for conducting this study will be obtained from the

Institutional Review Boards (IRB) of University Hospitals of Cleveland/Case Medical

Center.

Potential participants will be contacted in one of two ways. First, the Outpatient

Clinic Physician will ask if the mother would agree to be contacted regarding

participation in a research study of mothers of children with special technology needs.

Upon agreement from the mother, the Physician will notify the researcher. The researcher

will then approach the parents to give an overview of the study, assess for eligibility and

obtain informed consent. The second method of contact, as approved by the IRB, is to

have attending physicians identify potential participants so that a letter could be sent to

the mother introducing the study and inviting her to participate (Appendix G). The letter

will give a telephone number to contact the researcher. If the mother does not contact the

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researcher within 2 weeks the researcher will call the mother (Appendix H) to ask if she

is willing to volunteer; assuring her that her child’s care will not be affected in any way

by her decision to participate. The researcher will assess for eligibility and set up a

mutually agreeable time and a place for the interview. Following both methods of

recruitment participants will be assigned an identification number.

The amount of time for each interview will be noted and the range reported in the

dissertation. It is estimated that the total amount of time to answer all questions is 45-50

minutes. The time for each participant to complete the packet of instruments will be

recorded and noted. Statistics for response rate such as number of potential participants

approached, number who agreed or refused as well as reasons for refusal will be noted.

Sample Size

Statistical power analysis was conducted to determine sample size using the other

factors involved in statistical inference: significance criterion (alpha), population effect

size (estimated size of the relationship of the independent variables to the dependent

variables), and statistical power (Cohen, 1992). The number of variables and type of

statistical tests to be conducted were considered in the sample size calculations as well.

Power analysis minimizes the risk of Type II error.

The significance criterion (alpha) indicates the risk of committing a Type I error

or the rejection of the null hypothesis when in fact it is true (Burns & Grove, 1997;

Cohen, 1992). The alpha was set at .05 in this study because it is most commonly used in

the behavioral sciences as a minimum value at which to reject a null hypothesis (Cohen,

1992).

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The effect size is “the degree to which the phenomenon is present in the

population or to which the null hypothesis is false” (Burns & Grove, 1997, p. 780).

Phenomenon exists in a matter of degree, thus, the effect size can be estimated as small,

medium, or large. Cohen has established numerical values for the various estimated effect

sizes based on specific statistical procedures (1992). Effect size, estimates the size of the

relationship of the independent variables to the dependent variables and can be based on

previous research. If no such research has been conducted, a conservative value such as a

medium effect size can be used (Cohen, 1988). For this study, a medium effect size is

proposed because there has been no similar research conducted. A medium effect size of

.25 on the F test for MANOVA and .15 on the F test for Multiple Regression will be used

for this study (Cohen, 1992). According to the guidelines for power analysis set forth by

Cohen (1992), a sufficient sample size for this study was calculated to be 112. The

calculations used by Cohen (1988) to develop the table for sample size are: N=

[L/ ]+k+1. Convenience sampling techniques will be used to draw a sample of 112

mothers to achieve a power of .80 with 9 predictors (4 independent variables, 5

covariables), alpha ( ) of .05 and an effect size of .15 on the F test for Multiple

Regression (Cohen, 1992).

In order to compensate for any missing data, an additional 3% was added to the

total number of subjects to be recruited. A total of 115 participants will be needed to

examine 9 predictors including independent variables child’s severity of illness

(functional severity, level of technology dependence), mother’s depressive symptoms

(level of depressive symptoms), normalization efforts and covariables (amount of home

health care nursing hours, caregiving duration, race, family income and age of the child

who is dependent on technology) with the dependent variable, family functioning

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(satisfaction with relationships) of families with children who are technology dependent

using a 5% risk of a Type I error and 20% risk of a Type II error. Normalization efforts

and family functioning will alternate between independent/dependent variable depending

on the research question being analyzed.

Procedure for Data Collection

Following approval from the Institutional Review Boards at University Hospitals

of Cleveland, participants were identified and given an overview of the study including

study purpose, data collection procedures, provisions for confidentiality and their ability

to drop out of the study at any time. All information will be given to participants in both

verbal and written form. After it has been ascertained that potential participants have met

the inclusion criteria, the researcher will provide and explain the informed consent and

HIPPA forms (Appendix J & K). After the informed consent form has been signed, the

investigator will administer the questionnaires during a face-to-face interview and answer

any questions. Face to face interviews decrease the chances of missing data. Interviews

will take place in a quiet setting that is agreeable with both participant and researcher

such as the participant’s home, clinic conference room or public library.

Instruments

Five instruments and a set of demographic questions will be organized into a

packet with special attention to order of sequence; less intrusive questions will be

administered first. Data will be collected by the researcher or research assistant using a

face-to-face structured interview technique. The first measure in the packet is the

Functional Status II-R (FS II-R) (Stein & Jessop, 1990) (Appendix A) that assesses the

functional health status of children with chronic physical disorders over time. Next, the

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Level of Technology Dependency Questionnaire will measure the number and type(s) of

technology used by the child (Appendix B) followed by the Center for Epidemiological

Studies Depression Scale (CES-D) (Radloff, 1977) (Appendix C) to measure the

mother’s depressive symptoms. Normalization efforts will be measured using the “Actual

Effect of a Chronic Physical Disorder on Family” subscale of the Normalization Scale

(Murphy & Gottlieb, 1992) (Appendix D) followed by the outcome measurement of

family functioning using the Feetham Family Functioning Survey (Roberts & Feetham,

1982) (Appendix E). Table 3.1 displays the reliability coefficients for each of the

instruments for the current and previous studies. Last, the Demographic Questionnaire

(Appendix F) that includes number of home health care nursing hours, caregiving

duration, race, family income and age of the child who is dependent on technology will

be administered. A chart will then be completed to ascertain the child’s medical

diagnoses.

Table 3.1. Empirical Indicators and Reliability Coefficient Variable Instruments Cronbach’s alpha

Current Study Cronbach’s alpha Previous studies

Functional Status Functional Status II – Revised

.76 .84 – .86

Depressive Symptoms

Center for Epidemiological Studies – Depression (CES – D)

.92 .85 – .90

Normalization Normalization Scale

“Actual Effect of Chronic Physical Disorder on Family” Subscale

.83 .84

Family Functioning

Feetham Family Functioning Survey

.87 .85 – .88

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Functional Status

The child’s functional status will be measured using the Functional Status II-R

(FS II-R) instrument that assesses the functional health status of children with chronic

physical disorders (Stein & Jessop, 1990). The 14-item instrument uses common items

across all ages (newborn to 16 years) and requires a parent’s report regarding their child.

The FS II-R can be used with children ages newborn to 16 years of age and is comprised

of two parts to assess for dysfunction due to illness. Part 1 asks questions related to

performance of the activity or behavior over the past two weeks. Examples of questions

included on this instrument are as follows: “thinking about (child’s name), during the last

two weeks did (he/she) (a) eat well, (b) sleep well, (c) seem lively and energetic”. A

response card is given to the mother and the response may be “never or rarely” (coded as

0), “some of the time” (coded as 1), or “almost always” (coded as 2). Part 2 is

administered after the completion of all items in Part 1 and probes those items that

received a starred response on the instrument, indicating poor functioning, to determine

whether this was due “fully”(coded as 2), “partly” (coded as 1), or “not at all”(coded as

0) to a health problem (Stein & Jessop, 1990). This procedure is performed to minimize a

response set. Any items that are probed in Part 2, that the parent indicates are “not at all”

related to illness, should be recoded to the closest non-starred response on Part 1 (Stein &

Jessop, 1990). Items that require recoding so that a high score indicates “good” function

include items FS4, FS6, FS9, FS12 and FS14 such that (0=2) and (2=0).

The FS II-R assesses behavioral manifestations of illness that interfere with the

child’s performance of age-appropriate activities (Stein & Jessop, 1990). According to

the developers of this tool, behavior includes age appropriate physical, psychological,

intellectual and social behaviors. The items on this tool cuts across diseases and lists

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observable behaviors that reflect disturbances in the child’s function (Stein & Jessop,

1990).

The FS II-R score, an interval measure, is summed and converted to a percent of

the total 28 points possible with higher scores indicating higher function. This revised

instrument was tested with 456 children with a chronic illness and 276 well children for a

total of 732 children, ages 2 weeks to 16 years, with socioeconomic and racial diversity.

The psychometric data indicates that 12.9% of children with a chronic illness would score

below one standard deviation of the mean indicating severe dysfunction. The mean

percent of the total possible score for all ages was 86.8% SD+/- 15.7 for chronically ill

children and 96.1% SD+/-8.2 for well children (Stein & Jessop, 1990). The instrument

showed more variation in children with chronic illness and displayed discriminate

validity in that the mean for well children was higher than that of children with chronic

conditions. Included in the sample were children with significant chronic illness, as well

as children with and without ongoing health conditions. The Cronbach alpha, the measure

of internal consistency or reliability, for the short 14-item form in all ages was.86 for

children with chronic conditions and .87 for well children. The concurrent validity was

established using assessments made by physicians and measures of morbidity such as

school days missed and number of hospital days.

Few studies examining the impact of caring for a child who is technology

dependent on parents have used the FS II-R, a standardized instrument, to measure

functional severity (Kuster et al., 2004; Heyman et al., 2004). The Cronbach alpha for the

FS II-R in a study of ventilator dependent children conducted by Kuster et al. (2004) was

.84 for all ages. Heyman et al. (2004) did not report the Cronbach alpha for the FS II-R

for their study. Most studies have used unstandardized measures with no reported

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reliability, or those developed for adults (Leonard et al., 1993; Patterson et al., 1992;

Teague et al., 1993; Miles et al., 1999). The Functional Status II-Revised (FSII-R) will

be used in this study because it was specifically developed to assess the functional health

status of children with chronic illness. Therefore, use of a standardized instrument to

measure functional severity will strengthen the findings regarding relationships found

between functional status, depression, normalization and family functioning.

Level of Technology Dependency

The level of technology dependency will be measured by asking parents to list all

medically necessary assistive equipment that the child currently uses. Types of

technology necessary for the care of the child who is technology dependent will be

assessed to determine group assignment for level of technology dependence according to

the Office of Technology Assessment (OTA) (1987). Most quantitative studies of

children who are dependent on technology used the OTA (1987) rubric to define four

groups of technology dependence based on the types of technological equipment used

(Fleming et al., 1994; Leonard et al., 1993). Miles et al. (1999) however, developed a tool

to measure the acuity level for children who are dependent on technology to expand the

rubric for groups established by the Office of Technology Assessment (1987). The tool

developed by Miles et al. (1999) however, does not include any information regarding

reliability and validity and has as its goal determination of acuity rather than the level of

technology dependency so will not be used in this study.

Technology will be dummy coded and includes a) nasogastric tube feeding 0=no

and 1=yes, b) gastrostomy tube feeding 0=no and 1=yes, c) intermittent intravenous

infusion 0=no and 1=yes, d) continuous intravenous infusion 0=no and 1=yes, e)

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intravenous infusion of total parenteral nutrition 0=no and 1=yes, f) oxygen via nasal

cannula 0=no and 1=yes, g) oxygen via trach collar 0=no and 1=yes, h) oxygen via

continuous positive airway pressure (CPAP) 0=no and 1=yes, i) continuous positive

airway pressure (CPAP) without oxygen 0=no and 1=yes, j) tracheostomy tube 0=no and

1= yes, k) mechanical ventilator 0=no and 1=yes. Data will be collected regarding

technology for description purposes and to look at correlations and multiple analysis of

variance of the level of technology with depression, normalization and family

functioning.

Level of Technology Dependency will be categorized according to the groups

established by the Office of Technology Assistance (OTA, 1987) Group 1 as mechanical

ventilators (coded as 1), Group 2 intravenous total parenteral nutrition (coded as 2),

Group 3 respiratory or nutritional support equipment that could include such devices as

oxygen by nasal cannula or continuous positive airway pressure (CPAP), or CPAP

without oxygen, tracheostomy tubes, or gastrostomy tubes (coded as 3). For purposes of

the Multiple Regression analysis, level of technology dependency (Groups 1-3) will be

dummy coded as follows: variable x1) 1=Group 1 and 0=not Group 1, variable x2) 1=

Group 2 0=not Group 2. Group 3 then would be coded as zero for both variables.

Reliability will be based on accurate account of parents completing the questionnaire

regarding the types of technology the child uses. After completing the questionnaire

regarding the types of technology in use, parents will be asked if there are any other types

of technology that their child currently uses. Validity was based on review of the list by

the Pediatric Pulmonology Attending Physician who works with this population of

patients in the Technology Dependent Care Clinic.

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Level of Depressive Symptoms

The mother’s level of depressive symptoms will be measured by The Center for

Epidemiologic Studies Depression Scale (CES-D) that was designed for use in general

population surveys to examine relationships between depressive symptomatology and

other variables across population subgroups. The CES-D is sensitive to possible

depressive reactions in response to life events and discriminated significantly between

clinical psychiatric inpatients and general populations (Radloff, 1977). This 20-item self-

report scale asks how often during the past week a depressive symptom was experienced.

Scores on this scale range from 0 to 60 with higher scores indicating a greater level of

depressive symptoms as an indicator of mental health.

Depressive symptoms are an indicator of mental well-being and tap one indicator

that is inversely related to positive mental health. Examples of some of the questions on

this instrument include: “during the past week (a) I was bothered by things that usually

don’t bother me, (b) I did not feel like eating; my appetite was poor, (c) I had trouble

keeping my mind on what I was doing. Items are rated on a 4-point likert scale ranging

from a score of 0 (rarely or none of the time; less than 1 day), 1(some or a little of the

time; 1-2 days), 2 (occasionally or a moderate amount of time; 3-4 days), to 3 (most or all

of the time; 5-7 days). CES-D items 4, 8, 12 and 16 are phrased in a positive way to

eliminate the possibility of a response set, therefore, reverse coding must be conducted on

those items such that a score of 0=3, 1=2, 2=1, 3=0. A cutoff point of 16 is commonly

used to indicate a clinically significant level of depressive symptoms as a means to

identify high-risk groups however is not intended for use as diagnostic tool for depression

(Radloff, 1977). The CES-D was able to discriminate between populations with a total of

70% of the inpatients but only 21% of the general population scored at or above 16 on

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this instrument. The scale has been found to have high internal consistency with a

coefficient alpha of .85 in the general population and .90 with a population of adults

admitted to an inpatient psychiatric facility (Radloff, 1977). Validity was established by

conducting correlations with clinical depression ratings conducted by clinicians

(concurrent validity) and other self-report measures (Radloff, 1977). The scale was tested

on a probability sample of over 2,500 people with a wide range of demographic

characteristics (Radloff, 1977).

The CES-D has been used as a measure of depression symptoms in past studies of

caregivers who have children who are technology dependent. Heyman et al. (2004) found

that there was no significant difference in CES-D scores between caregivers of children

with a gastrostomy tube and those with a chronic illness without a gastrostomy tube.

Mean CES-D scores were 13.6 with SEM of 1.25 and 14.3 with SEM of 1.28 for

caregivers of children with and without gastrostomy tubes, respectively. Both groups,

however, had higher scores on the CES-D than those with healthy children (13.9 versus

10.0). The Cronbach alpha of the CES-D for the study was .90 (Heyman et al., 2004).

Miles et al. (1999) also used the CES-D to measure the level of depressive

symptoms in mothers of infants who were medically fragile and technology dependent.

The Cronbach alpha for this study was .90 at hospital discharge and .88 at 12 months.

The mean CES-D score was 15.4 (SD=12, range 0-52) at hospital discharge and 13.6

(SD=10, range 0-41) at 12 months (Miles et al., 1999).

Smith (1999) also examined level of depressive symptoms using the CES-D in

adult caregivers of adults who were technology dependent. The mean score on the CES-D

was 16.12 with a range of 14-33. The Cronbach alpha for this instrument was .86 for this

study (Smith, 1999).

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In conclusion, the CES-D has been widely used in studies of caregivers of family

members who are technology dependent and has been found to have Cronbach alpha

levels ranging from .85 to .90. This instrument has been shown to be a valid and reliable

instrument to measure the level of depressive symptoms in caregivers of family members

who are technology dependent.

Normalization

Normalization efforts will be measured using the “Actual Effect of a Chronic

Physical Disorder on Family” subscale of the Normalization Scale (Murphy & Gottlieb,

1992). The 25 items of the Normalization Scale were derived from work by Knafl and

Deatrick on normalization attributes and the Family Management Style Framework. The

“Actual Effect of a Chronic Physical Disorder on Family” subscale consists of 10 items

that is the portion of the instrument that deals with the effect of having a chronic physical

disorder on the family. Items included questions regarding impact of the chronic physical

disorder on family, couple’s and sibling’s activities, differences for family and child if

child did not have a chronic physical disorder, hassles related to the chronic physical

disorder, other’s inclusion of the family in activities, and degree that others treated family

like other families (Murphy, 1994). Examples of questions from this subscale are as

follows: (a) If your child did not have this chronic condition, how different would your

family be compared to what it is like now? (b) How much of your family’s daily

activities have to be planned around your child’s needs? (c) How much of a hassle does

your child’s medical treatment create for your family’s routine?

The Normalization Scale uses a visual analog scale. This format was used for this

instrument because it has been found to be very sensitive, found to reduce bias and has

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been shown to be helpful in measuring subjective experiences (Murphy, 1994).

Participants are asked to mark a slash on a 100 mm line between two extremes-a lot and a

little; with a right angle stop at either end. The slash’s distance is measured in

centimeters along the line and equals the score given with a theoretical range from 0-10.

Distances are rounded to the nearest millimeter with higher scores indicating greater

normalization efforts.

Content validity was established using three mothers of children with chronic

physical disorders and 10 expert nurses with clinical and research experience. The

Normalization Scale was then pilot tested with 10 mothers of diabetic children. Construct

validity of the scale was assessed using principal components analysis with varimax

rotation to identify scale’s statistical structure. Factors were accepted if they had an

eigenvalue greater than 1.0 with at least two items that loaded >.60 on it and were

clinically meaningful. The factor “Actual Effect of the Chronic Physical Disorder on the

Family”, the subscale that will be used in this study, had an Eigenvalue of 7.0, the highest

of all the factors. Factor loadings for this subscale ranged from 0.48 to .86. Internal

consistency was tested using Cronbach’s alpha on each of the subscales with the

following results: “Actual Effect of the Chronic Physical Disorder on the Family” (10

items) .84; “Perception by the Family and Others of the Chronic Physical Disorder Child

and Family” (7 items) .91; “Comparison of Chronic Physical Disorder Child and Family

to Other Chronic Physical Disorder Children and Families” (4 items) .33;

“Encouragement of Normal Activities” (4 items) .65. The “Actual Effect of the Chronic

Physical Disorder on the Family” subscale to be used in the proposed study had a range

of scores from 14-100 for 76 mothers with a mean of 71 and a SD of 22. The rationale for

using this subscale instead of the entire Normalization Scale is that this scale was

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originally developed for families with children who are eight to twelve years old so many

of the items on the other subscales do not apply to preschool age children. This scale was

originally developed by Murphy and Gottlieb (1992) for use in a thesis. No further use of

this instrument has been documented in the literature.

Research thus far regarding normalization of chronic illness in childhood has used

qualitative methodology with the exception of Murphy (1994). However, there is

currently a federally funded research grant being conducted to translate the Family

Management Style Framework into a quantitative instrument, The Family Management

Style Survey (Knafl & Deatrick, 2006), that can be used to measure level of

normalization. This instrument will translate the major components and dimensions of the

Family Management Style Framework into a questionnaire that will assess a family’s

response to a child’s chronic illness and indicate the Family Management Style the

family is currently using. Efforts are underway to field test the instrument with a diverse

sample of 600-700 parents of children with chronic illnesses to assess the psychometric

properties. No target date was reported by the authors for completion of this tool for use

in research (Knafl & Deatrick, 2006).

Normalization is the major concept underlying The Family Management Style

Framework. In a qualitative study using the Family Management Style Framework, Knafl

et al. (1996) delineated five different family management styles to reflect the degree of

normalization used by families: Normalcy was the overriding theme in the Thriving

Family Management Style and the dominant theme in the Accommodative Family

Management Style. The Enduring, Struggling and Floundering Family Management

Styles use consecutively lower degrees of normalization (Knafl et al., 1996). Therefore,

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The Family Management Style Survey, though not in completed form at this time, will be

a measure of a family’s efforts at normalization.

Family Functioning

Family functioning will be assessed using the Feetham Family Functioning

Survey (FFFS) (Roberts & Feetham, 1982). The FFFS measures the parent’s perception

and level of satisfaction with relationships between the family and individuals, the family

and subsystems and between the family and the community. The instrument has 25 items

on a 7-point likert scale ranging from 1 “little” to 7 “much” that ask participants to rate

(a) how much is there now? (b) how much should there be? (c) how important is this to

you? The Porter format that is used in this instrument allows for a measurement of a

discrepancy score between achieved (a) and expected (b) levels of functioning. The

higher the sum of the discrepant scores the greater the dissatisfaction with family

functioning. This format decreases the risk of social desirability and controls for cultural

and ethnic diversity since each item is valued by the participant. When the importance

scores (c) are used along with the discrepancy score, one can gain insight as to

importance of particular aspects of functioning (Roberts & Feetham, 1982). Only

discrepancy scores on this instrument will be used for analysis in this study. Examples of

questions on this instrument are as follows: (a) the amount of discussion with your

friends regarding your concerns and problems, (b) the amount of time you spend in

leisure/recreational activities, (c) the amount of time your work routine is disrupted

(including housework).

The FFFS is well established and has been used in numerous nursing research

studies, primarily in families with young children. The Cronbach’s alpha for the scale is

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.85 with a test-retest reliability of .85 and a .72 correlation between husbands and wives

scores. Concurrent validity is supported by a -.54 correlation with the Family Functioning

Index (Roberts & Feetham, 1982). The FFFS is appropriate for single-parent families that

do not have someone assuming a spousal role (Feetham, 1991). The respondent scores

the spouse-related items as if there was a person taking the spousal role such that when

asked questions related to the spouse (a. how much is there) is scored low and (b. how

much should there be) is also scored low. The result is a low or zero discrepant score for

the spouse-related items (Feetham, 1991).

In a study examining the response of parents to their child’s chronic illness, Knafl

and Zoeller (2000) found the Cronbach alpha for the FFFS was .75 for fathers and .88 for

mothers. The mean score for the FFFS in this study was 23.44 with SD of 16.51 for

mothers and 26.23 with SD 11.81 for fathers. Hock-Long (1997) also used the FFFS in a

dissertation study of mothers caring for ventilator dependent children. No Cronbach alpha

was reported for this study.

In conclusion, the FFFS has been shown to be a reliable and valid tool to measure

family functioning in families who have children with chronic illness. This study will

examine the use of a family functioning tool related to satisfaction with various

relationships of a family who has a child who is technology dependent.

Demographic Characteristics

Study Covariates

The age of the child who is technology dependent will be assessed and measured

as age in months, rounded to the nearest month. This data was collected for descriptive

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purposes and to examine the relationships between the child’s age as a covariable and

depressive symptoms, normalization and family functioning.

Duration of caregiving at home for the child who is technology dependent will be

measured in months and defined as the amount of time that the child has been cared for at

home with the special technology.

Amount of home health care nursing hours will be documented as a) number of

RN hours and b) number of LPN hours of care the child receives per week then added to

determine the total number of home health care nursing hours received in one week.

Both duration of caregiving and number of home health care nursing hours will be used

as covariables in three of the research questions; in particular related to family

functioning and normalization.

Race/ethnicity will be categorized and coded as 1=Caucasian, Latino or Hispanic,

2=African-American, 3=Caucasian, Non-Hispanic, 4=Asian, 5=Native American or

6=Bi-racial for descriptive purposes. These categories will be further collapsed to

1=Caucasian, Non-Hispanic and 0=all other races/ethnicity for the analysis of

normalization and family functioning.

Family income will be measured by asking the caregiver to select from the

following categories of total family income before taxes and coded as 0= equal or less

than $20,000, 1=$20,001-40,000, 2=$40,001-60,000, 3=$60,001-80,000, or 4=>$80,001.

Family income will be used as a covariate in the analysis of family functioning and

normalization.

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Other Demographic Information

Other demographic information that will be collected for descriptive purposes

only include mother’s relationship to the child, mother’s marital status and level of

education, number living in the household, child’s gender and mother’s employment data.

Mother’s relationship to the child will be coded as 1=biological mother,

2=adoptive mother, 3=foster mother, 4=step mother, 5=grandmother. These will be

collapsed into categories of biological mother (coded as 1) and all others (coded as 0), to

examine the relationship of mother’s relationship with the child and depressive

symptoms, normalization and family functioning.

Marital status will be assessed using self-report coded as 1=single, never married,

2=single, living with partner, 3=married, 4=separated, 5=widowed, or 6=divorced. In the

analysis, these will be coded as married and living with spouse (coded as 1) and

everything else (coded as 0) to examine the relationships between marital status and

depressive symptoms, normalization and family functioning.

Education will be categorized and coded as 1=less than 7th grade, 2=completed 8th

grade, 3=partial high school, 4=high school graduate, 5=technical/vocational program

graduate 6=partial college, 7=associate’s college degree, 8=baccalaureate degree,

9=partial graduate school and 10=master’s degree, 11=doctoral degree, 12=other. These

will be further collapsed into three categories of high school graduate or less, some

college and up to a baccalaureate degree and the final category, graduate school and

beyond. In the analysis, educational categories will be used to examine the relationship of

education with depressive symptoms, normalization and family functioning.

Total number in household will be measured by asking for a) number of adults

and b) number of children living in the household.

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Employment status will be coded as 0=not employed outside the home,

1=employed outside the home. Number of hours employed will be coded as number of

hours per week.

Data Management

Data integrity for this study will be maintained in the following ways. Instrument

pages will be labeled with the subject identification number only. The researcher will be

responsible for filling in missing codes, assigning codes to fill-in-the-blank items on the

Demographic and Level of Technology Dependency Questionnaire instruments,

measuring the Normalization Visual Analog Scale (VAS) as well as scoring the FS II-R

(after reverse coding), CES-D (after reverse coding), and the FFFS. A codebook will be

developed to give each variable in the study an abbreviated name up to 8 characters, as

well as a descriptive variable label, and a range of possible numerical values of every

variable entered in a computer file. Computer column numbers will be included on each

instrument form to aid data entry. Data entry will be conducted in a room free of

distractions with breaks at least every 2 hours to minimize the potential for errors in

computer entry. A research assistant will perform second-level coding of the instruments

to check for accuracy. Data from all the instruments will be double entered into the SPSS

15.0 software package. Accurate data entry will be ensured by printing out and

comparing the two versions of the data file. All data will be stored in locked file cabinets

and password secured computers. Back up files will be made at each step of the process

on the hard drive, school server and a jump drive that will be locked up in secure file

cabinet.

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The researcher will use SPSS 15.0 to conduct a statistical analysis of the data.

Frequencies will be examined on all variables to determine outliers. After outliers are

identified, they will be compared with information recorded on the instruments to verify

accuracy.

Data Analysis

The data analysis will include descriptive statistical analysis, preliminary data

analysis, Pearson product-moment correlation, correlation matrix, hierarchical multiple

regression and multiple analysis of variance (MANOVA). The descriptive statistical

analysis will be conducted for each variable to examine the shape of distribution (normal,

skewness, kurtosis), central tendency (mean, median, mode) and dispersion of scores

(range, variance, standard deviation). Preliminary analysis will include data cleaning and

testing to ascertain that assumptions for each statistical test are met by examining

residuals. The steps to test the assumptions for regression analysis will include an

assessment for: 1) a zero mean for residuals by examining SPSS output; 2) residual

normality by plotting residuals on a histogram, examining a P-P plot to compare observed

to expected residuals and casewise diagnostics; 3) independence by examining that one

residual is not influenced by other residuals and that the data is not nested by ascertaining

that the Durbin Watson statistic is approximately 2.0. If the Durbin Watson statistic is

>2.5 or <1.5 there is a problem with independence; 4) homoscedasticity by plotting the

standard residuals against the standard predicted value and ascertaining that residuals are

in an even, random scatter around the zero line. Other assumptions to be examined are: 1)

fixed independent variables; 2) absence of measurement error; 3) absence of

multicollinearity by examining if correlation is >.80 between any of the independent

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variables, tolerance is not equal to or <.20 and that the Variance Inflation Factor (VIF) is

not equal to or >10; 4) influential data points by determining that the Cook’s D is not

>1.0.

Number of independent variables for the multiple regression power analysis: 9 (4

independent variables and 5 covariables)

Independent Variables/Covariables:

Mother’s Mental Health:

Depressive Symptoms

Child Health:

Severity of Illness

Child's technology dependence

Child's functional severity

Normalization (May also function as a dependent variable depending on the

research question)

Level of normalization

Covariables: Length of caregiving duration, amount of home health care nursing, race,

family income and age of the child who is dependent on technology.

Dependent Variable:

Family Functioning (May function as an independent variable depending on the research

question).

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According to Cohen's (1992) article on "A Power Primer" assuming a medium effect size,

alpha .05, Power.80 a sample of 115 would be needed given 3% additional participants to

account for any missing data.

Research Questions

Five research questions are identified for this study. Data analysis methods

include descriptive statistics, Pearson r product-moment correlation, correlation matrix,

hierarchical multiple regression, and multiple analysis of variance. The following study

questions will be addressed:

1a. What are the relationships of mother’s depressive symptoms, child’s

severity of illness (functional severity and level of technology

dependence) and normalization efforts, with family functioning in families

with a child who is technology dependent? 1b. Do these relationships hold

after adjusting for length of caregiving duration, amount of home health

care nursing hours, race, family income and age of the child who is

dependent on technology? (Correlation Matrix, Multiple Regression)-

F statistic

After the assumptions for multiple regression have been met, Pearson r product-

moment correlation will be performed to examine the relationships between 1) mother’s

depressive symptoms and family functioning; 2) child’s severity of illness and family

functioning; 3) efforts at normalization and family functioning. All variables and

covariables will then be entered into a correlation matrix to examine their relationships.

Statistical significance will be set at alpha .05.

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It is hypothesized that greater level of mother’s depressive symptoms, greater

severity of the child’s illness and less normalization effort will be related to poorer family

functioning. A hierarchical multiple regression analysis will be conducted entering

mother’s depressive symptoms (level of depressive symptoms), child’s severity of illness

(functional severity and level of technology dependence), efforts at normalization (level

of normalization) in the first step of the model. For the first step of the analysis, the R2

(percent of explained variance) for the dependent variable (family functioning) accounted

for by the independent variables (mother’s depressive symptoms, child’s severity of

illness, efforts at normalization) will be determined. Next, the significance of the

equation (F test- alpha .05) as well as relative contribution of the independent variables

(Standardized Beta) will be determined. If the F test is not significant then none of the

independent variables were significant to explain the dependent variable therefore fail to

reject the null hypothesis. Additionally, the independent variable’s partial regression

coefficients (b’s) for significance will be examined via the t-test statistic. If the R2 is

significant and none of the partial correlations are significant then there is likely a very

high correlation between the independent variables (multicollinearity).

The covariables, length of caregiving duration, amount of home health care

nursing hours, race , family income and age of the child dependent on technology would

then be entered in the second step of this hierarchical multiple regression model to

determine if the relationships of mother’s depressive symptoms (level of depressive

symptoms), child’s severity of illness (functional severity and level of technology

dependence), efforts at normalization (level of normalization) still held. This would be

determined by using the above procedures of examining the amount of explained

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variance, significance on the F test (alpha .05), and assessing the partial regression

coefficients for significance on the t-test statistic.

2. Are there differences in a) family functioning and b) normalization efforts

and c) mothers’ level of depressive symptoms based on the child’s level of

technology dependence (3 levels)? (MANOVA, Univaritate ANOVA)-F

statistic

The assumptions for the multiple analysis of variance (MANOVA) will be tested

as follows: 1) instruments for the dependent variables, family functioning, efforts at

normalization, and mother’s depressive symptoms are continuous, interval level

measurements and the independent variable must be categorical with at least two groups;

2) normal distribution of the variable’s residuals will be determined by plotting residuals

on a histogram, examining a P-P plot to compare observed to expected residuals and

casewise diagnostics; 3) independent variable groups (level of technology dependence)

will be mutually exclusive as determined by the Office of Technology Assessment (1987)

rubric; and 4) equality of variances (homogeneity of variance) will be ascertained by

examining the descriptive statistics and by examining the Levene statistic for a non-

significant result; 5) sample size in each cell will be examined to see that it is

approximately equal however the test is robust enough if there is about 20 in the smallest

cell (Tabachnick & Fidell, 2001), 6) ascertain that there is no measurement error 7) a

correlation matrix will be performed and examined to determine if the dependent

variables are correlated (MANOVA works best with highly negatively correlated

dependent variables and acceptably well if they are moderately correlated in either

direction of .60) (Tabachnick & Fidell, 2001), 8) linearity as determined by scatterplots

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and 9) absence of outliers will be determined by examining the Mahalanobis distance

with an alpha not to exceed .001.

If the dependent variables are correlated at .3 or more MANOVA and univariate

analysis may proceed, otherwise, only the Analysis of Variance (ANOVA) can be

performed.Following the input of the variables into the MANOVA model, the Eta

squared will be examined to determine the explained variance (sum of squares between

the groups divided by the total sum of squares ie. the sum of the squared deviations of

each score in all groups from the grand mean for all).

To test for significance of the MANOVA, the Omnibus F test will be examined. If

the between group differences are greater than the within group differences there will be

a higher F statistic score (using either the Wilks’lambda, Hoetelling’s trace criterion,

Pillai’s criterion and Roy’s gcr criterion). The significance of the F distribution indicates

whether to reject the null hypothesis and conclude that one of the group means is

different from the others. Finally, post-hoc tests will be performed using the simultaneous

test procedures to determine which dependent variable(s) has the most influence in the

MANOVA model and the Scheffe test will be used for the ANOVA analyses.

3. Do a mother’s depressive symptoms have a mediating effect on the

relationship between the child’s severity of illness (functional status) and

(a) normalization and (b) family functioning in mothers with a child who

is technology dependent? (Correlation Matrix, Mediation using

Hierarchical Multiple Regression)-F statistic

After the assumptions for multiple regression have been met, Pearson product-

moment correlations will be performed to examine the relationships between the

independent variable in part (a), child’s severity of illness (functional status) and the

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dependent variable, normalization, as well as the independent variable in part (b), child’s

severity of illness (functional status) and the dependent variable, family functioning.

Three assumptions should be met prior to proceeding with a test for mediation

effects. The first assumption is that there should be a significant direct effect of the

independent variable on the dependent variable in both part (a) and part (b). Another

assumption that should also be met is that the dependent variable does not predict the

mediator variable. The final assumption is that there is no measurement error in the

mediator (Baron & Kenney, 1986; Bennett, 2000). An independent variable functions as

a mediator when variation in the independent variable account for variations in the

proposed mediator (path a), variations in the mediator account for significant variations

in the dependent variable (path b) and finally when the previous paths (paths a and b) are

controlled, there is no longer a significant relationship between the independent variable

and the dependent variable (path c). The strongest evidence of mediation is when this

path (path c) is zero (Baron & Kenny, 1986).

Three multiple regression equations are needed to test the mediation hypotheses

in part (a) and (b) of this research question. In the first equation (path a), the independent

variable for part (a) and (b), child’s severity of illness (functional status) will first be

entered followed by the mediator (mother’s depressive symptoms). The independent

variable, child’s severity of illness (functional severity) should be a significant predictor

of the mediator (mother’s depressive symptoms) as indicated by a significant F test

(alpha .05). In the second equation (path b), the independent variable, child’s severity of

illness (functional status) would be entered first followed by the dependent variables,

normalization in part (a) and family functioning in part (b). The independent variables

should be significant predictors of the dependent variables as indicated by a significant F

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test (alpha .05). In the final equation (path c), when the other paths (path a & b) are

controlled, the previously significant relationship between the independent variable and

the dependent variable will be less or no longer significant if mediation is present. It is

known as a complete mediation if the third equation (path c) becomes zero after the

independent variable and the mediator are added together in the same equation (Baron &

Kenney, 1986; Bennett, 2000).

4. Does normalization have a mediating effect on the relationship between

(a) child’s severity of illness (functional status) and family functioning, (b)

depressive symptoms and family functioning in mothers with a child who

is technology dependent? (Correlation Matrix, Mediation using

Hierarchical Multiple Regression)-F statistic

After the assumptions for multiple regression have been met, Pearson product-

moment correlations will be performed to examine the relationships between the

independent variable in part (a), child’s severity of illness (functional status) and the

dependent variable, family functioning, as well as the independent variable in part (b),

mother’s depressive symptoms and the dependent variable, family functioning. Three

assumptions should be met prior to proceeding with a test for mediation effects. The first

assumption is that there should be a significant direct effect of the independent variable

on the dependent variable in either part (a) and/or part (b). Another assumption that

should also be met is that the dependent variable does not predict the mediator variable.

The final assumption is that there is no measurement error in the mediator (Baron &

Kenney, 1986; Bennett, 2000).

An independent variable functions as a mediator when variation in the

independent variable account for variations in the proposed mediator (path a), variations

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in the mediator account for significant variations in the dependent variable (path b) and

finally when the previous paths (paths a and b) are controlled, there is no longer a

significant relationship between the independent variable and the dependent variable

(path c). The strongest evidence of mediation is when this path (path c) is zero (Baron &

Kenny, 1986).

Three multiple regression equations are needed to test the mediation hypothesis in

part (a) and (b) of this research question. In the first equation (path a), the independent

variable for part (a) child’s severity of illness (functional status) and part (b) mother’s

depressive symptoms will first be entered followed by the mediator, normalization. The

independent variable should be a significant predictor of the mediator for both part (a)

and part (b) as indicated by a significant F test (alpha .05). In the second equation (path

b), the independent variable, child’s severity of illness (functional status) for part (a) and

mother’s depressive symptoms in part (b) would be entered first followed by the

dependent variable, family functioning (satisfaction with relationships). The independent

variables should be significant predictors of the dependent variables as indicated by a

significant F test (alpha .05). In the final equation (path c), when the other paths (path a &

b) are controlled, the previously significant relationship between the independent variable

and the dependent variable will be less or no longer significant if mediation is present. It

is known as a complete mediation if the third equation (path c) becomes zero after the

independent variable and the mediator are added together in the same equation (Baron &

Kenney, 1986; Bennett, 2000).

5 a. What are the relationships among mother’s depressive symptoms, child’s

severity of illness (functional status, level of technology dependence),

family functioning on normalization efforts in families with a child who is

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technology dependent? 5b. Do these relationships hold after adjusting for

length of caregiving duration, amount of home health care nursing hours,

race, family income and age of the child who is dependent on technology?

(Correlation Matrix, Multiple Regression)- F statistic

After the assumptions for multiple regression have been met, Pearson product-

moment correlation will be performed to examine the relationships all variables and

covariables. All variables and covariables will then be entered into a correlation matrix to

examine their relationships. Statistical significance will be set at alpha .05. Less

depressive symptoms and severity of illness, Caucasian (non-Hispanic race), longer

caregiving duration, greater amount of home health care nursing hours and a younger

child are hypothesized to be significantly related to greater efforts at normalization while

controlling for length of caregiving duration, amount of home health care nursing hours,

race, family income and age of the child who is dependent on technology.

A multiple regression analysis will be conducted entering mother’s depressive

symptoms, child’s severity of illness (functional status, level of technology dependence),

and family functioning simultaneously. First, the R2 (percent of explained variance) for

the dependent variable (normalization) accounted for by the independent variables

(mother’s depressive symptoms, child’s functional status, level of technology

dependence, family functioning) will be determined. Next, the significance of the

equation (F test- alpha .05) as well as relative contribution of the independent variables

(Standardized Beta) will be determined. If the F test is not significant then none of the

independent variables were significant to explain the dependent variable therefore, fail to

reject the null hypothesis. Finally, the independent variable’s partial regression

coefficients (b’s) will be examined for significance via the t-test statistic. If the R2 is

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significant and none of the partial correlations are significant then there is likely a very

high correlation between the independent variables (multicollinearity).

The covariables, length of caregiving duration, amount of home health care

nursing hours, race , family income and age of the child dependent on technology would

then be entered in the second step of this hierarchical multiple regression model to

determine if the relationships of mother’s depressive symptoms , child’s severity of

illness (functional severity and level of technology dependence), family functioning still

held. This would be determined by using the above procedures of examining the amount

of explained variance, significance on the F test (alpha .05), and assessing the partial

regression coefficients for significance on the t-test statistic.

Protection of Human Rights

Approval from the Institutional Review Boards at University Hospitals/Case

Medical Center to conduct the study will be obtained. The researcher will contact

potential participants after being notified by the outpatient clinic physician as discussed

previously. The researcher will give an overview of the study and obtain informed

consent from participants on the IRB approved form that contains a written description of

background information, procedures, risks and benefits, confidentiality. The voluntary

nature of the study will be described which includes the option to withdraw at any time

without affecting care of their child in any way. Parents will be told that they may choose

not to answer a question or discontinue the interview at any time.

Confidentiality will be maintained by using participant identification numbers on

all data collection sheets. Identifying data (participant names and code numbers) and

consent forms will be separated from the study data and maintained in a locked file

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cabinet. The instruments will be coded and maintained along with the codebook in a

locked file cabinet. The data and codes will be kept for a period of five years following

completion of the study to allow for secondary analysis and will then be destroyed by the

researcher. Only the researcher and the dissertation chair will have access to the data and

codes.

Minimal risk is involved with participation in this study. Physical discomforts

might involve fatigue as the total time for the interview will be 45-50 minutes. Mothers

will be told that they may choose to take a break at any point during the interview. The

amount of time required for participation in the study may also be viewed as an

inconvenience. A potential emotional risk during the interview is that recalling

information regarding their child’s course of illness and potential home management

difficulties may make some mothers uncomfortable. The researcher will be vigilant to

assess the participant’s discomfort. Participants experiencing considerable discomfort

will be asked if they wish to stop the interview and will be given supportive assistance by

the interviewer. The parent will then be given the telephone number of an on call mental

health professional at a 24 Hour Crisis Center (Appendix I). Additionally, if any

participant is noted to have a level of depressive symptoms above the recognized cutoff

of 16 on the CES-D measure she will be told of the findings and given a mental health

information sheet as above (Appendix I). Any participant noted to have a score of 23 or

above on the CES-D will be assessed for imminent risk of suicide by asking if they have

had any thoughts of harming themselves in any way or perhaps killing themselves. If the

answer to any portion of the question is “yes” the participant will be told of a concern for

their safety and the necessity for the interviewer to contact the Mobile Crisis Team. The

risks and inconveniences involved are reasonable because the risks are temporary and can

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be minimized by the above procedures. In addition, the discomfort is no more that what

the participant might experience in her daily life.

There is no direct benefit from participation in this research study. Participants

will however have the opportunity to learn more about the research process and to receive

results from the study which may be of benefit to their family. Participants may also

benefit from the interaction and the opportunity to discuss their thoughts and feelings

related to caring for the child who is technology dependent. Other families of children

who are technology dependent will benefit from the study findings related to factors that

assist with family management.

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CHAPTER FOUR - RESULTS

The purpose of this study was to explore how families respond to and manage the

special challenges of children who are technology dependent after they are discharged

from the hospital to home. The study examined the relationships of the child’s severity of

illness (functional status, level of technology dependence), mother’s depressive

symptoms, and efforts at normalization with family functioning in families with children

who are technology dependent. Study covariates included age of the child, duration of

caregiving, amount of home health care nursing, family income and race. Additionally,

mediating effects of normalization on family functioning as well as mediating effects of

mother’s depressive symptoms on normalization and family functioning were examined.

A convenience sample of 103 mothers of children who are technology dependent were

recruited primarily from Rainbow Babies and Children’s (RB&C) Hospital in Cleveland,

Ohio to examine these questions. This chapter provides a summary of the results and an

interpretation of the findings.

Description of Participants

Mothers who were the participants in this study lived primarily in Northeastern

Ohio but due to the large service area of a tertiary children’s hospital some lived as far

away as Northwestern Pennsylvania and Southwestern New York. Participants were

identified and recruited from a number of specialty clinics at RB&C (Table 4.1). The

pulmonology department identified the majority of participants (n=56, 54.4%) to be

approached by the investigator and included the Technology Dependency Clinic (n=30),

Pulmonology Clinic/Service (n=20), and the Cystic Fibrosis Center (n=6). Other

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departments that collaborated in identifying participants included the Gastroenterology

(n=18, 17.5%), Otolaryngology (n=4, 3.9%) and Pediatric Surgery (n=11, 10.7%)

Departments as well as the Preterm Infant Follow-up Clinic (n=5, 4.9%). Nine other

participants were also identified by the RB&C Family Learning Center (n=2, 1.9%), a

friend or recruitment flyer (n=5, 4.9%) and a primary care physician of children who are

technology dependent (n=2, 1.9%).

Table 4.1. Location of Participant Recruitment (N=103) LOCATION n % Pulmonology Department 56 54.4 Gastroenterology Department 18 17.5 Otolaryngology Department 4 3.9 Preterm Infant Follow-up 5 4.9 Pediatric Surgery Department 11 10.7 Other 9 8.7

There were a total of 21 additional potential participants who were invited to

participant but did not enroll in the study: 14 did not respond following an introductory

letter, follow-up phone calls and messages; 5 stated they were too busy and overwhelmed

with other responsibilities; 2 stated they were not interested. Therefore, there were a total

of 124 mothers approached with a response rate of 88.7% (110/124) and a participation

rate of 93.6% (103/110). Additionally, there were 2 Spanish-speaking mothers who were

ineligible to participate because they could not speak or read English and 2 mothers who

lived over 75 miles from Cleveland stated that they were no longer coming to Ohio and

RB&C for their health care.

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Face-to-face interviews were conducted with mothers in outpatient clinics (n=38,

36.9%) or in another private place of the mother’s choosing such as their home (n=41,

39.8%), Dahm’s Clinical Research Unit at RB&C (n=9, 8.7%), United Cerebral Palsy

Center (n=4, 3.9%), Rehabilitation Services at RB&C (n=5, 4.9%), a public library (n=2,

1.9%) or other places such as their place of employment or their child’s school (n=4,

3.9%). Interviews ranged from 45 minutes up to 90 minutes with an average time of 55

minutes. The length of the interview depended on a number of factors such as

interruptions from the clinic staff, care required by the child who is technology dependent

or their siblings and issues that the mothers wanted to discuss triggered by the content of

the questionnaires. Whenever possible, further discussion regarding issues and concerns

was conducted following completion of the questionnaires. Mothers were directed to

contact their health care providers with any questions or concerns regarding their child’s

health care or service needs.

Table 4.2 provides a description of the 103 mothers who participated in this study.

Mothers in this study ranged in age from 21-66 years (M=37.87, SD=9.35); a majority

were between 31-40 years old (n=46, 44.6%). The race of participants was predominately

Caucasian (n=82, 79.6%) including those of Hispanic ethnicity (n=6) however the

percentage of participants from other races such as African American (n=17, 16.5%),

Asian (n=2, 1.9%) and Biracial (n=1, 1%) models the demographics of the greater

Northeastern Ohio population. One participant chose not to answer this question.

Mothers possessed a wide variation of educational backgrounds; 31.1% (n=32)

were technical/vocational, high school graduates or less, 38.8% (n=40) had a partial

college education or an associate’s degree and 29.1% (n=30) held a baccalaureate degree,

some graduate school, master’s degree or more. One mother chose not to answer this

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question. A majority of the participants (n=88%, 85.4%) were the biological mother of

the child but there were also adoptive (n=8, 7.8%) and foster (n=5, 4.9%) mothers as well

as grandmothers (n=2, 1.9%) who had the primary caretaking responsibility. Most

participants were married (n=73, 70.9%) however others were single, never married (n=8,

7.8%) or never married and living with a partner (n=5, 4.9%), separated (n=3, 2.9%),

widowed (n=2, 1.9%) or divorced (n=12, 11.6%). Almost half of participants were not

Table 4.2. Descriptive Statistics – Individual Characteristics of Mothers (N=103) Variable n % Race of Mother African American 17 16.5 Caucasian 82 79.6 Asian 2 1.9 Bi-racial 1 1.0 Unknown 1 1.0 Age of Mother 21 – 30 24 23.3 31 – 40 46 44.6 41 – 50 22 21.4 51 10 9.7 unknown 1 1.0 Family Income (U.S. Dollars) $20,000 12 11.6 $20,001 to $40,000 25 24.3 $40,001 to $60,000 15 14.6 $60,001 to $80,000 29 28.2 $80,001 20 19.4 unknown 2 1.9 Education Junior High Graduate 2 1.9 Some High School 5 4.9 High School Graduate 12 11.7 Technical/Vocational Graduate 13 12.6 Partial College 31 30.1 College (Associate Degree) Graduate 9 8.7 College Graduate (4 years) 20 19.4 Partial Graduate School or more 10 9.7 unknown 1 1.0

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Table 4.2 (Continued) Descriptive Statistics – Individual Characteristics of Mothers (N=103) Variable n % Mother’s relationship to child Biological mother 88 85.4 Adoptive mother 8 7.8 Foster mother 5 4.9 Grandmother 2 1.9 Marital Status Single, never married 8 7.8 Never married, living with partner 5 4.9 Married 73 70.9 Separated 3 2.9 Widowed 2 1.9 Divorced 12 11.6 Employment Status (hours worked / week) Not employed 47 45.6 10 – 24 hours per week 17 16.5 25 – 39 hours per week 11 10.7 40 hours per week 28 27.2

employed outside the home (n=47, 45.6%) while others worked 39 hours or less per week

(n=28, 27.2%) or full-time 40 hours or more per week (n=28, 27.2%). Yearly total family

income varied; about half of families earned $60,000 or less (n=52, 50.5%), 28.2%

(n=29) earned $60,001-$80,000 and 19.4% (n=20) earned $80,001 or more. Two

participants chose not to answer this question.

Children of Participants

Table 4.3 provides a description of the children who are technology dependent

whose mothers were participants in the study. About half of the children were age 5 years

or less (n=49, 47.6%) with 20.4% (n=21) age 23 months or less, 27.2% (n=28) 2-5 years

old, 31.1% (n=32) 6-10 years old and 21.3% (n=22) 11-16 years old. The gender of the

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children was evenly divided; male (n=51, 49.5%) and female (n=52, 50.5%). About half

of the children (n=51, 49.5%) had a primary diagnosis that was due to a genetic disorder

and 27.1% (n=28) were born preterm at 36 weeks or less.

Table 4.3. Descriptive Statistics – Individual Characteristics of Children (N=103) Variable n % Age of Child 23 months 21 20.4 2 – 5 years 28 27.2 6 – 10 years 32 31.1 11 – 16 years 22 21.3 Gender of Child Male 51 49.5 Female 52 50.5 Primary Diagnosis Neuromuscular Dysfunction 45 43.7 Respiratory Dysfunction 23 22.3 Digestive Dysfunction 17 16.5 Circulatory Dysfunction 6 5.8 Cystic Fibrosis 6 5.8 Metabolic Dysfunction 4 3.9 Renal Dysfunction 2 1.9 Born Preterm (36 weeks or less) Yes 28 27.1 No 72 70.0 Unknown 3 2.9 Main Diagnosis Related to Genetic Disorder Yes 51 49.5 No 49 47.6 Unknown 3 2.9 Level of Technical Dependence OTA (1987) Group Group 1 18 17.5 Group 2 9 8.7 Group 3 76 73.8

A chart review was done to determine the primary diagnosis of the children. The

primary diagnosis was defined as the diagnosis that led to the cascade of events that

necessitated the child’s dependence on technology. The largest number of children in this

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study had diagnoses related to a neuromuscular dysfunction (n=45, 43.7%). This group

included children with Cerebral Palsy (n=18), Down Syndrome (n=6), Athrogryposis

(n=4), Myelodysplasia (n=4), a Muscular Dystrophy related diagnosis including Spinal

Muscular Atrophy (n=10) and others (n=3) that cannot be specifically named to maintain

participant confidentiality. Respiratory dysfunction was the primary diagnosis for 22.3%

(n=23); about half of these children had Bronchopulmonary Dysplasia (n=10). Digestive

dysfunction was the primary diagnosis for 16.5% (n=17) of children including Short

Bowel Syndrome (n=3). Other primary diagnoses that were less common included

circulatory dysfunction (n=6, 5.8%), Cystic Fibrosis (n=6, 5.8%), metabolic dysfunction

(n=4, 3.9%) and renal dysfunction (n=2, 1.9%). Cystic Fibrosis was left as a separate

diagnostic category due to the fact that this disease includes multiple system (respiratory

and digestive) involvement. There were three children in foster care for whom HIPAA

releases for a chart review could not be obtained due to county custody regulations. The

main diagnosis was determined based upon the type of technology used, the recruitment

site responsible for identifying the child and the information that was permitted to be

shared with the investigator however specific information regarding preterm birth or the

presence of a genetic disorder could not be obtained.

Children required a variety of technologies and were categorized into levels of

technology dependence based on the OTA (1987) designation for groups: Group 1

mechanical ventilation (n=18, 17.5%), Group 2 intravenous medications or nutritional

substances (n=9, 8.7%) and Group 3 respiratory or nutritional support (n=76, 73.8%) as

displayed in Table 4.3. A majority of children required one type of technology (n=51,

49.5%), 23.3% required two (n=24), 14.6% required three types of technology (n=15)

however some children required up to four types of technology simultaneously (n=13,

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12.6%) as shown in Table 4.4. Table 4.5 provides a description of the types of

technologies required by the children. Most required some type of feeding tube; either a

nasogastric tube (n=2, 1.9%) or a gastrostomy tube (n=89, 86.4%). Others required

oxygen at least on an as needed basis (n=44, 42.7%) via nasal cannula or face mask

(n=18), trach collar (n=24), or continuous positive airway pressure (n=2) (Table 4.5).

Table 4.4. Number of Technologies Used

Number of Technologies

Used

n %

1 51 49.5 2 24 23.3 3 15 14.6 4 13 12.6

Table 4.5. Descriptive Statistics – Type of Technology Used (N=103) Variable n % Nasogastric Tube Feeding with 2 1.9 without 101 18.1 Gastrostomy Tube Feeding with 89 86.4 without 14 13.6 Intravenous Infusion with 10 9.7 without 93 90.3 Oxygen with 44 42.7 without 59 57.3 Continuous Positive Airway Pressure with 4 3.9 without 99 96.1 Tracheostomy Tube with 32 31.1 without 71 68.9 Mechanical Ventilator with 18 17.5 without 85 82.5 Note: May use more than one type of technology simultaneously.

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A total of 31.1% had a tracheostomy tube (n=32), 17.5% required mechanical ventilation

(n=18) and 3.9% used continuous positive airway pressure (n=4). Of the 9.7% (n=10)

with intravenous infusions 6 received intermittent infusions and 1 received continuous

infusion of medications while 3 received intravenous infusions of total parenteral

nutrition.

Description of Study Variables

The independent variables in this study included the child’s severity of illness

(functional status, level of technology dependence), mother’s depressive symptoms,

efforts at normalization and family functioning. Covariates included age of the child,

duration of caregiving, amount of home health care nursing, family income and race. The

outcome variable (family functioning/normalization) differed depending upon the

Table 4.6. Means, Standard Deviations, Ranges and Skew for Major Study Variable (N=103)

Variable Mean SD Possible Ranges

Actual Ranges

Skew

Age of Child (months)

78.86 53.13 3 – 203 7 – 202 .46

Duration of Caregiving (months)

57.06 44.92 2 – 203 2 – 161 .64

Home Health Care Nursing (hours/week)

41.23 43.89 0 – 168 0 – 144 .60

Functional Status 21.47 4.03 0 – 28 11 – 28 -.83 Depressive Symptoms 14.07 11.05 0 – 60 0 – 54 .94 Normalization (millimeters)

385.89 190.5 0 – 1000 45 – 900 -1.37

Family Functioning 35.25 20.58 0 – 150 0 – 114 1.02

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research question being addressed. Descriptive statistics (mean, standard deviation,

possible/actual range, skew) for all study variables except level of technology

dependence, a non-continuous variable, are shown in Table 4.6.

Description of Measurement Results for Independent/Dependent Variables

Table 4.7 describes the functional status of the participants’ children who are

technology dependent as measured by the Functional Status II-Revised (Stein & Jessop,

1990). Functional status is the effect of a chronic health condition on the child’s ability to

perform age-appropriate daily life activities (Stein et al., 1987). Higher scores indicate

better function with the maximum possible score of 28. Approximately 13% (n=13) had

very good function (26 or above) and over half (55.3%, n=57) had fairly good function

(21-25) while approximately 32% (n=33) had fair to poor function (20 or below).

Table 4.7. Functional Status II - Revised (N=103)

Total Score n % 15 11 10.7

16 - 20 22 21.4 21 - 25 57 55.3

26 13 12.6 Note: Higher scores indicate better function

Table 4.8 describes the amount of depressive symptoms reported by the

participants in the study using the Center for Epidemiological Studies- Depression Scale

(CES-D) (Radloff, 1977). Possible scores range from 0-60 with a cutoff score of 16 or

greater indicating participants at high risk for clinical depression. While approximately

60% (n=62) had scores of 15 or less, about 40% (n=41) had scores above the cutoff

indicating high risk for clinical depression. Noteworthy is the fact that in those who were

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above the cutoff the majority had scores equal to or greater than 21 (24.3%) indicating a

very high risk for clinical depression. The CES-D was scored while mothers were

completing other questionnaires. Mothers who scored equal to or greater than 16 were

informed that their score was high and were directed to discuss this with their primary

health care provider. They were also given an IRB approved resource information sheet

with the telephone number for 24 hour crisis information as well as a telephone number

for assistance with access to mental health care services or resources for themselves, their

child or other family members. Mothers who scored 23 or above on the CES-D were

screened for suicide risk as mandated by the IRB as previously described. None of the

mothers screened indicated a risk for suicide therefore none required emergency

intervention.

Table 4.8. Depressive Symptoms. Center for Epidemiological Studies – Depression

Scale (CES-D) (N=103) Total Score n %

15 62 60.2 16 – 20 16 15.5

21 25 24.3 Note: A cutoff of 16 is used to identify those at high risk for clinical depression.

Table 4.9 describes the amount of normalization efforts used by families as

measured by the “Actual Effect of Chronic Physical Disorder on the Family”, a subscale

of the Normalization Scale (Murphy & Gottlieb, 1992). Normalization includes,

“adjusting the environment to provide normal life experiences that will meet the child’s

evolving social, physical, intellectual and emotional needs” (Murphy, 1994, p. 10) while

at the same time managing family life and activities so that they can lead as close to a

“normal” family life as possible (Murphy, 1994). This Visual Analog Scale (VAS) has

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potential scores from 0-1000; higher scores indicate greater use of normalization. There

was fairly even distribution of scores with 27.2 % (n=28) having very low normalization

efforts and 15.5% (n=16) had very high normalization efforts.

Table 4.9. Normalization Scale. “Actual Effect of Chronic Physical Disorder on the

Family” Subscale (N=103) Total Score n %

250 28 27.2 251 – 350 19 18.4 351 – 450 21 20.4 451 – 550 19 18.5

551 16 15.5 Note: Higher scores indicate greater use of normalization.

Table 4.10 describes results from the Feetham Family Functioning Survey (FFFS)

that measures family functioning (Roberts & Feetham, 1982). This instrument measures

the parent’s perception and level of satisfaction with relationships between the family and

individuals, the family and subsystems and between the family and the community.

Scores indicate the discrepancy between how much there is now and how much there

should be of a particular activity or function with possible scores ranging from 0-150.

Higher scores indicate greater dissatisfaction with family functioning; poorer family

functioning. There was a fairly equal amount of distribution across all categories however

almost half of the participants had scores from 21-40 indicating they were somewhat

satisfied with family functioning. Approximately 23% (n=24) had scores equal to or less

than 20 indicating that they were for very satisfied with family functioning while 10%

(n=10) were very dissatisfied with family functioning. The majority of mothers (45%,

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n=45) indicated they were somewhat satisfied with family functioning and about 21%

(n=22) were somewhat dissatisfied.

Table 4.10. Feetham Family Functioning Survey (N=103) Total Discrepancy Score n %

20 24 23.3 21 - 40 45 43.7 41 - 60 22 21.4

61 10 9.7 missing 2 1.9 Note: Higher scores indicate greater dissatisfaction with family functioning (poorer

family functioning).

Description of Caregiving Variables

Table 4.11 describes two important covariables examined in this study; the duration of

caregiving and the amount of home health care nursing the children received. The

duration of caregiving is defined as the period of time the mother had been caring for the

child since he/she had been discharged from the hospital dependent on technology. The

duration of caregiving for this study ranged from 2 to 161 months (M 57.06, SD 44.92)

and included a fairly even distribution of participants in each of the categories: 17.5%

(n=18) 12 months or less, 22.3% (n=23) 13-35 months, 24.3% (n=25) 36-71 months,

22.3% (n=23) 72-107 months and 13.5% (n=14) had a caregiving duration of equal to or

greater than 108 months. The amount of home health care nursing the children received

ranged from 0-144 hours per week (M 41.23, SD 43.89). A large proportion of children

in the study did not receive any home health care nursing (n=38, 36.8%), however, 20.3%

(n=21) received 1-40 hours, 16.5% (n=17) received 41-80 hours, 23.3% (n=24) received

80-120 hours and 2.9% (n=3) received 121 or more hours per week.

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Table 4.11. Descriptive Statistics – Caregiving Variables (N=103) Variable n % Duration of Caregiving (total months) 12 months 18 17.5 13 – 35 months 23 22.3 36 – 71 months 25 24.3 72 – 107 months 23 22.3 108 months 14 13.5 Home Health Care Nursing (total hours / week) None 38 36.8 1 – 40 21 20.3 41 – 80 17 16.5 81 – 120 24 23.3 121 3 2.9

Preliminary Data Analysis

Preliminary analysis of all study variables included frequencies and descriptive

statistics. Data cleaning was performed and the assumptions of multiple regression and

Analysis of Variance (ANOVA) were examined.

Missing data was handled using individual mean substitution. On the CES-D, data

were missing completely at random for 4 participants; therefore, the mean score for the

missing item was imputed. For the “Actual Effect of the Chronic Physical Disorder on

the Family” subscale, those mothers who only had one child left item 6 blank because it

asked how much their child’s condition affected their other children’s activities. This

missing data was handled by summing the scores on items answered, determining the

mean for those items then multiplying by the total number of items on the scale (10

items). On the Feetham Family Functioning Scale those mothers who did not have

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partner/spouse involvement in their lives were reluctant to answer those questions despite

the fact that the directions indicate if there is no spouse/partner they are to answer the

questions based on how much they want the functions met. This missing data was

handled by summing the scores on items answered, determining the mean for those items

then multiplying by the total number of items on the scale (25 items).

Testing Assumptions for Multiple Regression

Correlation and hierarchical multiple regression were the primary methods of

analysis for this study. The assumptions for multiple regression were examined prior to

testing for statistical significance to prevent violation of assumptions that would

confound statistical conclusion validity. The primary assumptions for correlation and

multiple regression included:

1) Normality. For this assumption to be met the distribution of the variables must

follow a normal curve for adequate variance. This assumption was tested by

examining the frequency distribution and histogram with a superimposed normal

curve for all continuous study variables. Furthermore, while some of the variables

did have a slight skew as shown in Table 4.6, none were skewed greater than +/-3,

which would indicate a non-normal distribution (Tabachnick & Fidell, 2001). In

addition, the P-P probability plots were examined to compare observed to

expected residuals. The Normalization subscale and amount of home health care

nursing hours showed the most deviation between the observed and the expected

observations. The Central Limit Theorem proposes however that with a large

sample size such as in this study (n=103), the sampling distribution of means is

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normally distributed therefore the assumption of normality for this study was met

(Tabachnick & Fidell, 2001).

2) Linearity. The assumption of linearity requires that the association of X

(independent variable) and Y (dependent variable) must be linear and when

graphed follow a straight line (Tabachnick & Fidell, 2001). This assumption was

tested by analyzing the partial regression scatter-plots for each of the continuous

study variables. The partial plots option assesses the effect of each independent

variable on the dependent variable controlling for all of the other independent

variables. It was determined that normalization, family income and functional

status were non-linear therefore the variables were squared to overcome the

violation of non-linearity. Since no differences in results were noted, the original

(non-squared) variable was used to maintain a more parsimonious model.

3) Homoscedasticity. This assumption requires that the distribution of a Y score

should have approximately an equal score on X. Homoscedasticity was tested by

examining the constant error variance and plotting the Studentized Deleted

Residuals (Y axis ) against the Standard Predicted value (X axis) and ascertaining

that residuals are in an even, random scatter around the zero line. According to

Fox (1991), a problematic non-constant error indicating violation of this

assumption is higher than a 3:1 ratio of highest to lowest error variance. This

assumption was not violated for any of the study variables.

4) Independence of Observations. This assumption requires that independent

observations were collected; there are no nested data or data collected at multiple

time points on the same participant. Because this was a cross-sectional study this

assumption was not violated. This assumption was tested by examining that one

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residual is not influenced by the other residuals and determining that the Durbin

Watson statistic is not>2.5 or<1.5. There was no violation of this assumption; the

Dubin Watson statistic did not exceed 2.0 for any of the regression analyses.

5) Zero Mean for Residuals. This assumption requires that the mean value of the

error term for standardized residuals is zero and the standard deviation is one.

This assumption was met as determined by output from SPSS output of all

multiple regression analyses.

Secondary Regression Assumptions:

1) Absence of Measurement Error. All instruments were administered to mothers

in a consistent fashion by the investigator. Instruments demonstrated acceptable

reliability as evidenced by Cronbach’s alpha coeffients of .77-.92 in Table 3.1.

2) Absence of Multicollinearity. This assumption requires that independent

variables not be highly correlated >. 80. The highest correlation in this study (.73)

was between the variables child’s age and duration of caregiving.

Multicollinearity was also determined by examining that the tolerance was not

equal to or <.20. The lowest tolerance was noted to be duration of caregiving

(.433) which is still acceptable therefore this assumption was met.

3) Absence of Influential Data Points. This assumption examines if all cases exert

approximately the same influence on the regression coefficients. This assumption

was tested by examining that the Cook’s D is not equal to or >1.0. In this study

although there were two cases that were considered outliers (high discrepancy

scores on the Feetham Family Functioning Survey) none of the regression

analyses had a Cook’s D greater than .24; this assumption was not violated.

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Research Questions and Hypothesis Testing

Research Question 1a. What are the relationships of mother’s depressive

symptoms, child’s severity of illness (functional severity and level of technology

dependence) and normalization efforts, with family functioning in families with a child

who is technology dependent? 1b. Do these relationships hold after adjusting for length

of caregiving duration, amount of home health care nursing hours, race, family income

and age of the child who is dependent on technology?

More depressive symptoms, greater severity of illness and less normalization

effort were hypothesized to be related to poorer family functioning while controlling for

length of caregiving duration, amount of home health care nursing hours, race, family

income and age of the child who is dependent on technology. A Pearson product moment

correlation coefficient matrix comprising all study variables and covariates (Table 4.12)

was examined to determine if any significant relationships existed. The assumptions of

normal distribution and level of measurement were met. The value of statistical

significance was set at alpha .05.

Table 4.12. Correlations Among Study Variables (N=103) Variable Age Race Inc. Dur. HHC Dep. Norm. Fam. FS TD1 TD2 Age 1.000 Race .175 1.000 Inc .074 .360** 1.000 Dur .733** .145 .149 1.000 HHC .220* -.055 -.097 .236* 1.000 Dep .087 .080 -.224* .114 -.002 1.000 Norm .017 -.247* .044 -.068 -.126 -.453** 1.000 Fam .105 .049 -.090 .085 .034 .608** -.348** 1.000 FS -.108 .001 .087 -.020 -.002 -.239* .375** -.168 1.000 TD1 .120 .094 -.107 .191 -.417** .023 -.035 .099 .055 1.000 TD2 .202* .023 -.099 -.257** -.075 .079 -.131 .132 -.010 .142 1.000

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Note: Inc = Income; Dur = Duration of caregiving; HHC = Total home health care nursing hours; Dep = Depressive symptoms; Norm = normalization; Fam = Family Functioning; FS = Functional Status; TD1 = Ventilator dummy code;

TD2 = IV dummy code *p .05, **p .01 (2 – tailed)

Child’s Age.

As shown in Table 4.12 there were significant positive correlations between the

child’s age and duration of caregiving as well as amount of home health care nursing

hours. The older the child, the longer the duration of caregiving (r= .733, p<.01) and the

greater the number of home health care nursing hours required per week (r= .220, p<.05).

Additionally, age was significantly correlated in a positive direction with level of

technology dependence (r= .202, p<.05). The older the child, the more likely they were to

be classified as OTA (1987) Group 2 (intravenous medications or nutrition) than Group 1

(ventilators) or Group 3 (respiratory/nutritional support).

Race.

For purposes of the analysis, race was coded as 1=Caucasian (non-Hispanic) and

0=all others (Hispanic ethnicity, African American, Asian, Bi-racial). Mothers who

identified themselves as Caucasian of Hispanic ethnicity were grouped with the other

racial groups due to cultural differences with mothers in the Caucasian, non-Hispanic

group; many were immigrants and English was their second language. There was a

significant positive correlation between race and income (r= .360, p<.01); Caucasian,

non-Hispanic mothers reported significantly higher incomes than the other mothers in the

study. There was a significant negative correlation between race and normalization (r= -

.247, p<.05). Mothers who were of Hispanic ethnicity, African American, Asian, or Bi-

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racial had higher scores on the Normalization Subscale indicating greater normalization

efforts.

Income

A mother’s reported total family income was significantly correlated in a positive

direction with race (r= .360, p<.01) as noted above. There was also a significant negative

correlation of income with depressive symptoms (r= -.224, p<.05). Mothers with higher

total family incomes reported fewer depressive symptoms.

Duration of Caregiving

There was a significant positive correlation between duration of caregiving, and

both a child’s age and the amount of home health care nursing the child received per

week. The older the child the longer the duration of care (r= .733, p<.01). Also, the

longer the duration of care the greater the amount of home health care nursing the child

received per week (r= .236, p<.05). Duration of caregiving also had a significant positive

correlation with the level of technology dependence for the intravenous group compared

to all others (r= .257, p<.01) but not for the ventilator group. Children with a longer

duration of care were more likely to be classified as Group 2 (intravenous medications or

nutrition) than Group 1 (ventilators) or Group 3 (respiratory/nutritional support).

Amount of Home Health Care Nursing

There was a significant positive correlation between amount of home health care

nursing and duration of caregiving and the child’s age as noted above. Amount of home

health care nursing was also significantly correlated in a positive direction with level of

technology dependence (r= .417, p<.01). Children with a greater amount of home health

care nursing were more likely to be classified as OTA (1987) Group 1 (mechanical

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ventilation) than Group 2 (intravenous medications/nutrition) or Group 3

(respiratory/nutritional support).

Depressive Symptoms

There was a significant negative correlation between depressive symptoms and

income as noted above (r= -.224, p<.05). Additionally, there were significant negative

correlations between depressive symptoms and normalization efforts as well as the

child’s functional status. Mothers who reported a greater number of depressive symptoms

had lower scores on the Normalization subscale indicating less use of normalization (r= -

.453, p<.01) and had children with poorer scores on the Functional Status II-Revised

indicating poorer function (r= -.239, p<.05). There was a very strong significant positive

correlation between depressive symptoms and family functioning (r= .608, p<.01).

Mothers who reported a greater number of depressive symptoms had a higher

discrepancy score indicating poorer family functioning.

Normalization

There was a significant negative correlation between normalization and race (r= -

.247, p<.05) as well as depressive symptoms (r= -.453, p<.01) as noted above. Mothers of

non-Caucasian race or Hispanic ethnicity with fewer depressive symptoms reported

greater use of normalization. There was also a significant negative correlation between

normalization and family functioning (r= -.348, p<.01). Mothers who reported greater use

of normalization had lower discrepancy scores indicating better family functioning.

Additionally, functional status had a significant positive correlation with normalization

(r= .375, p<.01). Mothers who reported greater use of normalization had children with

better function.

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Family Functioning

There was a strong significant positive correlation between family functioning

and depressive symptoms (r= .608, p<.01) as noted above. Mothers with a higher

discrepancy score indicating poorer family functioning reported a greater number of

depressive symptoms. Additionally there was a significant negative correlation between

family functioning and normalization (r= -.348, p<.01). Mothers with lower discrepancy

scores indicating better family functioning reported greater use of normalization.

Functional Status

There was a significant negative correlation between the child’s functional status

and depressive symptoms (r= -.239, p<.05) as noted above. Mothers with children who

had higher functional status scores indicating better function reported fewer depressive

symptoms. Additionally, there was a significant positive correlation between the child’s

functional status and normalization efforts (r= .375, p<.01) as noted above. The higher

the child’s functional status score indicating better function the greater the use of

normalization efforts.

Level of Technology Dependence

In order for the categorical variable of level of technology dependence to be

included in the multiple regression analysis, two dummy variables were created to

represent the three levels of technology dependence (OTA Groups 1-3). Variable Tech.

Dependent 1= Group 1 (ventilators) versus all others and Tech Dependent 2= Group 2

(intravenous medications/nutrition) versus all others. There were significant positive

correlations between level of technology dependence OTA (1987) Group 2 and duration

of caregiving (r= .257, p<.01) as well as child’s age (r= .202, p<.05). Children who were

older and had a longer duration of care, more likely they were to be classified as OTA

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(1987) Group 2 (intravenous medications or nutrition) than Groups 1 or 2. Children who

were classified as Group 1 (ventilators) had a significantly greater number of home health

care nursing hours per week (r=.417, p<.01).

Regression Analysis – Family Functioning

A hierarchical multiple regression analysis was conducted to examine the

significance of the research hypotheses. Assumptions of normality, linearity,

homoscedasticity, independence, zero mean for residuals as well as absence of

measurement error, multicollinearity and influential data points were met.

For research question one, family functioning was regressed on mother’s

depressive symptoms, child’s severity of illness (functional severity and level of

technology dependence) and normalization efforts. As shown in Table 4.13 Model A,

depressive symptoms, child’s severity of illness and normalization accounted for 36.3%

of the adjusted variance in family functioning and achieved statistical significance (F=

12.172, p<.001). The only independent variable in the regression equation noted to be

significant according to the standardized beta was depressive symptoms ( = .56,

p=<.001). After all covariates (length of caregiving duration, amount of home health care

nursing hours, race, family income and age of the child who is dependent on technology)

were included in the regression model (Table 4.13 Model B) the adjusted variance

decreased slightly to 34.9%. Depressive symptoms continued to be the only significant

variable as indicated by the standardized beta ( = .585, p=<.001).

To address the hypothesis: “More depressive symptoms, greater severity of illness

and less normalization effort are related to poorer family functioning while controlling

for length of caregiving duration, amount of home health care nursing hours, race, family

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income and age of the child who is dependent on technology” the hierarchical regression

equation in Table 4.13 Model B was examined. There was a significant finding in relation

to depressive symptoms and family functioning in the direction proposed ( = .585,

p=<.001) therefore this portion of the hypothesis was supported. On the contrary, there

was no significant influence of severity of illness (functional status, level of technology

dependence) and normalization on family functioning therefore this portion of the

hypothesis was not supported.

Table 4.13. Summary of Regression Analysis for Family Functioning (N=99) Predictor Variable Model A Model B B B Constant 22.974 19.636 Normalization -.010 -.094 -.015 -.136 Functional Status -.001 .000 .115 .022 Depressive Symptoms 1.057 .560*** 1.103 .585*** Tech. Dependent: Ventilator 4.259 .078 5.774 .105 Tech. Dependent: IV 6.274 .087 7.211 .100 Race -4.826 -.100 Age of Child .059 .150 Duration of Caregiving -.060 -.132 Home Healthcare Nursing -.001 -.003 Family Income 1.867 .120 R2 .396 .415 Adjusted R2 .363 .349 F 12.172*** 6.243*** ***p .001

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In conclusion, the only variable in this regression equation that was significantly

related to family functioning was mother’s depressive symptoms; greater depressive

symptoms were related to poorer family functioning.

Research Question 2. Are there differences in a) family functioning and b)

normalization efforts and c) mother’s level of depressive symptoms based on the child’s

level of technology dependence (3 levels)?

Testing Assumptions for Analysis of Variance (ANOVA)

The following assumptions were examined prior to the ANOVA testing:

1) Instruments at Proper Level of Measurement. The instruments used to

determine the dependent variables (family functioning, normalization, depressive

symptoms) were continuous, interval level measurements and the independent variable (3

levels of technology dependence) was categorical with a minimum of two groups. This

assumption was met.

2) Normality. The variable residuals were examined using a histogram and

probability plot as previously discussed. The data assumed a normal distribution.

Additionally, with a large sample size such as in this study (N=103) the Central Limit

Theorem assumes normal distribution. This assumption was met.

3) Mutually Exclusive Groups. Levels of technology dependence were mutually

exclusive groups as determined by the OTA (1987) rubric therefore this assumption was

met.

4) Equality of Variances. This assumption was examined by determining the

Levene statistic. The Levene statistic was non-significant for each ANOVA meaning

equal groups were assumed. Since there were fewer than 20 participants (Tabachnick &

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Fidell, 2001) in two of the three groups, the Welch F-ratio and the Brown-Forsythe F-

ratio, more robust tests of equality of means were used (Fields, 2005).

5) Linearity. This assumption was tested by analyzing the partial regression

scatter-plots for each of the continuous study variables. The partial plots option assesses

the effect of each independent variable on the dependent variable controlling for all of the

other independent variables. It was determined that normalization was non-linear

therefore the variables were squared to overcome the violation of non-linearity. Since no

differences in results were noted, the original (non-squared) variable was used to

maintain a more parsimonious model.

6) Absence of Outliers. This assumption was tested by examining that the Cook’s

D for each of the dependent variables are not equal to or >1.0. This assumption was met.

Tests of Significance

Although each of the dependent variables (family functioning, normalization,

depressive symptoms) were correlated >.30 with one another, they were not correlated

>.30 with the independent variable (level of technology dependence). Individual

ANOVAs were performed for each of the dependent variables and results are shown in

Table 4.14.

Results on Table 4.14 include the means, standard deviations, ANOVA F

Statistic, Welch F Statistic and Brown-Forsythe F Statistic for ANOVA tests of each of

the dependent variables. The ANOVA tests examined if the between group differences

were greater than the within group differences. The F test was used to determine

significance at the .05 level. The F test for each of the dependent variables; family

functioning (F= 1.589, p= .208), normalization (F= 1.749, p= .362) and depressive

symptoms (F= .377, p= .687) was found to be non-significant. The Welch and the

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Brown-Forsythe Statistical tests for equality of means, alternative robust F-ratio

formulas, were employed to evaluate differences between the group means (Fields,

2005). Both the Welch F and the Brown-Forsythe F tests were non-significant for each of

the dependent variables indicating that there were no significant differences between the

levels of technology dependence (OTA Groups 1-3) for family functioning,

normalization and depressive symptoms (Table 4.14).

A Multiple Analysis of Variance (MANOVA) was performed with all three of the

dependent variables (family functioning, normalization, depressive symptoms) to reveal

differences not shown in separate ANOVAs and also to overcome any overall inflated

Type I error (Mertler & Vannatta, 2005; Tabachnick & Fidell, 2001). The result of the

Box’s Test was non-significant therefore, equal variances were assumed. Results from

the MANOVA were as follows: Wilk’s Lambda .959, F=.679, p=.667. Therefore, results

from the MANOVA were insignificant as well; there were no significant differences in

the means between the groups (level of technology dependence) for family functioning,

efforts at normalization and level of depressive symptoms.

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Table 4.14. Means, Standard Deviations and One-Way Analysis of Variance (ANOVA) for Three Levels of Technology Dependence and Three Dependent Variables

ANOVA Welch Brown-Forsythe Variable Group 1 Mechanical Ventilators

n=18

Group2 IV Nutrition and/or Meds.

n=9

Group 3 Resp/Nutrition

Support n=76

F

Statistic

p value

Statistic

p value

X SD X SD X SD Family Functioning 39.76 13.95 43.89 31.74 33.19 20.09 1.589ns 1.548 p=.240ns 1.130 p=.352ns

(df2, 98) Normalization 371.28 158.70 305.67 130.39 398.86 202.90 1.028ns 1.749 p=.198ns 1.586 p=.218ns

(df2, 100) Depressive Symptoms 14.61 9.94 16.89 10.11 13.61 11.47 .377ns .421 p=.662ns .445 p=.645ns

(df2, 100) Note: ns = non-significant

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Research Question 3. Do a mother’s depressive symptoms have a mediating effect

on the relationship between the child’s severity of illness (functional status) and (a)

normalization and (b) family functioning in mothers with a child who is technology

dependent?

The assumptions for multiple regression were met as previously discussed in

research question one: normality, linearity, homoscedasticity, independence, zero mean

for residuals as well as absence of measurement error, multicollinearity and influential

data points. Results of the Pearson r product-moment correlations were also examined

(Table 4.12). Additionally, the following assumptions for mediator testing were

examined prior to proceeding: 1) there is a significant direct effect of the independent

variable (functional status) on the dependent variable (normalization, family functioning)

and 2) there is no measurement error in the mediator- depressive symptoms (Baron &

Kenney, 1986; Bennett, 2000). Both of these assumptions were met.

Mediation Testing

A variable functions as a mediator when variation in an independent variable

account for variations in the proposed mediator (path a), variations in the mediator

account for significant variations in the dependent variable (path b) and finally when the

previous paths (paths a and b) are controlled, there is no longer a significant relationship

between the independent variable and the dependent variable (path c). The strongest

evidence of mediation is when this path is zero (Baron & Kenny, 1986).

Based on the study model, (Figure 1.2) the mediating effect of depressive

symptoms between the child’s functional status and normalization as well as family

functioning were examined. To test if depressive symptoms mediated the relationship

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between child’s functional status and normalization or family functioning three steps of

regression were used. If the mediating effect was present the following needed to occur:

(1) the independent variable (child’s functional status) is a significant predictor of the

mediator (depressive symptoms) as indicated by a significant F test (alpha .05), (2) the

independent variable (child’s functional status) is a significant predictor of the dependent

variable (normalization or family functioning) as indicated by a significant F test (alpha

.05), (3) the relationship between the independent variable and the dependent variable is

reduced when the mediator (depressive symptoms) is added, that is, there is a significant

drop in the -weight or the -weight for the independent variable becomes non-

significant.

Table 4.15. Mediating Effect of Depressive Symptoms between Functional Status and Normalization (N=103)

Variable Normalization Model A

Normalization Model B

B B Constant 5.406 192.214 Functional Status 17.725 .375*** 13.373 .283** Depressive Symptoms -6.638 -.385*** R2 .141 .281 Adjusted R2 .132 .266 R2 Change .134 ** p .01, ***p .001

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and accounted for 13.2% of variance in normalization. After the mediator (depressive

symptoms) was entered, the adjusted R square increased to 26.6% (Table 4.15 Model B).

Therefore, after depressive symptoms was added the incremental R squared change was

13.4%.

A test for mediation effect of depressive symptoms between functional status and

family functioning was conducted using the assumptions and techniques described above.

The Pearson’s product moment correlation table (Table 4.12) was examined and it was

determined that functional status was not significantly correlated with family functioning

therefore depressive symptoms could not be a mediator between functional status and

family functioning. Table 4.16 and Figure 4.2 represent the test for mediation steps

followed.

Table 4.16. Mediating Effect of Depressive Symptoms between Functional Status and Family Functioning (N=101)

Variable Family Functioning Model A

Family Functioning Model B

B B Constant 53.781 21.373 Functional Status -.864 -.168 -.107 -.021 Depressive Symptoms 1.128 .603*** R2 .028 .371 Adjusted R2 .018 .358 R2 Change .340 *** p .001

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Table 4.18. Mediating Effect of Normalization between Depressive Symptoms and

Family Functioning (N=101) Variable Family Functioning

Model A Family Functioning

Model B B B Constant 18.944 24.292 Depressive Symptoms 1.138 .608*** 1.056 .564*** Normalization -.011 -.100 R2 .370 .378 Adjusted R2 .364 .366 R2 Change .002 *** p .001

A test for mediation effect of normalization between depressive symptoms and

family functioning was conducted. The Pearson’s product-moment correlation matrix was

examined (Table 4.12) and it was determined that all three variables were significantly

correlated therefore mediation testing could proceed. Three conditions to test for a

mediating effect of normalization between functional status and family functioning were

as follows (Table 4.18, Figure 4.4): (1) the mothers depressive symptoms had a

significant relationship with normalization ( = -.453, p = <.001) that is an increased

number of depressive symptoms was related to less use of normalization, (2) on the

second regression, depressive symptoms explained 36.4% of the variance in family

functioning, increased depressive symptoms was related with higher family functioning

discrepancy score ( = .608, p= <.001) indicating poorer family functioning and the

equation was significant (Baron & Kenney, 1986) (Table 4.18 Model B). (3) when both

depressive symptoms and normalization were entered together the -weight for

depressive symptoms decreased ( = .564, p= <.001) however the level of significance

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Research Question 5a. What are the relationships among mother’s depressive

symptoms, child’s severity of illness (functional status, level of technology dependence),

family functioning on normalization efforts in families with a child who is technology

dependent? 5b. Do these relationships hold after adjusting for length of caregiving

duration, amount of home health care nursing hours, race, family income and age of the

child who is dependent on technology?

Less depressive symptoms and severity of illness, Caucasian (non-Hispanic race),

longer caregiving duration, greater amount of home health care nursing hours and a

younger child were hypothesized to be significantly related to greater efforts at

normalization while controlling for length of caregiving duration, amount of home health

care nursing hours, race, family income and age of the child who is dependent on

technology.

A Pearson’s product moment correlation coefficient matrix comprising all study

variables and covariates (Table 4.12) was examined to determine if any significant

relationships existed. The assumptions of normal distribution and level of measurement

were met. The value of statistical significance was set at alpha .05.

Multiple Regression Analysis - Normalization

A hierarchical multiple regression analysis was conducted to examine the

significance of the research hypotheses. Assumptions of normality, linearity,

homoscedasticity, independence, zero mean for residuals as well as absence of

measurement error, multicollinearity and influential data points were met. Normalization

was regressed on mother’s depressive symptoms, child’s severity of illness (functional

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Table 4.19. Summary of Regression Analysis for Normalization (N=99) Predictor Variable Model A Model B B B Constant 224.801 240.903 Family Functioning -1.013 -.112 -1.242 -.137 Functional Status 12.928 .275** 14.629 .312*** Depressive Symptoms -4.944 -.289* -4.204 -.246* Tech. Dependent: Ventilator -31.183 -.063 16.070 .034 Tech. Dependent: IV -66.959 -.103 -87.049 -.134 Race -133.185 -.305** Age of Child 1.177 .330** Duration of Caregiving -.511 -.122 Home Healthcare Nursing -.825 -.193* Family Income 4.917 .035 R2 .284 .410 Adjusted R2 .246 .343 F 7.381*** 6.111*** *p< .05 **p< .01 ***p .001 status and level of technology dependence) and family functioning. As shown in Table

4.19 Model A, depressive symptoms, child’s functional status and family functioning

accounted for 24.6% of the adjusted variance in normalization and achieved statistical

significance (F= 7.381, p<.001). Two independent variables in the regression equation

were significant: depressive symptoms ( = -.289, p=<.05) and functional status of the

child ( = .275, p=<.01). After all covariates (length of caregiving duration, amount of

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home health care nursing hours, race, family income and age of the child who is

dependent on technology) were included, the adjusted variance of the regression model

(Table 4.19 Model B) increased to 34.3% with a R square change of 12.6%. Depressive

symptoms remained a significant variable in the equation however the addition of the

covariates caused the standardized beta to drop ( = -.246, p= .030). Functional status

continued to be significant and the standardized beta increased with the addition of the

covariates ( = .312, p= .001). Three covariates added to the explained variance in the

regression model (Table 4.19 Model B); amount of home health care nursing ( = -.193,

p= <.05), child’s age ( = .330, p= <.01) and race ( = -.305, p= .001).

To address the hypothesis: “Less depressive symptoms and severity of illness,

Caucasian (non-Hispanic ethnicity), longer caregiving duration, greater amount of home

health care nursing hours and a younger child are significantly related to greater efforts at

normalization while controlling for length of caregiving duration, amount of home health

care nursing hours, race, family income and age of the child who is dependent on

technology” the hierarchical multiple regression equation in Table 4.19 Model B was

examined. There was a significant finding in relation to depressive symptoms ( = -.246,

p= <.05) as well as functional status ( = .312, p= .001) and normalization in the direction

proposed therefore these hypotheses was supported. On the contrary, level for technology

dependence was not significantly related to normalization efforts in the direction

proposed for either Group 1 ( = .034, p= .739) or Group 2 ( = -.134, p= .139) as

compared to Group 3 therefore the hypothesis regarding severity of illness was only

partially supported. Better functional status but not level of technology dependence was

significantly related to greater normalization efforts. Caucasian (Non-Hispanic) race was

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not significantly related to greater efforts at normalization. The opposite was true;

Hispanic ethnicity and other races were significantly related to greater efforts at

normalization in this study ( = -.305, p= .001). This hypothesis was not supported.

Duration of caregiving was not significantly related to greater efforts at normalization

( = -.122, p= .341). This hypothesis was not supported. The amount of home health care

nursing hours was significantly related to efforts at normalization but contrary to the

hypothesis, decreased amount of home health care nursing hours was significantly related

to greater efforts at normalization ( = -.193, p= <.05). This hypothesis was not

supported. The age of the child was significantly related to efforts at normalization but

contrary to the hypothesis, older age of the child was significantly related to greater

efforts at normalization ( = .330, p= .009). This hypothesis was not supported.

Therefore the variables that were significantly related with greater normalization

efforts include: better functional status, less depressive symptoms, Non-Caucasian race or

Hispanic ethnicity, less home health care nursing hours and older age of the child who is

technology dependent.

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CHAPTER FIVE - DISCUSSION

Introduction

Few quantitative studies have examined the impact of a child who is technology

dependent on their families yielding little evidence to guide delivery of care for this

population. This study explores how families (n=103) manage the challenges of caring

for a child who is technology dependent at home following discharge from the hospital.

This chapter includes a summary of the study findings, significance for nursing research

and practice as well as an interpretation of the findings based on prior empirical and

theoretical literature. Limitations of the study are discussed and future research

recommendations, based on study findings, are suggested.

Summary

This study examined how families respond to and manage the special challenges

of children who are technology dependent after they have been discharged from the

hospital to home. Participants of this study included 103 mothers who cared for their

children aged 7 months-16 years who are technology dependent at home for a minimum

of 2 months. The purpose of this study was to examine the relationship between

child/maternal factors (child’s functional status, level of technology dependence,

mother’s depressive symptoms, length of caregiving duration, amount of home health

care nursing hours, race, family income and age of the child) and (a) family functioning

as well as (b) normalization in families with a child who is technology dependent.

Additionally, this study examined whether there are differences in family functioning,

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normalization and depressive symptoms based upon the child’s level of technology

dependence (Group 1 mechanical ventilation, Group 2 intravenous nutrition/medication,

Group 3 respiratory/nutritional support) using the Office of Technology Assessment

(1987) rubric. The orienting frameworks for this study were the Family Management

Style Framework by Knafl and Deatrick (2003) and Paterson’s “Shifting Perspectives

Model of Chronic Illness” (2001).

Convenience sampling was used to recruit mothers (n=103) primarily from

Rainbow Babies and Children’s Hospital (RB&C), a large children’s hospital in

Cleveland, Ohio from July 2007-November 2008. A majority of mothers lived in the

Northeastern Ohio area; a few were recruited from bordering states served by the tertiary

care hospital. The number of participants in the study was adequate based upon an a

priori power analysis that included the effect size, alpha and power of greater than .80 to

decrease the chance of a Type II error. Face-to-face interviews were conducted in the

RB&C outpatient clinics or in a private place of the mother’s choosing. In addition to a

demographic questionnaire five instruments were included in the data collection

interview: the Functional Status II-Revised (Stein & Jessop, 1990), Level of Technology

Dependency Questionnaire, the Center for Epidemiological Studies-Depression Scale

(Radloff, 1977), “Actual Effect of a Chronic Physical Disorder on the Family” subscale

of the Normalization Scale (Murphy & Gottlieb, 1992) and the Feetham Family

Functioning Survey (Roberts & Feetham, 1982).

Statistical analyses were performed to answer the research questions. A Pearson

product-moment correlation matrix was developed to evaluate the relationship between

the variables. Normalization, family functioning and depressive symptoms were all

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significantly correlated with one another. Additionally, functional status was significantly

correlated with normalization and depressive symptoms; income with depressive

symptoms and race with normalization. Hierarchical Multiple Regression analyses were

used to evaluate the percentage of explained variance as well as the relationship of

independent variables and covariates with the outcome variables family functioning and

normalization. Additionally, mediating effects of normalization on family functioning as

well as mediating effects of mother’s depressive symptoms on normalization and family

functioning were examined using multiple regression analyses. Analysis of Variance

(ANOVA) was performed to examine differences in normalization efforts, family

functioning and mother’s depressive symptoms based on the child’s level of technology

dependence.

Findings from this study indicate that mothers of children who are technology

dependent are at high risk for psychological distress that can affect overall family

functioning. A mother’s depressive symptoms was the only significant predictor of

family functioning while controlling for covariates (length of caregiving duration,

amount of home health care nursing hours, race, family income and age of the child who

is dependent on technology) in Hierarchical Multiple Regression analysis; greater number

of depressive symptoms was related to poorer family functioning. Using Hierarchical

Multiple Regression Analysis, several independent variables/covariates were found to be

significant predictors of greater normalization efforts: better child’s functional status, less

depressive symptoms, fewer home health care nursing hours per week, an older child and

Non-Caucasian race or Hispanic ethnicity. ANOVA analyses indicate there were no

statistical differences in normalization efforts, family functioning and mother’s

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depressive symptoms based on the child’s level of technology dependence. Statistical

analyses for mediation (Baron & Kenney, 1986) revealed that depressive symptoms are a

mediator between the child’s functional status and normalization.

Findings

Characteristics of Participants

Statistical analyses of mother’s individual characteristics showed that while the

average age of the mothers was 38 years old (range 21-66 years) there was a wide age

variation; approximately 10% were 51 years or older. The majority of mothers were

Caucasian, non-Hispanic (73%) however the percentage of minority participants modeled

the demographics of Northeastern Ohio. Mothers possessed a wide variation of

educational backgrounds; the majority (68.9%) had at least a partial college education.

Most participants (85.4%) were the biological mother of the child but a surprising finding

was the number of adoptive/foster mothers and grandmothers who cared for these

children. All of the adoptive and foster mothers had full knowledge that the child was

technology dependent and had multiple special health care needs prior to establishing the

caretaking relationship. A majority of mothers were married (70.9%). About half of the

participants (50.5%) had a yearly total family income that was $60,000 or less however

19.4% earned $80,001 or more and 11.6% had incomes less than the federal poverty level

for a family of four. Almost half of participants (45.6%) were not employed outside the

home while 27.2% worked 40 hours or more per week. No data were collected in this

study regarding employment status prior to caring for the child who was technology

dependent however. Many mothers who were not employed outside of the home

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indicated that this was their full time job; coordinating care, performing multiple care

tasks and transporting their child to a variety of health care and therapy appointments.

Characteristics of Participant’s Children

The mean age of the children was 6.58 years (range 7 months-16.6 years);

approximately half were age 5 years or less (47.6%); a finding consistent with the

literature. According to Houtrow, Kim and Newacheck (2008), children with special

health care needs ages 5 years or less had 3.6 times the hospital days and 100 times more

home health care days as their peers with the highest decile of these children accounting

for 74% of medical expenditures for this age group. Undoubtedly, the highest decile

group referred to are those children who are technology dependent. There were an equal

number of each gender. Only one article was found that described the demographics and

characteristics of children who are technology dependent in England (Kirk, 2008); none

were found that described this population in the United States. Most of the available

literature described children with special health care needs in general.

About half (49.5%) of the children had a primary diagnosis that was due to a

genetic disorder and 27.1% were born preterm at 36 weeks or less. The largest percentage

of children had diagnoses related to neuromuscular dysfunction (43.7%) or respiratory

dysfunction (22.3%).

A majority of children (73.8%) were categorized as belonging to OTA (1987)

Group 3 and required respiratory or nutritional support such as oxygen, feeding tube or a

tracheostomy while others were categorized as Group 1 (17.5%) requiring mechanical

ventilation or Group 2 (8.7%) requiring intravenous medications or nutritional

substances. This finding is characteristic of the population of these children as fewer

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children require mechanical ventilation or intravenous medications/nutrition. About half

required one type of technology (49.5%) however some children (12.6%) required up to

four types of technology simultaneously. Almost all (88.7%) of the children had either a

nasogastric tube (1.9%) or gastrostomy tube (86.4%). Other frequently required

technologies included oxygen or a tracheostomy tube. A total of 42.7% required oxygen

on at least an as-needed basis via nasal cannula, face mask, trach collar, continuous

positive airway pressure and 31.1% required a tracheostomy tube. Less frequently

required technologies were mechanical ventilators (17.5%) or intravenous

medication/nutritional substances (9.7%).

Caregiving Variables

The average caregiving duration was 57 months (range 2-161 months) and

included a fairly even distribution in each of the categories (Table 4.6). This data points

to the fact that many of these children do remain dependent on technology for significant

periods of time. The high level of care required by these children over the long term can

be taxing on the mother’s mental health and consequently family functioning. The

average amount of home health care nursing that the children received was 41.23 hours

per week (range 0-144) however; a large proportion of children (36.8%) did not receive

any home health care nursing (Table 4.6). Therefore, while some mothers received

respite others were not as fortunate either because they were unable to find qualified

nursing help or they were not eligible to receive such services due to insurance or had

been denied enrollment in the state funded Medicaid waiver program. Children who

received the greatest number of home health care nursing hours were those children who

were categorized as Group 1 (mechanical ventilators) and Group 2 (intravenous

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medications/nutritional substances). These children often required more frequent and

complex care and treatments and therefore were often enrolled in the Medicaid waiver

program.

Depressive Symptoms

Mothers in this study reported higher levels of depressive symptoms compared to

the general population (Radloff, 1977) as measured by the CES-D. In this study, 39.8%

of mothers scored at or above 16 (cutoff established indicating risk for clinical

depression) on the CES-D as compared to 21% of the general population (Radloff, 1977)

and up to 45% in previous studies of mothers of children who are technology dependent

(Kuster & Badr, 2006; Miles et al., 1999). The mean CES-D score of 14.07 (SD 11.05)

for women in this study was similar to previous findings in studies of mothers with

children who are technology dependent where mean scores ranged from 13.6-15.5

(Heyman et al., 2004; Kuster & Badr, 2006; Miles et al., 1999). While prior studies

indicated the percentage of mothers who scored 16 or greater on the CES-D, none

indicated the percentage that scored above a more critical level of 21 or greater (Myers &

Weissman, 1980). In this study, 15.5% scored 16-20 while 24.3% scored equal to or

greater than 21 on the CES-D indicating a higher risk for clinical depression. Therefore,

women in this study were identified as at risk for clinical depression at approximately

double the rate of the general population (21% vs. 40%). Clearly, a large proportion of

these mothers are at risk for clinical depression and many (24%) are at a significant risk

due to their very high levels of depressive symptoms.

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Depressive Symptoms and Severity of Illness

Correlational analysis revealed that depressive symptoms had a significant

negative correlation with the child’s functional status which is consistent with prior

studies of children with chronic illness (Silver et al., 1998; Silver et al., 1995; Frankel &

Wamboldt, 1998; Lustig et al., 1996; Weiss & Chen, 2002). On the contrary, other

researchers did not find a relationship between depressive symptoms and severity of

illness however standardized instruments were not used to measure severity of illness

(Affleck, 1987; Miles et al., 1999; Thompson et al., 1993).

While functional status was significantly correlated with a mother’s depressive

symptoms, findings from this study, both Pearson Product-Moment Correlations and

ANOVA, indicate that the level of technology dependence was not. Mothers of children

with mechanical ventilation or intravenous medications/nutrition did not have a greater

number of depressive symptoms as compared to mothers of children with respiratory or

nutritional support. This is consistent with findings from previous studies (Fleming et al.,

1994) and may be due in part to the mother’s perception and definition of the situation.

What may seem overwhelming and tragic to one mother regarding treatment and care for

her child may be viewed as manageable to another.

Depressive Symptoms and Family Income

A mother’s depressive symptoms had a significant negative correlation with

family income which is consistent with prior research (Canning et al., 1996; Drotar et al.,

1997; Shore et al., 2002). Lower family income was related to a mother’s increased

depressive symptoms. Income however was not a significant predictor of normalization

or family functioning in the multiple regression analyses.

215

Depressive Symptoms and Family Functioning

In this study, depressive symptoms had a strong positive correlation with family

functioning; greater number of depressive symptoms was related to a higher family

functioning discrepancy score indicating poorer family functioning. Furthermore,

hierarchical multiple regression analysis indicated (Figure 5.1) that a mother’s level of

depressive symptoms was the only significant predictor ( = .585, p=<.001) of family

functioning while controlling for the child’s severity of illness (functional status, level of

technology dependence), normalization efforts as well as the covariates (caregiving

duration, amount of home health care nursing hours, race, family income and child’s

age). A mother’s level of depressive symptoms therefore accounted for 34.9% (F=6.243,

p<.001) of the variance in family functioning (Table 4.10).

This study’s findings regarding a strong relationship between depressive

symptoms and family functioning is consistent with previous research (Frankel &

Wamboldt, 1998; Ireys and Silver, 1996; Kuster et al., 2006). Past research indicates that

decreased satisfaction with family relationships, an indicator of family functioning, is

significantly related to greater depressive symptoms (Shore et al., 2002; Weiss & Chen,

2002). Frankel and Wamboldt (1998) found that the Psychiatric Symptom Index

predicted 39% of the variance in the Impact On Family Scale, a measure of family

functioning. The researchers propose that how a child’s illness interferes with and

impacts a family is determined by the psychological health of the primary parent

caretaker. The psychological health of the parent is in turn affected by family functioning

and the child’s functional status (Frankel & Wamboldt, 1998). Ireys and Silver (1996)

also found that family functioning accounted for a large percentage of variance on a

216

mental health measure (p=<.001) even after controlling for other variables. Therefore, a

mother’s psychological state, particularly the amount of depressive symptoms, plays a

significant role in how a family with a child who is technology dependent functions.

Age

Race

Amt. ofHHC Nsg

NormalizationR2 = .34

.585 ***-.2

46 *

FamilyFunctioning

R2 = .35

Mother'sDepressiveSymptoms

Child'sSeverity of

Illness

.312 ***.330 **

-.305 **

-.193 *

-.239

*

* p< .05 **p<.01 ***p<.001

Figure 5.1. Study Model for Normalization and Family Functioning in

Families with a Child who is Technology Dependent

Depressive Symptoms and Normalization Efforts

Depressive symptoms also had a strong negative correlation with normalization

efforts in this study; mothers with greater depressive symptoms made fewer

normalization efforts (Figure 5.1). Normalization efforts and depressive symptoms of

mothers have not been measured together to date in any previous study. Past research

217

regarding caregivers of children who are technology dependent has indicated a significant

level of depressive symptoms. Some of the depressive symptoms such as sleep

disturbances or inability to concentrate could affect performance of complex

technological procedures (Smith, 1999) and consequently compromise the care of her

child who is technology dependent as well as any typically developing siblings in the

household. Additionally, depressive symptoms can influence a mother’s definition of the

situation, one of the major components of normalization (Knafl & Deatrick, 2003).

Therefore, a mother’s psychological state plays an important role in normalization efforts

that can promote the integration of the child who is technology dependent and the care

required into the family and family life.

In conclusion, a mother’s depressive symptoms have a significant influence on a

family’s functioning and are a major predictor of how much a family is affected by the

child’s chronic illness and dependence on technology. Additionally, depressive

symptoms significantly affect a mother’s normalization efforts.

Normalization

Normalization had a significant negative correlation with race, depressive

symptoms and family functioning as well as a significant positive correlation with the

child’s functional status. Non-Hispanic, Caucasian mothers, greater depressive

symptoms, poorer family functioning and a child’s poorer functional status were all

significantly related with less normalization efforts (Figure 5.1). Greater normalization

indicates greater integration of the child and his/her care into every day family life and is

viewed as a desirable goal for families of children with chronic illness (Knafl & Deatrick,

2003).

218

Normalization and Race

In this study, normalization was significantly correlated with race; mothers who

were of Hispanic ethnicity, African American, Asian or Biracial had significantly greater

efforts at normalization. There have been few previous studies regarding normalization

but to date none have found a relationship between race and normalization (Knafl et al.,

1996). One quantitative study regarding normalization did not specifically examine the

relationship between race and normalization (Murphy, 1994). Many of the studies were

qualitative with small samples and did not describe the ethnicity of participants and in

those that did, the majority was Caucasian.

Normalization and Child’s Age

Normalization and child’s age was significantly correlated in this study.

Participants with older children used greater normalization efforts. This is contrary to

previous studies. Knafl and Deatrick (2006) stated that one of the barriers to

normalization in children with chronic illness was older age of the child; parents had a

greater difficulty with normalization efforts particularly if the child was an adolescent.

Qualitative studies that examined normalization efforts with children who are technology

dependent (Carnevale et al., 2006; Judson, 2004; Lee, 1996; Morse et al., 2000; Rehm &

Bradley, 2005) did not examine the effect of a child’s age on normalization efforts.

Normalization and Depressive Symptoms

Normalization also had a significant negative correlation with depressive

symptoms; mothers in this study with increased depressive symptoms used less

normalization efforts. According to Clark et al. (1999), depression, and even sub-clinical

219

depressive symptoms, are associated with significant impairment in psychosocial and

physical functioning and well being and could therefore affect normalization.

Furthermore, depressive symptoms can influence a mother’s definition of the situation,

one of the major components of normalization (Knafl & Deatrick, 2003). Having a

negative view of the situation could thus influence and lessen the amount of

normalization effort. No previous research has specifically examined the relationship

between a parent’s normalization efforts and their own psychological well-being.

Normalization and Family Functioning

Normalization also had a significant negative relationship with family

functioning; greater normalization efforts were related to lower family functioning

discrepancy scores indicating better family functioning. Knafl and Zoeller (2000) also

found that parents with greater normalization efforts had better family functioning. The

researcher hypothesized that with greater normalization efforts families identify illness

management strategies that help with family functioning (Knafl & Zoeller, 2000).

Greater normalization efforts have been found to be related to greater flexibility in

carrying out treatments so that the illness care is incorporated into family routines thus

improving family functioning (Gallo & Knafl, 1998; Knafl & Deatrick, 2002; Knafl &

Gilliss, 2002; Thorne et al., 1997; Wilson et al., 1998). Additionally, care for the child

who is technology dependent, if fit around other family activities, will facilitate family

functioning. Families learn “tricks of the trade” to facilitate this process of flexibility

(Gallo & Knafl, 1998) and routinization of the treatment regimen (Knafl & Gilliss, 2002).

Rules were broken to deliver safe care but yet “normalize” childhood by giving

experiences similar to other children while at the same time maintaining family

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functioning (Wilson et al., 1998). Families who used less normalization effort in previous

studies were noted to have difficulties related to strictly adhering to the treatment

regimen, saw the treatment regimen as the focus of family life and a burden that made

their family different from other families (Knafl & Deatrick, 2002). Therefore, families

who incorporated flexibility in adherence to medical regimen used greater normalization

efforts which consequently led to improved family functioning.

Normalization and Severity of Illness

In this study, both functional status and level of technology dependence were

included in measurement of the concept, severity of illness. Only functional status was

found to have a significant correlation with normalization; greater normalization efforts

were related to better functional status. This is consistent with previous qualitative study

findings (Dashiff, 1993; Knafl & Deatrick, 2002). Additionally, Lee (1996) found that

mothers reported increased normalization efforts with their child’s decreased dependency

on technology, ie. less dependent on oxygen.

Components of the illness that have been found to interfere with and hinder

normalization efforts in children with chronic conditions include illness control,

uncertainty or unpredictability of the illness (Cohen, 1993; Haase & Rostad, 1994;

O’Brien, 2001) and visibility of the condition (Murphy, 1994). Knafl & Deatrick (2002)

found that with exacerbations and flare-ups of the child’s disease, there was dissolution

of normalization even in parents who described themselves as competent and confident

caregivers and managers of the illness. The illness exacerbation made the illness

management burdensome and the focus of family life; as in Paterson’s Model of Shifting

Perspectives (2001), brought the disease to the foreground. Conversely, as the illness

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improved or became better controlled, parents shifted to normalization in their family life

and increased their normalization efforts. While the illness was in remission or was well

controlled, the illness management was taken for granted and became part of the family

routine thus normalizing efforts were increased (Knafl & Deatrick, 2002).

Researchers who studied families with children who were technology dependent

also noted a link between increased severity of illness and decreased normalization

efforts. In a qualitative study of families with ventilator dependent children, Carnevale et

al. (2006) found that normalization efforts were undermined by the unpredictability of the

child’s condition that was complicated and at times overwhelming. All of the families

devoted significant effort toward normalizing the experience by creating routines so that

their lives would resemble that of “normal” families.

Rehm and Bradley (2005), on the contrary, state that based on their qualitative

study, families with children who are technology dependent and developmentally delayed

do not meet the established attributes of normalization. Most notable was the conclusion

that the “normalcy lens” in these families was impossible. The researchers propose that

since the child’s condition required constant vigilance and frequent skilled intervention,

parents felt that they needed to continuously prioritize the care of the child and strove for

stability for all family members but did not see life as “normal”. Additionally, due to the

fragility of the child’s condition, particularly since they were both technology dependent

and developmentally delayed, parents were not able to modify the treatment regimen very

much nor were they able to count on a predictable routine that would aid in normalization

efforts. Due to the visibility of equipment and appearance of the child, parents felt

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conspicuous in public and frequently experienced distress due to discrimination (Rehm &

Bradley, 2005).

Families of children who are technology dependent experienced many limits in

their routines and activities related to the child’s chronic condition. The equipment, time

required for treatments and care, the need for trained caregivers all affected normalization

efforts as well as the child’s ability to participate in normal childhood and family

experiences and socialization with peers (O’Brien & Wegner, 2002). The reliance on

technology also affected daily activities due to the time and planning required even for a

simple outing. Therefore, families of children who have poorer functional status,

particularly children who are technology dependent with poor functional status, have

more difficulty with normalization.

Hierarchical Multiple Regression Analysis of Normalization

Two independent variables in the regression equation were significant: depressive

symptoms and functional status of the child explained about 25% of the variance in

normalization. These findings are consistent with past research as discussed above;

depressive symptoms and functional status of the child are predictors of normalization

efforts. Following the addition of the covariates (length of caregiving duration, amount of

home health care nursing hours, race, family income and age of the child who is

dependent on technology) on the second step of the regression analysis, the amount of

explained variance increased to 34.3%. Three covariates were significant and added to

the explained variance; amount of home health care nursing, child’s age, and race. Less

home health care nursing hours, older child and those who identified themselves as

African-American, Asian, Bi-racial or of Hispanic ethnicity were significantly related

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with increased normalization efforts. The remaining variance that has not been explained

in the regression model could be due to strain experienced by these mothers that may

hinder normalization efforts or as Rehm and Bradley (2005) suggest, the presence of

developmental delay. Additionally, as Knafl and Deatrick (2003) indicate, mutuality

between both mother and father regarding management strategies helps promote

normalization while increased conflict decreases normalization efforts.

With regard to the home health care nursing hours, those children who require

more hours are those children who have a poorer functional status and more complex care

needs such as ventilators and intravenous medications/nutrition. Therefore, there are two

plausible explanations for the relationship between amount of home health care nursing

hours and normalization. One possibility is that it is actually the functional status of the

child and complexity of care needs that determine the amount of nursing hours; children

with more complex care needs qualify for more hours. It can then be proposed that it is

the functional status and not necessarily the home health care nursing hours that exert the

greatest influence on normalization efforts.

Another explanation for the relationship between nursing hours and normalization

is that an increased amount of nursing hours is disruptive to the family routine and limits

the amount of normalization efforts. Past research presents conflicting results regarding

the use of home health care nursing with children who are technology dependent. Nurses

in the home were seen as supportive yet disruptive (Wang & Barnard, 2004) as their

presence affected privacy and family dynamics (Obrien et al., 2002) such as marital,

sibling and parental relationships (Murphy, 1997). Knafl and Deatrick (2002) noted that

the major barrier to normalization is parental conflict and not illness management

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because the continual tension keeps the illness a focus of family life. Furthermore, lack

of consistent, high quality nursing care contributed to family disorganization (Tommet,

2003). Interestingly, while children with ventilators (Group 1) required more home health

care nursing hours than children with intravenous medications/nutrition (Group 2) or

respiratory/nutritional support (Group 3) there were no significant differences between

groups in normalization efforts. To date, no previous studies have been found that

examined amount of home health care nursing hours and normalization efforts.

The findings from the model regarding contributors to normalization suggest that

families with older children who are technology dependent have significantly greater

normalization efforts. This finding is contrary to previous studies as noted above.

Races/ethnicity other than Caucasian, Non-Hispanic was noted to have greater

normalization efforts in this study. While no past research has examined the relationship

between race and normalization, the literature suggests that African American caregivers

are resilient despite the negative effects of caregiving and displayed higher levels of

resourcefulness in the face of adversity (Gonzalez, 1997; Zauszniewski & Wykle, 1994).

These findings from past studies help to illuminate possible reasons for differences in

normalization efforts between Caucasian, Non-Hispanic mothers and other racial/ethnic

groups.

Normalization, while not an entirely new concept has only been measured

quantitatively in one previous study (Murphy, 1994). According to Knafl and Deatrick

(2002), a family’s definition of the situation influences management behaviors; both of

these, then, ultimately affect normalization efforts. In this study, the child’s functional

status and mother’s depressive symptoms were the most important predictors of

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normalization efforts. This is consistent with previous literature that complexity of care is

related to how much families are able to normalize particularly if the child’s condition is

fragile and unpredictable (Carnevale et al., 2006). Parents are thus unable to establish a

routine, a key component of normalization. Furthermore, a child’s functional status will

determine how much a family’s lifestyle may be limited particularly if there is little room

for flexibility with treatments and management behaviors; an important component of

normalization. Additionally, a mother’s response to the child who is technology

dependent is shaped by their definition of the situation. Increased depressive symptoms

experienced by a mother and actual severity of illness affect their definition of the

situation thus management behaviors and consequently normalization.

Contrary to previous studies, older age of the child was related to increased

normalization efforts. Others proposed that with increasing age there is increased

pressure for conformity to peer groups. This did not appear to be the case in this study of

children who are technology dependent. Mothers were well aware of their child’s

differences from other children but sought to include them in as many age appropriate

activities as possible including attending school with a ventilator, intravenous

medications and feeding tubes and attending sporting events, camps and including them

on family vacations.

As an adjunct to this study, a qualitative question was posed. Many mothers

described how they problem-solved how to transport their child and their equipment and

supplies to appointments, family outings and vacations. Others described various

organizational strategies such as charts, notebooks and computer spread sheets to keep

track of the child’s supplies, physicians, therapists, surgeries, treatments, medications and

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appointments. All of these activities point to management strategies employed by

mothers that were a part of their normalization efforts. An interesting finding was that

mothers who scored low on normalization efforts were unable to name an example of

how they were able to problem solve to continue living a “normal family life”; rather,

they discussed how the child’s condition was an obstacle or a barrier to being able to

carry on a normal family life.

Family Functioning

Family Functioning was significantly correlated with only two variables in this

study: mother’s depressive symptoms and normalization efforts. The relationship of both

of these predictors to family functioning was discussed above.

Hierarchical Multiple Regression Analysis osf Family Functioning

Interestingly, in contrast to normalization that had a number of predictors,

depressive symptoms was the sole predictor of family functioning in the regression

equation and accounted for a large proportion of explained variance (34.9%) even after

the addition of covariates (length of caregiving duration, amount of home health care

nursing hours, race, family income and age of the child who is dependent on technology).

Greater number of depressive symptoms was related to poorer family functioning.

Similar to past research, those mothers with greater depressive symptoms reported that

their child’s condition and technology dependence was disruptive and had a major impact

on family life, more specifically family functioning. The findings from the hierarchical

multiple regression analyses for family functioning suggest that a mother’s depressive

symptoms influence the mother’s perception of the illness or “definition of situation” as

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proposed by Knafl and Deatrick’s (2003) Family Management Style Model and

consequently affect family functioning and helps to explain how families develop the

“normalcy lens”.

Results from this study also validate Paterson’s (2001) Shifting Perspectives

Model of Chronic Illness. A mother’s perception of reality is how the mother interprets

and responds to the illness. When a mother has “wellness in the foreground” the child is

seen as well or “normal” even if numerous technologies and treatments are necessary for

the child. This is not a distortion of reality or denial of the child’s fragile condition, but

rather a revision of what is “normal”. The goal of normalization is to allow the child who

is technology dependent to be integrated into the family and not to be the central focus.

This was evident in several cases when the mother scored high in normalization efforts.

Although normalization was significantly correlated with family functioning it was not a

significant predictor of family functioning in this analysis.

In summary, findings from the hierarchical multiple regression analyses for

normalization and family functioning suggest that a mother’s depressive symptoms and a

child’s functional status strongly influence the mother’s “definition of situation” and play

an important role in perception of the impact of the illness on normalization efforts and

family functioning. Since Group 3 (Respiratory/Nutritional Support) was such a large

group further analysis was performed to divide the children into two groups; children

with/without tracheostomies. The results from the ANOVA analysis with 4 groups (levels

of technology dependence) continued to show no difference for amount of depressive

symptoms. Therefore, results from ANOVA testing help to substantiate that the type of

technology the child uses (ventilator, intravenous medications/nutrition,

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respiratory/nutritional support) is not a significant predictor of family functioning,

normalization efforts or a mother’s depressive symptoms.

Mediator Testing

Four separate analyses were performed in research questions 3-4 to test if

depressive symptoms and normalization functioned as mediators in this study. Mediators

help to give additional information regarding how or why variables are strongly

associated (Baron & Kenney, 1986) and show a process. Using mediation analysis

techniques as described by Baron and Kenney (1986), only a mother’s depressive

symptoms were found to have partial mediation effects between a child’s functional

status and normalization. Poorer functional status and greater depressive symptoms were

related to fewer normalization efforts. In hierarchical multiple regression of

normalization, the predictor variable, functional status, had the strongest, most significant

influence on the outcome variable, normalization (p=<.001) due to the presence of

depressive symptoms as a mediator. While no quantitative data could be found regarding

the mediation effects of depressive symptoms between functional status and

normalization, many researchers have described the significant relationship of a child’s

severity of illness, particularly functional status to a parent’s depressive symptoms

(Canning, et al., 1996; Lustig, et al., 1996; Silver et al., 1998; Silver et al., 1995; Frankel

& Wamboldt, 1998; Lustig et al., 1996; Weiss & Chen, 2002). Furthermore, increased

severity of illness has been linked to decreased normalization efforts particularly when

the illness is poorly controlled, the illness symptoms are unpredictable or with illness

exacerbations (Carnevale et al., 2006; Cohen, 1993; Dashiff, 1993; Haase & Rostad,

1994; Knafl & Deatrick, 2002; Lee, 1996; O’Brien, 2001). Therefore, this study finding

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helps to explain the strong correlation between a child’s functional status and

normalization efforts; a mother’s depressive symptoms act as a mediator to help

strengthen the relationship.

While normalization was significantly correlated with family functioning it did

not perform as a mediator in the analysis. However, it is possible that with a larger

sample size normalization would be a significant predictor of family functioning.

Normalization would then possibly serve as a mediator between depressive symptoms

and family functioning.

Instruments

Overall, the instruments chosen for this study performed well and had acceptable

instrument reliability (Table 3.1). A Cronbach alpha coefficient analysis for the CES-D

excluded four participants due to missing data on one of the questions. The data was

missing totally at random on four different questions. After the mean score for each of the

missing items was imputed and the data re-analyzed the Cronbach alpha (.92) did not

change. Mothers did not voice any difficulty with completing this instrument.

The Cronbach alpha for the Normalization subscale was computed with only 81

participants because one item of the subscale (item 6) queried mothers regarding effect of

the technology dependent child on siblings. Therefore, mothers with only one child left

this VAS question blank. Reliability with 81 participants was .83. When the instrument

was reanalyzed with Item 6 deleted, the alpha was .78 with 99 participants. Some

mothers voiced concerns regarding the wording of a few of the items. Many indicated

that they did not care for the wording in item 5 “How much of a hassle does your child’s

medical treatment create for your family’s daily routine?” Mothers questioned the use of

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the word “hassle” and found it too strong of a word. Most stated that they see their child’s

treatment as part of their responsibility as parents and not a hassle or bother. Some of the

mothers stated that they found completing the questionnaire helpful because it helped

them to reflect on their situation and gain insight.

The Feetham Family Functioning Scale (FFFS) was more problematic because

mothers without a spouse/partner left those items blank even though the instructions

regarding spouse/partner indicated that they were to answer the questions based on how

much they wanted the functions met. Most stated they did not care to complete those

items because they saw them as non-applicable. The Cronbach alpha was .86, however,

34 participants had missing data primarily on the spouse/partner questions. When this

scale was re-analyzed with spouse items removed, the alpha was .81 for 80 participants.

Additionally, since there was a fair amount of missing data on the FFFS, data was

imputed as discussed earlier in Chapter 4. Therefore, to assure statistical conclusion

validity, the hierarchical multiple regression analysis was re-run using only those

participants with no missing data. The results were the same; depressive symptoms were

the sole predictor of family functioning and the level of significance for the equation

remained the same. While the FFFS has good reliability and validity and has been used in

many previous studies of children with chronic conditions, it is problematic with mothers

who have no spouse/partner involvement due to missing data.

The Functional Status II-R had a Cronbach alpha of .76. Since this instrument was

administered by the investigator only two had missing data. These missing data were due

to errors in omitting questions that should have been asked in Part 2 of the instrument.

Mothers did not express any difficulty with completing this instrument. The first item on

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the scale that queried mothers about their child’s eating was problematic however. This

item asked if their child ate well. Although almost 90% of the children had a feeding tube

many of the mothers answered that their children ate well most of the time. For

consistency sake, mothers were then asked to think about whether they ate well by

mouth, not if the tube feeding sessions went well.

Study Limitations

The study had methodological and sampling limitations. Generalizability of the

study is limited because of the convenience sample. While there was diversity in regards

to race/ethnicity, education, income, and marital status, the sample was primarily

comprised of married, Caucasian, Non-Hispanic mothers from suburban, Northeastern

Ohio with at least a partial college education who were recruited from a large tertiary

children’s hospital. Therefore, study results should be interpreted with caution for other

groups. Recruitment from a variety of sites such as a large county hospital in the area that

cares for a large population of children who receive public assistance would have added

to the diversity in terms of family income, inner city residence as well as race/ethnicity.

Since the sample was voluntary, it may be less representative of the general

population of mothers. However, most mothers who met inclusion criteria were invited to

participate therefore; this study had a good sampling of the population of mothers with

children who are technology dependent in the Northeast Ohio area. A vast majority of the

participants were eager to share their story and intrigued that someone would be

interested to hear about their life with a child who is technology dependent. Mothers who

declined to participate in the study primarily stated that they were too busy; overwhelmed

with all of their responsibilities caring for their families. These refusals occurred despite

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the offer of flexible interview times and locations. These mothers would have added

greater understanding of this population because they may have had more challenges with

family functioning and normalization, particularly organization, thus they felt

overwhelmed.

Third, since the sample was cross-sectional it is difficult to assess the study

variables over time. The investigator is currently conducting a 12-month follow-up study

of participants to overcome this limitation.

Another limitation of the study was the amount of missing data on the Feetham

Family Functioning Survey. This limitation was mitigated by imputation of data using a

statistical calculation based on data available. Further analysis using only participants

with complete data on this instrument helped to validate use of this statistical technique;

there were no differences in statistical results or conclusions.

A fifth limitation is that only one subscale of the Normalization Scale, the “Actual

Effect of the Chronic Physical Disorder on the Family” (Murphy & Gottlieb, 1992), was

used in this study to measure normalization. Since this study was to include children ages

3 months to 16 years, many items on the other subscales were found to be not applicable.

This subscale however had the highest Cronbach alpha reliability (.84) and the highest

Eigenvalue (7.0) making it the most representative of the concept of normalization.

Additionally, since this instrument has only been used in one previous study by the

scale’s creator, results should be interpreted with caution. Missing data on one of the

items related to siblings was discussed above.

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Study Implications

Clinical Implications

The results from this study increase nursing knowledge regarding the impact of

the child who is technology dependent on their families and provides evidence for

improved delivery of care for this population. Study findings indicate that mothers of

children who are technology dependent are at high risk for psychological distress that can

affect overall family functioning. Greater depressive symptoms and less use of

normalization were found to be significantly associated with poorer family functioning.

Findings from this study provide evidence that managing the demanding care regimens

and coordinating the many services typically required by these children while still

maintaining a “normal” family life is difficult for many families.

It is of paramount importance that the complex issues these families face be

addressed by a multidisciplinary team and an effective intervention employed to facilitate

family functioning thereby bolstering the child’s optimal growth and development. Past

research indicates that maintaining the mental health of caregivers and positive family

functioning affect the child’s growth and development (Miles et al., 1999). Findings from

this study revealed that 35% of the variance in family functioning was explained solely

by the level of depressive symptoms, underscoring the impact of such symptoms.

Therefore, an intervention that targets a mother’s psychological distress, a key concern in

this population, is vitally necessary.

Study findings support the need for all health care providers working with

families of children who are technology dependent to be vigilant to objectively assess the

level of depressive symptoms in the primary caregivers of these children. Noteworthy is

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the fact that during the course of data collection the investigator had no indication of the

high level of depressive symptoms in some participants during the interview. Mothers

with a high level of depressive symptoms often would appear very “upbeat and together”

for the investigator as well as their child’s health care providers. The level of depressive

symptoms was only evident after the CES-D instrument had been scored and the findings

discussed with the mother. It could be proposed that mothers wanted to appear composed

so that there would be no question as to her ability to adequately care for her child.

Mothers indicated that the availability of and financial resources for mental health

support services are sorely lacking. Many stated that when they did find a mental health

provider the provider was overwhelmed by the magnitude of the situation the mother

presented and was of little assistance or guidance. Some indicated that they were unable

to continue attending mental health care sessions due to lack of financial resources to do

so. Furthermore, complicating a mother’s access to mental health care is the fact that she

often has limited time or respite care for her child to attend such sessions. Therefore, the

availability of and referral to mental health care providers who are familiar with the

complex situations these mothers face and who would provide flexibility as far as time

and location for mental health care sessions would greatly benefit this population by

enhancing family functioning and in the long term promote the child’s growth and

development.

When the access to mental health care is delayed or unavailable, all health care

providers can help to make a big difference for the mental health of mothers by simply

asking how things are going at home caring for a child who is technology dependent

while trying to maintain a “normal” family life. Many mothers indicated that no one else

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had genuinely been interested in what the experience is like. Frequently mothers stated,

“they just don’t get it”-referring to the fact that it is difficult to explain the experience of

caring for a child who is technology dependent to family and friends or even health care

providers. Mothers would greatly benefit from the opportunity to have a discussion with

health care providers. The mothers invest substantial amounts of time and effort in the

care of their child that is often unrecognized by family, friends and at times even health

care providers. Simple words of encouragement to mothers from health care providers

such as “you are doing an amazing job caring for your child…” can be of great benefit as

was witnessed by the investigator.

Many indicated that while they were taught the techniques and procedures for

their child’s equipment, treatments and medical care prior to discharge from the hospital

no one discussed with them how to practically apply it and make it work at home in their

family situation. Basically they felt as if they were “left to figure it out on your own”.

Very few had a resource person to contact following discharge when questions arose.

While normalization efforts were not a significant predictor of family functioning,

perhaps in part due to the sample size, normalization was significantly correlated with

family functioning: better family functioning was associated with more normalization

efforts. Therefore, health care providers could assist families and perhaps help to enhance

family functioning by providing education regarding normalization efforts particularly

prior to a child’s discharge from the hospital to home. Normalization efforts includes the

development of management techniques such as organizational strategies; charts,

notebooks to help keep track of the child’s supplies, physicians, therapists, surgeries,

treatments, medications and appointments. Also, health care providers could educate

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caregivers regarding how to deliver safe care and treatments to the child while

concomitantly providing information regarding flexibility that would allow them to live

as “normal” of a family life as possible. Additionally, health care providers could assist

with tips and techniques of how to transport the child and their equipment and supplies to

appointments, family outings and vacations. While more research needs to be done these

are some of the many management strategies that health care providers could teach

families that might help to bolster a family’s normalization efforts and in the long term

positively enhancing family functioning.

Theory Implications

Results from this study helped to support the models upon which it was based:

The Family Management Style Framework (Knafl &Deatrick, 2003) and Paterson’s

(2001) Shifting Perspectives of Chronic Illness Model. Findings suggest that a mother’s

depressive symptoms influence her perception of the illness or “definition of the illness”,

thereby affecting family functioning as suggested by Knafl and Deatrick (2003).

Additionally, these findings help to explain how families develop the “normalcy lens”

and what factors might hinder the process of normalization, primarily the child’s poor

functional status and the mother’s high level of depressive symptoms.

The findings support Paterson’s (2001) Shifting Perspectives Model of Chronic

Illness which suggests that a mother’s perception of reality will dictate how she interprets

her situation and consequently responds to the child’s condition. Thus, when a mother

has “wellness in the foreground”, the child is seen as “normal” despite the number of

technologies or treatments necessary. As Knafl and Deatrick (2003) suggest in their

description of normalization attributes, this is not a denial of the child’s condition or

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distortion of reality, but instead a revision of what is “normal”. On the other hand, in the

case of children with poorer functional status, or who are experiencing an illness

exacerbation, mothers often had “illness in the foreground”, and therefore had decreased

normalization efforts.

Policy Implications

Findings from this study indicate the need for review of current policy regarding

families of children who are technology dependent. There is an urgent need to address

resources allocated to help families continue to care for their child who is technology

dependent at home. These resources include family mental health support systems that

need to be put in place prior to the child’s discharge home. As evidenced in this study, it

is a formidable task to care for these children, particularly when there are no supports

available that address mental health. The goal of such supports might be for family

members to have preventive mental health treatment available before discharge home. At

least it should be available by the home health care nurse or be assessed. Such preventive

support and/or treatment would help to potentially avoid mental health crises that would

negatively affect family functioning and the health of the child.

Another resource that policy change would need to address is establishment of a

qualified respite care system for families. Respite requires individuals who are

knowledgeable and capable of caring for the needs of these children. Almost half of the

families did not have any nursing care. For those who did receive nursing care, a number

indicated that some of the nurses assigned to them were not adequately prepared to care

for their child. These mothers often felt they could not leave their child with others

because they did not have adequate knowledge or training to be able to safely care for

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their child and had experienced negative consequences as a result. Some mothers

indicated, even tearfully, that none of their family members were willing to watch their

child even for a few hours due to fear of the equipment or that they would not be able to

appropriately handle an emergency should one arise.

On a federal level, the government needs to comprehensively examine the current

state of funding for these children. Research into best practices that would

comprehensively care for these children and their families while being fiscally

responsible is of paramount importance. This is of particular interest at this point in

history due to the economic concerns of this country. Children who are technology

dependent while small in number are responsible for a large percentage of the health care

expenditures for children. Families are often willing to care for these children but need to

be given the resources to do so in the form of respite care, mental health support and

financial compensation for the caregiving parent who has often given up gainful

employment. Development of a comprehensive program will in the long term be fiscally

responsible and prudent approach as it will decrease duplication of services, provide

coordination of services and address health care needs promptly to decrease hospital

readmission rates.

At a state level, mechanisms need to be put into place to adequately assess

children who are technology dependent and in need of placement on a Medicaid waiver

program that would provide financial assistance to fund much needed respite care,

medications and equipment as well as transportation equipment. Some mothers have

suffered permanent back injuries due to lack of funds for lifts that would help with

transporting and moving their children.

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At a local level, pediatric tertiary care centers would be able to serve this

population more effectively with a comprehensive, interdisciplinary team approach.

Members of the team include health care professionals from a variety of specialties as

well as home care agencies and durable medical equipment companies. Holistic care for

the child and family such as the hospice care model would be important to explore.

Important members of the team include the care coordinator that would act as a liaison

between the family and other members of the team and a mental health care professional

with experience dealing with the issues these families face on a daily basis.

Recommendations for Future Research

Findings from this study point to the need for further research regarding families

of children who are technology dependent. Future research of this population of children

and their families should include longitudinal, quantitative studies. Few quantitative

studies have been done of these children and their families particularly studies that have

longitudinally examined family functioning or how families try to provide as normal a

family life as possible for children who are technology dependent. Furthermore, little

information exists regarding the impact of a child’s technology dependence on their

siblings or family finances. A 12 month follow-up study of the participants from this

study is currently underway to help to address these gaps.

Although mothers were chosen as participants for this study, little has been done

to examine the perspective of fathers with children who are technology dependent. Some

of the spouse/partners of the study participants expressed interest in being able to provide

their views regarding the experience. Having the perspective of both parents would yield

helpful evidence regarding family functioning.

240

This study provides foundational evidence to develop and design interventions

that will address the needs of this population. While the sample did include diversity in

terms of race/ethnicity, purposive recruitment of minority families would greatly enhance

the generalizability of the findings. The study results indicate that mothers with a high

level of depressive symptoms are at risk for poorer family functioning and decreased

normalization efforts that consequently place the child as well as their typically

developing siblings at risk for deleterious effects on their growth and development.

Further descriptive, correlational studies need to be done to look at factors correlated with

increased depressive symptoms so that risk factors could be identified. Additionally,

interventions that will target the mother’s psychological distress, a key concern in this

population, are essential. Of paramount importance are mental health resource

interventions. The goal of such interventions is to help the mother to reframe her

situation; put life in perspective and celebrate the positive thereby fostering personal

growth.

Future research with larger sample sizes should investigate the relationship of

normalization and family functioning. Given the correlation of these variables,

interventions that may offer assistance with normalization efforts may ultimately assist

with family functioning. Such normalization strategies include teaching of organizational

skills such as helping to establish a routine and schedule for care and treatments,

assistance with prioritizing, time management, and flexibility. Other intervention

suggestions include on-line support groups in lieu of in-person support groups due to

time constraints and limited respite as well as a web site with resource information.

241

Future research should include development of a measurement tool for severity of

illness for children who are technology dependent. While the FSII-Revised instrument

did capture functional status it did not capture the variation of time required for care and

treatment, the number of care needs or the level of acuity for these children thus it did not

seem to capture the true severity of illness. Also, a tool other than the OTA (1987) rubric

that would capture the level of technology dependence would be helpful for future

quantitative studies.

Other recommendations for future research include a randomized, prospective

experimental study of families upon discharge from the hospital for the first time with

and without an intervention protocol that would include intense discharge teaching and

preparation as well as normalization promotion activities and support. Families in the

treatment group would be taught how to balance daily life with the technology to keep

the child stable and healthy while concomitantly maintaining effective and positive

family functioning and ensuring the child who is technology dependent has a childhood.

Finally, the holistic delivery of care that is an essential part of the hospice model

shows great promise for families of children who are technology dependent. This model

includes supports for the spiritual, instrumental and physical needs of patients and their

families. Future research should investigate an intervention using this model of care for

families with children who are technology dependent.

Conclusions

This descriptive, correlational study examined how 103 families respond to and

manage the special challenges of children who are technology dependent after they have

242

been discharged from the hospital to home. This is the first quantitative study to examine

both family functioning and normalization efforts of these families.

Findings from this study support past research; a large percentage of mothers of

children who are technology dependent have a significant amount of psychological

distress. A total of 40% of mothers who were participants in this study had a level of

depressive symptoms above the established cutoff point of 16 on the CES-D, indicating a

risk for clinical depression (Radloff, 1977). Furthermore, a total of 24% of mothers had

levels indicating a very high risk for clinical depression (Myers & Weissman, 1980)

(Table 4.8).

A critical new finding of this study is that a mother’s level of depressive

symptoms has a very significant affect on overall family functioning in this population.

Hierarchical Multiple Regression analysis revealed that a mother’s depressive symptoms

was the only significant predictor of family functioning while controlling for covariates

(length of caregiving duration, amount of home health care hours, race, family income

and age of the child who is technology dependent); greater number of depressive

symptoms was related to poorer family functioning.

Other significant findings from this study are related to normalization efforts,

level of technology dependence and depressive symptoms as a mediator variable. Several

independent variables/covariates were found to be significant predictors of greater

normalization efforts: better child’s functional status, less depressive symptoms, fewer

home health care nursing hours, an older child and Non-Caucasian race or Hispanic

ethnicity. This finding is important empirical evidence because it is the first time that

normalization has been quantitatively analyzed to determine correlates and predictors.

243

Results from the ANOVA analyses indicate there were no statistical differences in

normalization efforts, family functioning and mothers’ depressive symptoms based on the

child’s level of technology dependence (OTA, 1987 Group 1: mechanical ventilation,

Group 2: intravenous medications/nutritional substances, Group 3: respiratory or

nutritional support). These findings are consistent with the few quantitative studies that

have examined outcomes based on level of technology dependency (Fleming et al., 1994;

Leonard et al., 1993), however improved methodology to capture level of technology

dependence and severity of illness may help to assess for significant differences of these

groups in future studies.

Statistical analyses for mediation (Baron & Kenney, 1986) reveal that depressive

symptoms are a partial mediator between a child’s functional status and normalization.

Normalization was not found to be a mediator between a child’s functional status or a

mother’s depressive symptoms and family functioning. While normalization was

significantly correlated with family functioning it was not a significant predictor of

family functioning in multiple regression analysis.

This work provides important empirical evidence for the design of interventions

to assist families caring for this vulnerable and growing population of children at home. It

is of paramount importance that the complex issues these families face be addressed by a

multidisciplinary team and an effective intervention employed to facilitate family

functioning thereby bolstering the child’s optimal growth and development. The

implementation of an effective intervention that will target the level of psychological

distress experienced by these mothers is an important beginning step in this process.

244

APPENDICES

Appendix A Functional Status II (Revised)

245

246

Appendix B Level of Technology Dependency Questionnaire

Participant’s ID #____________ Child’s OTA Group Level:________

Types of Technology

0=No, 1= Yes

a Nasogastric tube feeding

b Gastrostomy tube feeding

c Intermittent intravenous infusion

d Continuous intravenous infusion

e Intravenous infusion of total parenteral nutrition

f Oxygen via nasal cannula

g Oxygen via tracheostomy collar

h

Oxygen via continuous positive airway pressure (CPAP)

i

Continuous positive airway pressure (CPAP) without oxygen

j Tracheostomy tube

k Mechanical ventilator

l Other

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Appendix C Normalization Scale

Instructions: Please think about your child and your family over the last two months. For each question, you are asked to rate the frequency with which your child or family has believed something or done something by placing a slash (l) to cut the line somewhere between the two extremes labeled A LOT or A LITTLE- at the place that best corresponds to the answer that fits best for your child and family. For example, if you feel that the question describes your child/family a lot over the past two months, you should place the slash (l) at the A LOT end of the line. If you believe that the question sort of describes your child/family, you should place the slash (l) somewhere between the two extremes-closer to the A LITTLE end if it only partly describes your child/family and closer to the A LOT end if it pretty much describes your family. If the question hardly describes your child/family at all, you place your slash (l) at the A LITTLE end of the line.

Please remember-there are no right or wrong (nor good nor bad) answers. You know what best describes your situation.

1. If your child did not have this chronic condition, how different would your family be

compared to what it is like now?

a lot --------------------------------------------------------------------- a little

2. If your child did not have this chronic condition, how different would your child be

compared to what she/he is like now?

a lot --------------------------------------------------------------------- a little

3. How much of your family’s daily activities have to be planned around your child’s

needs?

a lot --------------------------------------------------------------------- a little

4. How much are your activities with your spouse/partner or other adults affected by your

child’s condition?

a lot --------------------------------------------------------------------- a little

248

5. How much of a hassle does your child’s medical treatment create for your family’s

daily routine?

a lot --------------------------------------------------------------------- a little

6. If you have more than one child: How much are your children’s activities affected by

your child’s condition?

a lot --------------------------------------------------------------------- a little

7. How much does your child’s condition affect your family life?

a lot --------------------------------------------------------------------- a little

8. How much do other people treat your family like they treat other families?

a lot --------------------------------------------------------------------- a little

9. How much does a child with a condition like your child’s need to be treated differently

because of the condition?

a lot --------------------------------------------------------------------- a little

10. How reluctant are other people to include your family in some activity because of

your child’s condition?

a lot --------------------------------------------------------------------- a little

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Appendix D Center for Epidemiological Studies- Depression (CES-D)

ID#

Instructions for Questions: Below is a list of the ways you might have felt or behaved. Which of the following statements best describes how often you felt or behaved this way DURING THE PAST WEEK. 0. Rarely or None of the Time (Less than 1 Day) 1. Some or a Little of the Time (1 – 2 Days) 2. Occasionally or a Moderate Amount of Time (3 - 4 Days) 3. Most or All of the time (5 - 7 Days) DURING THE PAST WEEK……… SCORE

1. I was bothered by things that usually don’t bother me. 0 1 2 3

2. I did not feel like eating; my appetite was poor. 0 1 2 3

3. I felt that I could not shake off the blues even with help from my family or friends.

0 1 2 3

4. I felt that I was just as good as other people. 0 1 2 3

5. I had trouble keeping my mind on what I was doing. 0 1 2 3

6. I felt depressed. 0 1 2 3

7. I felt that everything I did was an effort. 0 1 2 3

8. I felt hopeful about the future. 0 1 2 3

9. I thought my life had been a failure. 0 1 2 3

10. I felt fearful. 0 1 2 3

11. My sleep was restless. 0 1 2 3

12. I was happy. 0 1 2 3

13. I talked less than usual. 0 1 2 3

14. I felt lonely. 0 1 2 3

15. People were unfriendly. 0 1 2 3

16. I enjoyed life. 0 1 2 3

17. I had crying spells. 0 1 2 3

18. I felt sad. 0 1 2 3

19. I felt that people dislike me. 0 1 2 3

20. I could not get “going”. 0 1 2 3

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Appendix E Feetham Family Functioning Survey (FFFS)

In this survey you are asked to rate activities (functions) that occur in your family and with family members. For each family function you are asked to answer three questions: How much is there now? How much should there be? How important is this to you? Please answer all three questions for each family function by circling the number with represents how your feel now about the family function. The term spouse refers to your husband or wife or the person who assumes the functions of a spouse/partner. If you do not have a person in the spouse/partner role, answer the questions based on how much you want the functions met. Please try to answer all items. Please mark you answer by circling the number Little Much

1. The amount of discussion with your friends regarding your concerns and problems.

a. How much is there now? b. How much should there be? c. How important is this to you?

1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7

2. The amount of discussion with your relatives regarding your concerns and problems (do not include your spouse/partner).

a. How much is there now? b. How much should there be? c. How important is this to you?

1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7

3. The amount of time you spend with your spouse/partner.

a. How much is there now? b. How much should there be? c. How important is this to you?

1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7

4. The amount of discussion of your concerns and problems with your spouse/partner.

a. How much is there now? b. How much should there be? c. How important is this to you?

1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7

5. The amount of time you spend with your neighbors.

a. How much is there now? b. How much should there be? c. How important is this to you?

1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7

6. The amount of time you spend in

251

leisure/recreational activities. a. How much is there now? b. How much should there be? c. How important is this to you?

1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7

7. The amount of help from your spouse/partner with family tasks such as care of children, house repairs, household chores, etc.

a. How much is there now? b. How much should there be? c. How important is this to you?

1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7

8. The amount of help from relatives with family tasks such as care of children, house repairs, household chores, etc.

a. How much is there now? b. How much should there be? c. How important is this to you?

1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7

9. The amount of time with health professionals (doctors, nurses, social workers, etc.)

a. How much is there now? b. How much should there be? c. How important is this to you?

1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7

10. The amount of help from your friends with family tasks such as care of children, house repairs, household chores, etc.

a. How much is there now? b. How much should there be? c. How important is this to you?

1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7

If you do not have a child(ren) please indicate here and skip questions 11, 12 and 13.

Children Yes No

11. The number of problems with your child(ren).

a. How much is there now? b. How much should there be? c. How important is this to you?

1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7

12. The amount of time you spend with your child(ren).

a. How much is there now? b. How much should there be? c. How important is this to you?

1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7

Do you have a child(ren) in school?

Child in school

Yes No If No, please skip question # 13

13. The amount of time your child(ren) miss

252

school. a. How much is there now? b. How much should there be? c. How important is this to you?

1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7

14. The number of disagreements with your spouse/partner.

a. How much is there now? b. How much should there be? c. How important is this to you?

1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7

15. The amount of time you are ill. a. How much is there now? b. How much should there be? c. How important is this to you?

1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7

16. The amount of time you spend doing housework (cooking, cleaning, washing, yard work, etc.).

a. How much is there now? b. How much should there be? c. How important is this to you?

1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7

17. The amount of time you miss work (including housework).

a. How much is there now? b. How much should there be? c. How important is this to you?

1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7

18. The amount of time your spouse/partner misses work (including housework).

a. How much is there now? b. How much should there be? c. How important is this to you?

1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7

19. The amount of emotional support from friends.

a. How much is there now? b. How much should there be? c. How important is this to you?

1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7

20. The amount of emotional support from relatives.

a. How much is there now? b. How much should there be? c. How important is this to you?

1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7

21. The amount of emotional support from your spouse/partner.

a. How much is there now? b. How much should there be? c. How important is this to you?

1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7

22. The amount of time your work routine is

253

disrupted (including housework). a. How much is there now? b. How much should there be? c. How important is this to you?

1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7

23. The amount of time your spouse’s/partner’s work routine is disrupted (including housework).

a. How much is there now? b. How much should there be? c. How important is this to you?

1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7

24. The amount of satisfaction with your marriage (relationship with spouse/partner).

a. How much is there now? b. How much should there be? c. How important is this to you?

1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7

25. The amount of satisfaction with the sexual relations with your spouse/partner.

a. How much is there now? b. How much should there be? c. How important is this to you?

1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7

26. What is most helpful to you now?

27. What is least helpful to you now?

254

Appendix F Demographic Questionnaire

1. Age of child (in months)_____: Date of Birth _______; Today’s Date_______

2. Gender of child:

1=Male

2=Female

3. Mother’s age:______

4. Mother’s relationship to the child:

1=biological mother 4=step mother

2=adoptive mother 5=grandmother

3=foster mother

5. Marital status:

1=single, never married 4=separated

2=single, living with partner 5=widowed

3=married 6=divorced

6. Education:

1=less than 7th grade 7=associate’s college degree

2=completed 8th grade 8=baccalaureate degree

3=partial high school 9=partial graduate school

4=high school graduate 10=master’s degree

5=technical/vocational program graduate 11=doctoral degree

6=partial college 12=other

255

7. Race/ethnicity:

1=Latino or Hispanic 4=Asian

2=African-American 5=Native American

3=White, Non-Hispanic 6=Bi-racial

8. Total number living in household:

Number of adults_____

Number of children_____

9. Mother’s employment status:

0=not employed outside the home

1=employed outside the home

10. Number of hours employed per week:______

11. Yearly family income:

0=equal or less than $20,000

1=$20,000-40,000

2=$40,001-60,000

3=$60,001-80,000

4=>$80,001

12. Duration of caregiving (in months)_______:

Date began caregiving_________

Today’s date____________

13. Number of home health care nursing hours:

Number of RN hours__________

Number of LPN hours_________

256

14. Current Address with Zip Code:

________________________________________________ ________________________________________________

15. Current Phone Number:

(Home)___________________ (Cell)__________________ 16. Name, Address, Phone Number of Two Family Members/Friends who will know your whereabouts should you move. You may be contacted about a long term study in the future about how you and your child are doing and what changes have occurred. a. ______________________________________________ ________________________________________________ ________________________________________________ ________________________________________________

b. ______________________________________________ ________________________________________________ ________________________________________________ ________________________________________________

257

Appendix G Introductory Letter

July 1, 2008 Hello! My name is Valerie Toly and I am a doctoral student at the Frances Payne Bolton School of Nursing, Case Western Reserve University. I have had over 20 years experience as a pediatric nurse. Because you are a female caregiver for a child who has special technology needs you have been identified as a potential participant for a research study. The study has been approved by University Hospitals of Cleveland/Rainbow Babies & Children’s Hospital Institutional Review Board. This research study looks at how families manage the care of their child with special technology needs after he/she is discharged from the hospital. I would like to know if you would be willing to answer some questions regarding your experiences of caring for your child with special technology needs. You will be compensated for your time and inconvenience. I can arrange to meet you when you come for your child’s next clinic visit. Participation in this research study is voluntary. Your care would not be affected in any way by your decision regarding participation in this study. The goal of this study is to explore what helps families to best manage the care of their child with special technology needs after they are discharged from the hospital to home. This is also your chance to discuss your thoughts and feelings related to caring for your child. You may contact me at to hear more about the study and to arrange an interview time. I know that you are very busy caring for your child so if I do not hear from you I’ll try giving you a call in about 2 weeks. Thank you for your kind consideration. I look forward to hearing from you soon! Sincerely, Valerie Toly, PhD(c), RN, CPNP Instructor of Nursing

258

Appendix H Telephone Script for Recruitment

Families with Children who are Technology Dependent Valerie Boebel Toly, PhD(c), RN, CPNP

PhD Student at the FPB School of Nursing, CWRU

My name is Valerie Toly and I am a doctoral student at the Frances Payne Bolton School of Nursing, Case Western Reserve University. I sent you a letter a couple of weeks ago that described a research study. This research study looks at how families manage the care of their child with special technology needs after he/she is discharged from the hospital. I would like to know if you would be willing to answer some questions regarding your experiences of caring for your child with special technology needs. I can arrange to meet you when you come for your child’s next clinic visit. Participation in this research study is voluntary and I can go over more details about the study when we meet. Your care would not be affected in any way by your decision regarding participation in this study.

259

Appendix I Resources for Mental Health

Resources for Mental Health Care

This information is provided for participants of the research study regarding families of children with special technology needs. Symptoms of psychological distress should be discussed with your primary care physician. Other resources in the Greater Cleveland area that can provide immediate assistance include: Cuyahoga County 24-Hour Mental Health Crisis Information and Referral Hotline Phone: (216) 623-6888 United Way Services of Greater Cleveland First Call for Help Phone: 211 or (216) 436-2000

260

Appendix J

Consent Form

261

262

263

Appendix K HIPPA Release Form

264

265

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