Sarah Blyth Dwyer - QUT · Sarah Blyth Dwyer BA(Hons)Psych This thesis is submitted for the degree...

IDENTIFYING CHILDREN AT RISK OF DEVELOPING

MENTAL HEALTH PROBLEMS: SCREENING FOR

FAMILY RISK FACTORS IN THE SCHOOL SETTING

Sarah Blyth Dwyer BA(Hons)Psych

This thesis is submitted for the degree of Doctor of Philosophy

in the School of Public Health, Queensland University of Technology,

October, 2002

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DECLARATION

The material presented in this thesis is, to the best of my knowledge, my own

original work, except as acknowledged in the text. None of the material has been

submitted, either in whole or in part, for a degree at this or any other university.

Data were collected by a team of researchers funded by the National Health

and Medical Research Council. My original input is as follows:

1. I designed the studies and the main instrument used in the studies, collated

the validation instruments, and jointly prepared the funding applications.

2. I was actively involved in the recruitment, engagement, and ongoing

maintenance of schools and families in the research.

3. I supervised and actively participated in the collection and scoring of the

data for all studies reported, including the production of a Data Checking

Manual.

4. I conducted all data analyses.

5. I conducted the literature review and produced all the written output.

Data collection was conducted with the approval of the Queensland University

of Technology Human Research Ethics Committee. The original data are available

for inspection.

Sarah Blyth Dwyer, 18th November 2002

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ACKNOWLEDGEMENTS

I would like to wholeheartedly thank Dr Jan Nicholson, a better supervisor I

could not have asked for. Not only has Jan been an amazing role model and mentor,

she has provided a delightful mix of expertise, friendship, and support that made the

whole thesis possible. She has also provided me with opportunities far beyond those

necessary for the research which have enabled me to develop valuable skills for my

future academic and professional life. Jan, thank you.

I would also like to thank Dr Diana Battistutta. I was very lucky to have

Diana as an Associate supervisor. She is a talented statistician whose patient and

thorough teaching have greatly enriched my understanding of all things statistical.

Diana provided invaluable advice for the analyses conducted in this research.

During the long process of researching and writing, my other Associate

supervisor, Professor Matt Sanders, enabled me to maintain my clinical skills by

employing me as a therapist at the Parenting and Family Support Centre. I am

grateful to him for the numerous opportunities he has offered me over the years and

thank him for his feedback on the (nearly) final versions of the thesis.

I would like to acknowledge the hard work and support of the dedicated team

of researchers who worked on the Promoting Adjustment in Schools (PROMAS)

Project. Special thanks to Michelle Gill who had the unenviable task of keeping track

of all families in the data collection process and Bonnie MacFarlane whose cheery

demeanour helped keep us all smiling during long hours of telephoning

nonresponders. Thanks also to Jyai Allen and Chris Sibthorpe who were project

research assistants in its early days.

Under the leadership of Professor Brian Oldenburg, the School of Public

Health at QUT provided an environment remarkably conducive for postgraduate

studies. The School provided expertise, technical assistance, and financial support,

but more than this, a friendly environment. I felt that people in the School were

prepared to go out of their way to address obstacles occurring along the way. I gained

motivation and support from swapping PhD stories with my fellow PhD students,

Nicola Burton, Diane Keating, Jan McDowell, Dru Carlsson, Fiona Rowe, and Clare

Macaulay. Thanks to Nicola Burton and Shilu Tong for critiquing parts of the

manuscript.

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My husband, Jonathan, provided me with everything else I needed. I thank

him for his understanding and support, and for the little things he did that made life

easier and more enjoyable (I especially liked the late night cheese on toast). Having

completed a PhD himself, he also gave me several valuable tips for getting through it

all and his feedback on drafts was greatly appreciated. Jon, thank you for your love.

Thank you also to my parents and family for their unfailing belief in me. My

father’s conspiratorial sharing of his vision of the words ‘Dr Sarah’ on my office door

was with me from the start. My parent’s company, Source Technology, generously

donated two computers and money for ‘scratch-it’ tickets, enabling us to offer some

incentives for the return of parent and teacher data. I’d also like to thank Keith and

Dorothy Dwyer for treating me like a daughter and for helping with the finances

during these ‘lean’ student years. My good friend, Lucy Tully, provided support and

encouragement from afar and thank you to little Gemma Duncan for sharing this

phase of my life - you were a constant source of comfort and happiness.

Finally, I am grateful to the parents and teachers who gave their time to

answer questionnaires so that I could collect the precious data. Without them, there

would be no PROMAS Project.

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PUBLICATIONS AND GRANTS Publications

This thesis is submitted as a ‘PhD by publication’. As such, it is comprised of

three papers, the first of which has been accepted for publication (see Appendix A).

The second paper was first submitted to the Journal of Abnormal Child Psychology.

As a result of feedback from the reviewers for this journal, the paper underwent

substantial revisions and is now under review by the American Journal of Community

Psychology. The third paper is under review by the Journal of Consulting and

Clinical Psychology (see correspondence concerning these journals’ receipt of the

manuscripts in Appendix B).

Please note, that because this thesis is formatted to fulfil the requirements of a

‘PhD by publication’, there is necessarily some overlap between the literature review

conducted in the first three chapters and the introductions to each of the respective

papers. The three papers included in this thesis are listed below.

Paper 1: Dwyer, S.B., Nicholson, J.M., & Battistutta, D. (2003). Population level

assessment of the family risk factors related to the onset or persistence of

children’s mental health problems. Journal of Child Psychology and

Psychiatry, 44(5), 699-711.

Paper 2: Dwyer, S.B., Nicholson, J.M., Battistutta, D. & Oldenburg, B. (2002).

Teachers’ knowledge of children’s exposure to family risk factors: Accuracy,

sources, and usefulness. Manuscript submitted for publication.

Paper 3: Dwyer, S.B., Nicholson, J.M., & Battistutta, D. (2002). Identification of

children at risk of developing internalising or externalising mental health

problems: A comparison of screening methods. Manuscript submitted for

publication.

Two other papers are also relevant to this dissertation. The rationale, design,

and baseline findings of the PROMAS Project have been described in one paper (see

Appendix C) and the recruitment procedures have been outlined in another (see

Appendix D). These two papers are listed below.

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Nicholson, J.M., Oldenburg, B., Dwyer, S.B., & Battistutta, D. (2002). The Promoting

Adjustment in Schools (PROMAS) Project: Study design, methods and

baseline findings. Manuscript submitted for publication.

Battistutta, D., Dwyer, S.B., Nicholson, J.M., Oldenburg, B. (2002). A successful

multilevel strategy for improving response rates in school-based research.

Manuscript in preparation.

Grants The research reported in this thesis was supported by several grants:

1. National Health and Medical Research Council project grant ‘Promoting

the mental health of children and families: Measurement and intervention

within school communities’ (No. 990241), awarded to J.M. Nicholson, B.

Oldenburg, G.C. Patton, and G.S. Parcel (Chief Investigators) and S.

Tortolero, and S. Glover (Associate Investigators).

2. National Health and Medical Research Council Public Health Postgraduate

Research Scholarship ‘School-based mental health promotion: Identifying

family risk factors, early symptoms, and intervention strategies’ (No.

987409), awarded to S.B. Dwyer (Chief Investigator).

3. Australian Rotary Health Foundation Research Fund ‘Preventing family-

based child abuse through schools: Evaluation of a teacher and

environmental intervention’, awarded to J.M. Nicholson, S.B. Dwyer, and

B. Oldenburg (Chief Investigators).

4. Australian Research Council small grant ‘Reliability and validity of the

Family Risk Factor Checklist for measuring children’s risk of developing

mental health problems’, awarded to J.M. Nicholson, B. Oldenburg, and

S.B. Dwyer (Chief Investigators).

5. QUT Postgraduate Overseas Study Grants-in-Aid Scheme awarded to S.B.

Dwyer to attend an overseas conference and visit American centers

involved in school-based mental health promotion.

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ABSTRACT

Children’s mental health problems are a significant public health concern.

They are costly to society in both human and financial terms. This thesis contributes

to the ‘science of prevention’ by examining issues related to the identification of

children at risk of mental health problems. In particular, it was of interest to

determine whether ‘at-risk’ children could be identified before the development of

significant behavioural or emotional problems. Three areas were explored: family

risk factors that predict the development of children’s mental health problems,

teachers’ ability to identify family risk factors, and parent- and teacher-report

screening methods.

Data were collected from the parents and teachers of over 1000 children in

preschool to Year 3 as part of the Promoting Adjustment in Schools (PROMAS)

Project. Parents and teachers each completed two questionnaires at two time points,

one year apart. Parents completed the Family Risk Factor Checklist - Parent (FRFC-

P) and the Child Behaviour Checklist (CBCL) and the equivalent instruments for

teachers were, respectively, the Family Risk Factor Checklist - Teacher (FRFC-T) and

the Teacher Report Form (TRF). The FRFC-P and FRFC-T were original to the

current research and were designed to assess children’s exposure to multiple family

risk factors across five domains: adverse life events and instability (ALI), family

structure and socioeconomic status (SES), parenting practices (PAR), parental verbal

conflict and mood problems (VCM), and parental antisocial and psychotic behaviour

(APB).

Paper 1 investigated the psychometric properties of the FRFC-P and the

potential for its use at a population-level to establish community risk factor profiles

that subsequently inform intervention planning. The FRFC-P had satisfactory test-

retest reliability and construct validity, but modest internal consistency. Risk assessed

by the PAR domain was the most important determinant of mental health problem

onset, while the PAR, VCM, and APB domains were the strongest predictors of

mental health problem persistence. This risk factor profile suggests that, for the

studied population, the largest preventive effects may be achieved through addressing

parenting practices.

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Paper 2 examined teachers’ knowledge of children’s exposure to family risk

factors using the FRFC-T. While teachers had accurate knowledge of children’s

exposure to risk factors within the ALI and SES domains, they had poor knowledge of

children’s exposure to risk factors within the PAR, VCM, or APB domains - the types

of risk factors found in Paper 1 to be the most strongly related to children’s mental

health problems. Nevertheless, teachers’ knowledge of children’s exposure to risk

factors within the ALI and SES domains predicted children’s mental health problems

at one year follow-up even after accounting for children’s behaviour at the first

assessment.

Paper 3 investigated the potential of both the FRFC-P and FRFC-T for

identifying individual, at-risk children. The accuracy of the FRFC in predicting

internalising versus externalising disorders was compared against behavioural and

simple nomination screening methods. For both parents and teachers, the behavioural

screening methods were superior, however, the simple nomination method also

showed promise for teachers. Both parents and teachers were more accurate at

identifying children at risk of externalising mental health problems than children at

risk of internalising problems. The performance of the FRFC and simple nomination

methods in identifying children for selective interventions, before the development of

significant behavioural or emotional problems, was also tested. Both the FRFC and

simple nomination methods showed only modest predictive accuracy for these

children.

Combined, the results suggest that while on the one hand, the FRFC is useful

for population level screening to inform intervention planning, on the other hand, it

falls short of achieving good predictive accuracy for individual children. Future

research should investigate ways to optimise predictive accuracy for individual

children, particularly those at risk of developing internalising disorders. One option

may be to use the FRFC in conjunction with behavioural screening methods. The

challenge is to develop accurate screening methods that remain practical to complete

at a population level. Finally, this body of research provides insight into the

feasibility of offering selective preventive interventions within the school setting.

While significant obstacles remain, there were several promising indications that

using screening methods such as FRFC-T or simple nomination, teachers may be able

to identify children earlier on the developmental pathway, before significant

behavioural or emotional symptoms have developed.

ix

CONTENTS

Declaration ................................................................................................................... ii

Acknowledgements..................................................................................................... iii

Publications and Grants ................................................................................................v

Abstract ...................................................................................................................... vii

Contents ...................................................................................................................... ix

List of Tables ............................................................................................................ xvi

List of Figures ........................................................................................................... xix

CHAPTER 1 - OVERVIEW AND BACKGROUND .......................................... 1

Brief Overview of Research.........................................................................1

Significance ......................................................................................3

Background ......................................................................................................4

Definitions of mental health problems .............................................4

Epidemiology of children’s mental health problems........................4

Prevention of children’s mental health problems .............................8

Schools as a setting for prevention.................................................10

CHAPTER 2 - RISK FACTORS FOR CHILDREN’S MENTAL HEALTH PROBLEMS .............................................................................................. 12 Definition of ‘risk’ and ‘protective’ factors ...................................12

Types of risk (and protective) factors.............................................14

Child factors..........................................................................15

Family factors .......................................................................18

School context .......................................................................20

Community and cultural factors ...........................................21

Life events .............................................................................21

Nature of risk (and protective) factors............................................26

Implications for screening methods................................................29

CHAPTER 3 - IDENTIFICATION OF AT-RISK CHILDREN......................... 30 Aims of screening...........................................................................30

Screening for risk factors at a community level .............................31

Screening methods..........................................................................32

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Screening for behavioural or emotional symptoms ..............32

Multiple gate screening.........................................................33

Simple nomination of at-risk children...................................34

Screening for exposure to a single risk factor ......................34

Screening for exposure to multiple (family) risk factors.......35

Evidence regarding parents’ and teachers’ ability to identify at-risk children................................................................................39

Based on observation of behavioural indicators ..................39

Based on knowledge of family background characteristics..40

Psychometric properties .................................................................42

The Family Risk Factor Checklist (FRFC) ....................................43

CHAPTER 4 - METHODS.......................................................................... 45

Design .............................................................................................. 45

Sample Selection ............................................................................. 47

Measures .......................................................................................... 48 Key instruments..............................................................................48

Validation instruments....................................................................49

Short Form - 36 (SF-36) .......................................................50

Alabama Parenting Questionnaire (APQ)............................50

Abbreviated Dyadic Adjustment Scale (ADAS).....................50

Offending History (OH) ........................................................51

Depression, Anxiety, Stress Scale21 (DASS21) .......................51

Psychoticism Subscale from the Symptom Check List - 90 (SCL-90) .................................................................51

Social Support Inventory (SSI)..............................................52

Conflict Tactics Scale (CTS) .................................................52

Procedure ......................................................................................... 54 Time 1 ......................................................................................................54

Initial approvals and support ..........................................................54

Gain project approval ...........................................................54

Establishment of PROMAS Advisory Committee..................54

Engagement of schools...................................................................54

Engagement of parents ...................................................................56

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Data collection................................................................................57

Key instruments.....................................................................57

Validation instruments ..........................................................59

Time 2 ......................................................................................................59

Reengagement of schools and parents............................................59

PROMAS newsletters ............................................................59

Meetings with Principals and Liaison Officers.....................59

Feedback presentations.........................................................60

Tracking parents ...................................................................61

Data collection................................................................................61

Key instruments.....................................................................61

Reliability FRFC-P ...............................................................61

Sample Response Rates and Characteristics ................................... 62 Response rates ................................................................................62

Nonresponders ................................................................................63

Sample characteristics: Schools......................................................66

Sample characteristics: Parents ......................................................68

Sample characteristics: Teachers....................................................72

Data Management and Analysis ...................................................... 73 Data management ...........................................................................73

Quality assurance .................................................................74

Data analysis...................................................................................74

Prospective sample size calculations ....................................74

CHAPTER 5 - POPULATION LEVEL ASSESSMENT OF THE FAMILY RISK FACTORS RELATED TO THE ONSET OR PERSISTENCE OF CHILDREN’S MENTAL HEALTH PROBLEMS .......................................... 77 Abstract ............................................................................................ 77

Introduction...................................................................................... 79

Method ............................................................................................. 81 Design ......................................................................................................81

Sample selection ......................................................................................81

Measures ..................................................................................................82

Family Risk Factor Checklist - Parent (FRFC-P)...........................82

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Construct validation instruments ....................................................83

Child Behaviour Checklist (CBCL) ...............................................84

Procedure .................................................................................................84

Analyses ...................................................................................................85

Scale construction and scoring .......................................................85

Psychometric properties .................................................................86

Relative importance of FRFC-P risk factors ..................................87

Results.............................................................................................. 88 Psychometric properties...........................................................................89

Internal consistency ........................................................................89

Test-retest reliability.......................................................................89

Construct validity ...........................................................................92

Relative importance of FRFC-P risk factors............................................92

Discussion........................................................................................ 97

Acknowledgements........................................................................ 101

Appendix........................................................................................ 102

Author Note ................................................................................... 104

CHAPTER 6 - TEACHERS’ KNOWLEDGE OF CHILDREN’S EXPOSURE TO FAMILY RISK FACTORS: ACCURACY, SOURCES, AND USEFULNESS .................................................................................. 105 Abstract .......................................................................................... 105

Introduction.................................................................................... 106

Method ........................................................................................... 109 Design ....................................................................................................109

Sample selection ....................................................................................109

Measures ................................................................................................110

Family Risk Factor Checklist (FRFC)..........................................110

Sources of teachers’ family risk factor knowledge ......................110

Teacher Report Form (TRF) and Caregiver-Teacher Report Form (C-TRF) ..............................................................................111

Procedure ...............................................................................................111

Analyses .................................................................................................111

Extent and accuracy of teachers’ family risk factor knowledge...111

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Usefulness of teachers’ family risk factor knowledge .................112

Results............................................................................................ 113 Response rates and sample characteristics ...................................113

Extent and accuracy of teachers’ family risk factor knowledge...113


Short FRFC-T...............................................................................119

Usefulness of teachers’ family risk factor knowledge .................120

Discussion...................................................................................... 121

Author Note ................................................................................... 127

CHAPTER 7 - IDENTIFICATION OF CHILDREN AT RISK OF DEVELOPING INTERNALISING OR EXTERNALISING MENTAL HEALTH PROBLEMS: A COMPARISON OF SCREENING METHODS ...... 128 Abstract .......................................................................................... 128

Introduction.................................................................................... 129

Method ........................................................................................... 132 Design ....................................................................................................132

Sample selection ....................................................................................132

Screening measures................................................................................133

Child Behaviour Checklist (CBCL) .............................................133

Family Risk Factor Checklist (FRFC)..........................................133

Nomination of at-risk children .....................................................133

Procedure ...............................................................................................134

Analyses .................................................................................................134

Sample characteristics ..................................................................134

Predictive accuracy.......................................................................135

Definition of outcomes ........................................................135

Definitions of low and high risk groups..............................135

Sensitivity and specificity ....................................................136

Receiver Operating Characteristic (ROC) Curves .............136

Identification of at-risk children before behavioural symptoms emerge ................................................................136

Results............................................................................................ 137

xiv

Response rates and sample characteristics ...................................137

Sensitivity and specificity.............................................................137

Parent-report screening......................................................137

Teacher-report screening....................................................139

Receiver Operating Characteristic (ROC) Curves........................142



Identification of at-risk children before behavioural symptoms emerge ..........................................................................................145



Discussion...................................................................................... 147

Author Note ................................................................................... 156

CHAPTER 8 - GENERAL DISCUSSION.................................................... 157 Summary of results.......................................................................157

Synthesis of results .......................................................................159

Strengths and limitations ..............................................................167

Study design and sampling..................................................167

Measurement .......................................................................168

Procedure............................................................................169

Results .................................................................................171

Future research .............................................................................172

Conclusions ..................................................................................173

REFERENCES......................................................................................... 175

APPENDICES.......................................................................................... 209 A - Paper 1 ...............................................................................................................209

B - Copy of Correspondence Concerning Journals’ Receipt of Manuscripts..........224

C - Baseline Paper....................................................................................................227

D - Recruitment Paper..............................................................................................258

E - Pilot Study..........................................................................................................277

F - Key Instruments.................................................................................................281

G - Scoring Instructions in SPSS Syntax for FRFC-P .............................................303

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H - School Recruitment Package .............................................................................326

I - School Recruitment Presentation.......................................................................339

J - Information Sheets.............................................................................................347

K - Consent Forms ...................................................................................................354

L - Teacher Details Sheet ........................................................................................358

M - PROMAS Newsletter.........................................................................................360

N - Characteristics of Participants with Full Time 1 and 2 Teacher Data Versus Participants Missing Teacher Data ....................................................................365

O - Coordinates used to Construct Receiver Operating Characteristic (ROC) Curves in Paper 3...............................................................................................367

P - Teacher Judgement of Children’s Future Risk of Developing Mental Health Problems.................................................................................................380

Q - Suggestions for Future Improvements to the FRFC-P.......................................386

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LIST OF TABLES

2.1 Risk factors associated with children’s internalising and/or

externalising mental health problems................................................................23

2.2 Protective factors associated with children’s internalising and/or

externalising mental health problems................................................................25

4.1 Family Risk Factor Checklist - Parent (FRFC-P) items and their

corresponding validation questionnaires ...........................................................53

4.2 Questionnaire response rates across times, waves, and respondents.................64

4.3 Summary of the number of nonresponders selected and contacted to

complete FRFC-P questions over the telephone at Time 1 ...............................65

4.4 Characteristics of participating schools at Time 1 ............................................66

4.5 Demographic characteristics and proportions of children at different

levels of risk in each FRFC-P domain by participating versus

nonresponding parents at Time 1 ......................................................................69

4.6 Demographic characteristics and proportions of children at different

levels of risk in each FRFC-P domain by Wave A versus Wave B

parents at Time 1 ...............................................................................................71

4.7 Characteristic of teachers who returned completed questionnaires

at Time 1 (N = 220) ...........................................................................................73

5.1 Observed agreement and kappa coefficients showing test-retest

reliability for each FRFC-P item (two categories per item = risk absent,

risk present; N = 212) ........................................................................................90

5.2 Construct validity of the FRFC-P risk domains and total risk score:

Pearson’s correlation coefficients with the composite validation scores ..........93

5.3 Percentage of children at low, medium, or high risk and median

number of risk factors reported by parents within each FRFC-P

risk domain (N = 1022) .....................................................................................94

5.4 Relative risk (RR) and population attributable risk (AR) for each FRFC-P

risk domain (total N = 779; N with mental health problem onset = 25) ...........95

xvii

5.5 Relative odds of having persistent mental health problems for children

exposed to medium or high risk compared with children exposed to low

risk (the referent) in each risk domain (total N = 779; N with persistent

mental health problems = 120) ..........................................................................96

5.6 Items included in each FRFC-P subscale and ‘risk present’ responses

for each item....................................................................................................102

6.1 Percentage of FRFC-P and FRFC-T responses that matched for each

risk factor (N = 749)........................................................................................114

6.2 Percentage of children (averaged across items with each risk domain)

for whom teachers’ information concerning family risk factors

was obtained from various sources (total N = 756).........................................118

6.3 Relative odds of having clinically significant behaviour problems at T2

for those exposed to medium or high risk compared with those exposed

to low risk (the referent) in the ALI and SES short FRFC-T risk domains ....121

7.1 Demographic characteristics and proportions of children at low,

medium, or high risk as measured by the FRFC-P for participants

with full Time 1 and Time 2 parent data versus participants with

missing parent data..........................................................................................138

7.2 Sensitivity and specificity of three different parent methods for

predicting children in the clinical range for parent-rated internalising

only, externalising only, or total behaviour problems by one year

follow-up (N = 766) ........................................................................................140

7.3 Sensitivity and specificity of three different teacher methods for

predicting children in the clinical range for teacher-rated internalising

only, externalising only, or total behaviour problems by one year

follow-up (N = 455) ........................................................................................141

7.4 Predictive accuracy of parent screening methods for identifying

‘at-risk’ children before behavioural symptoms emerge (N = 623) ................146

7.5 Predictive accuracy of teacher screening methods for identifying

‘at-risk’ children before behavioural symptoms emerge (N = 402) ................147

8.1 Recommendations for future research.............................................................172

xviii

E.1 List of changes made to the FRFC-P and FRFC-T as a result

of the pilot study..............................................................................................280

N.1 Demographic characteristics and proportions of children at low,

medium, or high risk as measured by the FRFC-T for participants with

full Time 1 and Time 2 teacher data versus participants missing

teacher data......................................................................................................366

O.1 Coordinates used to construct ROC curves for the prediction of

internalising only problems: Parent-report screening methods .......................368


externalising only problems: Parent-report screening methods ......................370


total behaviour problems: Parent-report screening methods ...........................372


internalising only problems: Teacher-report screening methods ....................374


externalising only problems: Teacher-report screening methods....................376


total behaviour problems: Teacher-report screening methods ........................378

P.1 Relative odds of teachers responding ‘Yes’ (vs ‘No’) to the single

nomination question concerning a child’s future risk of developing a

mental health problem by levels of teacher-perceived exposure to family

risk factors and teachers’ observation of the child’s behaviour at school.......383

Q.1 Suggestions for future improvements to the Family Risk Factor

Checklist - Parent (FRFC-P) ...........................................................................387

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LIST OF FIGURES

2.1 Model of the development of mental health problems in children....................28

4.1 PROMAS project research design and timeline................................................46

7.1 CBCL and FRFC-P ROC curves for the prediction of parent-rated

internalising only, externalising only, and total behaviour problems .............143

7.2 TRF and FRFC-T ROC curves for the prediction of teacher-rated

internalising only, externalising only, and total behaviour problems .............144

24/12/04 Risk Factors for Children’s Mental Health Problems

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CHAPTER 1 - OVERVIEW AND BACKGROUND

Brief Overview of Research As the striking public health costs of mental health problems have become

evident, attention has increasingly focused on prevention. Around these efforts, a

‘science of prevention’ (Coie et al., 1993) has emerged. This science has seen the

ongoing refinement of theory and methods towards decreasing the prevalence of

children’s mental health problems.

There are three fundamental requirements to the success of prevention efforts:

(1) a sound understanding of the risk and protective factors that influence the

development of mental health problems, (2) reliable and valid methods for identifying

those individuals at elevated risk, and (3) an effective means of modifying the

developmental trajectories of at-risk children (Loeber & Dishion, 1987; Spence,

1998). Despite our growing understanding of developmental psychopathology, there

is still much to learn about aetiological pathways and the complex interplay of risk

and protective factors they engender. Perhaps more worrying is that what is known

about the nature of risk factors is not reflected in current screening methods, which,

perhaps partly as a consequence of this, have inadequate accuracy in detecting at-risk

children. A particular challenge for screening research is the development of tools

that are valid at both the individual and population level. This thesis will contribute to

the prevention literature by advancing our understanding of both the risk factors for

the development of children’s mental health problems and screening methods for

identifying at-risk children.

Prior to describing this research, three reviews are undertaken. First, the

background to the research is described (Chapter 1). Second, the literature on risk

factors for the development of children’s mental health problems is reviewed with the

aim of producing a model that synthesises our current knowledge about the nature of

risk factors and facilitates understanding of the implications this knowledge has for

the identification of at-risk children (Chapter 2). Third, current methods for

identifying at-risk children are examined to determine their accuracy and limitations

(Chapter 3).

This body of research has six key aims, each which will be listed by the paper

in which they are examined.


2

Paper 1 (Chapter 5) reports the reliability and validity of the parent version of

a new risk factor screening instrument, relevant for its use at a population level. In

this first paper, the instrument is specifically used as a ‘checklist’ measure to screen

for family risk factors in the school community. There are two aims:

(1) To determine the reliability and validity of a new screening tool designed

to assess children’s exposure to family risk factors at a population level.

(2) To determine the relative importance of different family risk factors to the

onset versus persistence of children’s mental health problems.

Paper 2 (Chapter 6) examines the feasibility of a teacher version of the risk

factor instrument by exploring whether teachers are able to identify the more

influential risk factors in children’s family backgrounds that were identified in Paper

1. This second paper also examines whether teacher-report of family risk factors

predicts children’s later mental health problems. Specifically, there are three aims:

(3) To determine the extent and accuracy of teachers’ family background

knowledge and whether this knowledge varies by the child’s year level,

SES, gender, or behaviour.

(4) To determine the sources that teachers access when assessing a child’s

exposure to family risk factors.

(5) To determine the usefulness of teachers’ ratings of children’s exposure to

family risk factors by examining the relationship between teacher-rated

family risk factors and children’s future mental health outcomes.

Paper 3 (Chapter 7) explores the validity of both the parent and teacher

versions of the instrument, for use at an individual level. In this final paper, the

instruments are specifically used as ‘screening’ measures to identify individual, at-

risk children. This study has one aim:

(6) To compare the predictive accuracy of different parent and teacher

screening methods for identifying children at risk of developing

internalising versus externalising mental health problems.


3

Significance This research introduces a new screening instrument, the Family Risk Factor

Checklist (FRFC), designed to identify at-risk children on the basis of their exposure

to multiple family risk factors. The instrument is unique in its potential to identify

prevalent and influential risk factors at a population level, thereby informing

community intervention planning. This body of research therefore lays the

foundations for the development and implementation of effective preventive

interventions within school community settings.

A teacher version of the FRFC is used to determine the level of teachers’

family background knowledge. The identification of family risk factors that are

poorly identified by teachers in combination with information on the relative

importance of different risk factors engenders clear avenues for improving teachers’

ability to identify at-risk children. In addition, this combination of information is

essential in determining whether selective preventive interventions are feasible within

the school setting. If so, then the use of such a screening instrument may enable

earlier identification of at-risk children, prior to the development of serious

behavioural or emotional problems.

Finally, the performance of the new screening instrument in predicting

different types of mental health problems is examined in relation to the performance

of other screening methods for detecting individual children. This investigation

provides useful insights into the selection of optimal screening instruments for

targeted interventions and the ensuing discussion suggests ways of improving the

predictive performance of existing screening methods.

At a time when children’s mental health has become a major public health

issue, the current research aims to contribute to the success of prevention efforts

through enhancing our understanding of the risk and protective factors that influence

the development of mental health problems and evaluating screening methods for

identifying those individuals at elevated risk.

Background Definitions of Mental Health Problems

The term ‘mental health problems’ refers to a broad range of behavioural or

emotional difficulties that may cause concerns or distress (Raphael, 2000). They


4

range along a continuum from mild to severe problems. In this dissertation, children

were considered to have a mental health problem if the number of behavioural or

emotional problems they were experiencing was in the ‘clinical’ range as defined by

Achenbach’s (1991a) Child Behaviour Checklist (CBCL). The CBCL clinical cut-

offs have been shown to differentiate children who are attending mental health clinics

from the nonreferred population.

At the more severe end of the spectrum are ‘mental disorders’. These are

diagnosable conditions that significantly interfere with an individual’s cognitive,

emotional, or social functioning (Davis, Martin, Kosky, & O'Hanlon, 2000). Many

children with clinically significant mental health problems also meet the criteria for

mental disorders. This is consistent with observations that CBCL scores and more

formal diagnostic approaches are reasonably comparable in their assessment of child

psychopathology (Jensen, Watanabe, & Richters, 1996). In this dissertation, the term

‘mental health problems’ will encompass ‘mental disorders’.

Mental health problems may be grouped into two broad categories:

internalising and externalising mental health problems. Internalising problems are

characterised by inhibited or over-controlled behaviour (Sawyer et al., 2000).

Children with internalising problems are often quiet and withdrawn. The two most

common forms of internalising disorders are anxiety and depression. In contrast,

externalising problems are characterised by antisocial or under-controlled behaviour

(Sawyer et al., 2000). These children often display ‘acting out’ symptoms such as

noncompliance with requests, disruptiveness in class, and aggressiveness towards

other children. Conduct disorder (CD) [or oppositional defiant disorder (ODD) for

younger children] and attention deficit hyperactivity disorder (ADHD) are the two

most common externalising disorders.

Epidemiology of Children’s Mental Health Problems

Two large mental health surveys have recently been conducted in Australia

using the CBCL (Achenbach, 1991a). In a nationally representative sample of 4-17

year old children, the Child and Adolescent Component of the National Survey of

Mental Health and Wellbeing (Sawyer et al., 2000) obtained prevalence rates for

internalising, externalising, and total mental health problems of 12.8%, 12.9%, and

14.1%, respectively. In a representative sample (excluding Aboriginal children living


5

in country areas) of over 2700 Western Australian children, aged 4-16 years, the

Western Australian Child Health Survey (Zubrick et al., 1995) obtained a

corresponding prevalence rate for total mental health problems of 17.7%. (Rates for

the ‘broad band’ internalising and externalising mental health problems were not

reported.) The slightly higher prevalence of total mental health problems obtained in

the Western Australian study can be explained by methodological differences.

Whereas the results of the Western Australian Child Health Survey were based on the

combined reports of parent and teachers, the results of the National Survey of Mental

Health and Wellbeing were based solely upon parent report.

While there is evidence that rates of disorder increase with age across the

adolescent years (Fergusson, Horwood, & Lynskey, 1997a), the prevalence rates for

young children of same age range (4-8 years) as those who participated in the current

research are very similar to the figures reported above for the wider age range of 4-17

year old children. For children within the younger age group, the National Survey of

Mental Health and Wellbeing obtained rates of 12.0% for internalising mental health

problems, 13.3% for externalising problems, and 14.5% for total mental health

problems (B. Graetz, personal communication, 2002).

The local estimates described above for total mental health problems are

comparable to those obtained in other countries for children meeting criteria for at

least one psychiatric disorder. Across six community samples, including Dunedin,

New Zealand (Anderson, Williams, McGee, & Silva, 1987), Puerto Rico, USA (Bird

et al., 1988), Pennsylvania, USA (Costello et al., 1988), New York, USA (Velez,

Johnson, & Cohen, 1989), Ontario, Canada (Offord et al., 1987), and Zuid-Holland,

Netherlands (Verhulst, Berden, & Sanders-Woudstra, 1985), the prevalence rates for

children with one or more DSM-III (American Psychiatric Association, 1980)

diagnosis ranged from 17.6% to 26.0% (Costello, 1989; Verhulst & Koot, 1992). The

age ranges of children surveyed in these communities varied, with two studies

including children as young as 4 years old (Bird et al., 1988; Offord et al., 1987) and

one including young people up to 20 years of age (Velez et al., 1989). Thus, at any

one time, about one in five young people suffer from clinically significant emotional

or behavioural disorders.

Many children with mental health problems qualify for more than one

diagnosis. Of the children with CD, ADHD, or depressive disorder who participated

in the National Survey of Mental Health and Wellbeing (Sawyer et al., 2000), 23%


6

also had symptoms that met the criteria for a second disorder. A similar comorbidity

rate was obtained in the Dunedin Multidisciplinary Health and Development Study

for 15 year olds, based on adolescent self-report. In this study, 25% of adolescents

with a disorder met the criteria for at least one other DSM-III disorder (McGee et al.,

1990). In a second New Zealand cohort of 15 year old children, the comorbidity rate

was higher. The Christchurch Health and Development study found that 41% of

adolescents who met diagnostic criteria on the basis of maternal or child report, had at

least two diagnoses, and more than 10% of the identified children met criteria for

three or more diagnoses (Fergusson, Horwood, & Lynskey, 1993). The marked

discrepancy between the comorbidity rates obtained in the Christchurch Study versus

the rates obtained in the first two studies is easily explained by differences in the

criteria used to identify disorders across the studies. Whereas both the Australian and

the Dunedin studies relied upon diagnostic information provided by a single

informant (parents and adolescents, respectively), the figures reported in the

Christchurch study were obtained on the basis of either maternal or child report.

Comorbidity of internalising and externalising problems is not uncommon.

For example, Lewinsohn, Hops, Roberts, Seeley, and Andrews (1993) found a

significant degree of lifetime comorbidity of depression and disruptive behaviour

disorders in a community sample of adolescents. Of the children in the sample who

had had disruptive behaviour disorders, 34.4% of them had also had unipolar

depression. Conversely, 12.4% of the children who had had depression had also had

disruptive behaviour disorders.

Children with comorbid problems often have a worse prognosis than children

with discrete disorders. Clinically referred children with comorbid diagnoses of CD

and depressive disorder have a higher risk for adult criminality than depressed

children without CD (Harrington, Fudge, Rutter, Pickles, & Hill, 1991). Similarly,

Year 6 children with elevated levels of both conduct and depressive symptoms are

more likely to be using substances by Year 8 than children with either depressive or

conduct symptoms alone (Miller-Johnson, Lochman, Coie, Terry, & Hyman, 1998).

Pliszka (1989) provided further evidence for the poorer prognosis of comorbid

disorders. In this study, children with comorbid ADHD and anxiety responded more

poorly to stimulants than did children with ADHD alone. Regardless of their number

of diagnosable psychiatric conditions, children with mental health problems also

frequently exhibit difficulty in social relationships with their parents, peers, and


7

teachers and often have poor school performance (Offord, Boyle, Fleming, Munroe

Blum, & Rae-Grant, 1989; Sanford, Offord, Boyle, Peace, & Racine, 1992).

The onset of such difficulties may herald a lifetime of persistent psychosocial

problems. This is because early problem behaviour is one of the best predictors of

future mental health problems, particularly for externalising disorders (Loeber, 1991;

Lynam, 1996; Patterson, 1993). In a five year follow-up study of child psychiatry

clinic attendees, the odds of children having clinically significant externalising

problems at follow-up were 5.4 times higher for children with externalising problems

at baseline, relative to children who did not have externalising problems at baseline

(Mattison & Spitznagel, 1999). The odds ratio (OR) for the continuation of

internalising problems from baseline to follow-up was slightly lower at 4.3. Other

researchers have also demonstrated moderate to high stability of both internalising

and externalising problems across several samples (Campbell & Ewing, 1990;

Fergusson, Horwood, & Lynskey, 1995; Keller et al., 1992; Pianta & Caldwell, 1990;

Pianta & Castaldi, 1989; Rose, Rose, & Feldman, 1989). This stability is even

observed for mental health symptoms emerging in the first two years of life.

Temperamental difficulties, noncompliance, and aggression at 12 and 18 months of

age are associated with a variety of later problem behaviours (Keenan, Shaw,

Delliquadri, Giovannelli, & Walsh, 1998). Furthermore, the stability of mental health

problems increases substantially with the severity of the disorder (Loeber, 1982).

Cohen, Cohen, and Brook (1993) showed that over a two and a half year period, the

odds of receiving a diagnosis of CD at Time 2 were 13.9 times higher for children

with severe CD at Time 1, relative to children not meeting criteria for CD at Time 1.

In comparison, the corresponding ORs for moderate and mild CD were 7.8 and 3.1,

respectively. The same dose-response effect was found for anxiety disorders. The

ORs for the persistence of severe, moderate, and mild levels of anxiety were 17.3, 8.6,

and 3.3, respectively.

Finally, the costs of children’s mental health problems are very high. They

cause considerable distress to the young people affected and their families, and are

costly to society through demands on health, mental health, justice, welfare, and

education services (e.g., Potas, Vining, & Wilson, 1990). In Australia, in the early

1990s, the direct financial burden of mental disorders was estimated to be between $3

billion and $6 billion per year (Human Rights and Equal Opportunity Commission,

1993). It is likely that this has increased with the increasing prevalence of children’s


8

mental health problems (Rutter & Smith, 1995), but no recent economic data are

available.

Attempts to lower this ‘aggregate burden of suffering’ (Kosky & Hardy, 1992)

have relied primarily upon clinical services that provide treatment to individual

children one at a time (Offord, 1996). However, it is unlikely that this approach will

ever succeed in decreasing the prevalence of children’s mental health problems.

Indeed, the majority of children and adolescents with significant psychosocial

problems fail to receive specialist mental health care (Offord et al., 1987; Sawyer et

al., 2000; Zubrick et al., 1995) and it is clearly not possible to provide specialist care

for all those who may benefit (Sanders & Markie-Dadds, 1992; Verhulst, 1995).

In sum, children’s mental health problems are prevalent, often comorbid with

other disorders, persistent, and extremely costly to society in both human and

financial terms. They represent a serious public health problem (Commonwealth

Department of Human Services & Health, 1994; Nutbeam, Wise, Bauman, Harris, &

Leeder, 1993). Consequently, there have been calls for the development of alternative

services in primary care settings, and for a greater emphasis on preventive approaches

(American Psychological Society, 1996; Human Rights and Equal Opportunity

Commission, 1993; Raphael, 1993).

Prevention of Children’s Mental Health Problems Preventive interventions can be classified into three levels (Gordon, 1987;

Mrazek & Haggerty, 1994). Universal interventions are provided to entire

populations or cohorts, not identified on the basis of individual risk. Examples of

universal interventions include the Gatehouse Project (Patton et al., 2000), which aims

to enhance young people’s sense of ‘connectedness’ by addressing the policies and

social environments of participating schools, and Botvin’s (1996) life skills training

program to prevent substance abuse, initially provided to all grade 7 children in

participating schools.

Selective interventions are targeted towards groups or individuals whose risk

of developing a given disorder is higher than average, but who do not necessarily

have current behavioural symptoms. Examples include the Children of Divorce

Intervention Project (Pedro-Carroll & Cowen, 1985), in which elementary school

children with separated or divorced parents are targeted to reduce the negative


9

psychological impact of parental divorce, and the Comprehensive Child Development

Program (Pizzolongo, 1996), in which low income families are targeted in an attempt

to promote their self-sufficiency and educational achievement.

Indicated interventions are designed for individuals who have detectable

symptoms that could later develop into a more serious disorder. The Triple P

(Positive Parenting Program; Sanders, Markie-Dadds, Tully, & Bor, 2000b) and Fast

Track Program (Conduct Problems Prevention Research Group, 1992) are examples

of indicated interventions. Both of these programs involve working with the

caregivers of young children who already have behavioural or emotional symptoms.

While there is likely to be an important role for all three levels of prevention,

selective interventions have unique advantages over the other two. Specifically, they

may be more economical than universal interventions because they direct resources

towards high risk subgroups of the population who need them most, thereby

potentially minimising the risk of iatrogenic effects. Compared with indicated

interventions, selective programs may intervene earlier in the developmental pathway

of disorder, before behavioural or emotional symptoms have emerged, thereby

preventing the considerable distress that many individuals entering an indicated

program will already have suffered. Another advantage of selective interventions is

that they focus attention on the underlying causes of mental health problems rather

than the associated symptoms. This is essential for effective preventive interventions

(Dishion, Andrews, Kavanagh, & Soberman, 1996), and may serve to reduce some of

the negative effects associated with labelling and stigmatisation of children that can

occur with indicated programs. However, selective interventions also have

disadvantages. The major obstacle for selective interventions is the limited accuracy

of current screening methods to identify at-risk children who do not display

significant behavioural symptoms.

Schools as a Setting for Prevention Schools have long been recognised as a setting for promoting children’s

physical health. In particular, considerable research has assessed the impact of

school-based preventive strategies for cardiovascular disease, skin cancer, and unsafe

sex practices (e.g., Coyle et al., 2001; Perry et al., 1990; Tripp, Herrmann, Parcel,

Chamberlain, & Gritz, 2000). Consistent with WHO’s recommendations for ‘Health


10

Promoting Schools’ (World Health Organisation, 1995), successful intervention

efforts have employed coordinated strategies across multiple levels of school

organisation, including the school curriculum, school ethos (including the physical &

social environments & policies & practices of the school), and school-community

links. Such multi-component interventions are believed necessary to address the

multiple risk factors responsible for sustaining many health problems, thereby

providing the environmental or structural changes needed to facilitate and maintain

changes made at the individual level (Allensworth, Lawson, Nicholson, & Wyche,

1997; Parcel, Kelder, & Basen-Engquist, 1998).

It is only in more recent years that schools have been identified as a key

setting for the delivery of preventive mental health interventions (Adelman & Taylor,

1998; Nutbeam et al., 1993; World Health Organisation, 1990). However, the logical

appeal and suitability of promoting children’s mental health through schools has

already seen a burgeoning of research in this area both in Australia and overseas (e.g.,

Botvin, 1996; Burns & Hickie, 2002; Cowen et al., 1996; Dishion et al., 1996;

Greenberg, Domitrovich, & Bumbarger, 2001; Conduct Problems Prevention

Research Group, 1992; Patton et al., 2000). In Western society, schools are a key

force, second only to the family, in shaping the child’s early mental health and

development (Cowen et al., 1996). In primary schools, the majority of children have

extensive contact over the course of the year with a single teacher, and this affords

excellent opportunities for monitoring individual well-being and detecting problems

at an early stage (Nicholson, McFarland, & Oldenburg, 1999a; Nicholson, Oldenburg,

McFarland, & Dwyer, 1999b). For young people exposed to adverse family or social

conditions, schools have the potential to promote resilience by providing stability,

predictability, and appropriate models of behaviour (Alexander & Entwisle, 1996).

Furthermore, both young people (Zubrick et al., 1995) and education professionals

(Nicholson et al., 1999b) view school personnel as appropriate sources of help.

Zubrick et al.’s (1995) study of mental health problems in Western Australia revealed

that 38% of distressed young people talked to a school-based professional about their

problems, in contrast with only 4% who accessed specialist mental health services.

An additional reason for addressing children’s mental health problems in the

school setting concerns the impact of these problems on others. Teachers report that

children’s psychosocial problems detract from their ability to teach, impede learning

opportunities for other children, and can be personally distressing to teachers


11

(Nicholson et al., 1999b). As such, schools have a key role to play in fostering child

adjustment. The success of selective preventive interventions within the school

setting will depend upon teachers’ understanding of the risk and protective factors to

which children in their community are exposed and the availability of a valid

screening method for identifying children exposed to these risk factors. Risk factors

and identification methods are now reviewed in turn.


12

CHAPTER 2 - RISK FACTORS FOR CHILDREN’S

MENTAL HEALTH PROBLEMS

Definition of ‘Risk’ and ‘Protective’ Factors A risk factor is defined as any characteristic, experience, or event that

increases the probability (risk) of a particular outcome (e.g., mental health problem)

relative to the base rate of the outcome in the general (unexposed) population

(Kazdin, Kraemer, Kessler, Kupfer, & Offord, 1997; Kraemer et al., 1997; Mrazek &

Haggerty, 1994). For example, harsh parenting practices or conflict in the home are

risk factors for children’s mental health problems because children who are exposed

to them are more likely, than unexposed children, to develop mental health problems

(Patterson, 1982; Patterson, De Baryshe, & Ramsey, 1989).

In parallel use to the term ‘risk factors’, protective factors may be

conceptualised as conditions, skills, or events that decrease the likelihood of

undesirable outcomes or increase the likelihood of positive outcomes (Kazdin et al.,

1997; Patterson, 1982; Patterson et al., 1989). However, risk and protective factors

are not necessarily each other’s opposites (Durlak, 1998). For example, the opposite

poles of harsh parenting practices or conflict in the home are, respectively, decreased

parental punitiveness and decreased conflict, but these do not imply either warmth or

family harmony (protective factors). The term ‘protective factor’ is also commonly

used in conjunction with the concept of resilience to refer to conditions, skills, or

events that promote positive outcomes in the face of adversity. In this respect,

protective factors act to reduce the negative impact of risk factors on individuals or

groups identified as at-risk for a particular outcome (Kazdin et al., 1997; Spence,

1998). For example, effective social skills, which enable a young person to maintain

relationships with well-adjusted peers, provide protection against a trajectory toward

delinquency in boys identified as aggressive (Kazdin et al., 1997; O'Donnell,

Hawkins, & Abbott, 1995; Spence, 1998). While the overt focus of this research is on

the identification of risk factors, the contribution of protective factors is implicit

throughout much of the discussion.

The importance of a risk (or protective) factor for prevention purposes is

dependent upon the status of its relationship with the outcome. Risk factors may be


13

described as correlates, risk factors, markers, or causal risk factors, on the basis of

empirical findings delineating their relationship with mental health outcomes. These

relationships are defined and discussed in detail by Kraemer et al. (1997) and Kazdin

et al. (1997). A brief summary is provided below.

Correlates are associated with the outcome at a given point in time, without

any implication of a temporal ordering of antecedent and outcome. Correlates are

typically identified through cross-sectional studies where directionality of the

relationship between antecedent and outcome cannot be established.

In comparison, risk factors are not only associated with the outcome, they

have been shown to precede the outcome. Risk factors are established through

prospective, longitudinal research where it can be shown that a characteristic, in the

absence of the outcome at one point in time, is related to the development of the

outcome at a later time.

Marker variables are risk factors that are unchangeable (fixed) or malleable

(variable), but if changed, do not influence the likelihood of the outcome. That is,

marker variables are not causally related to the outcome.

Finally, causal variables are risk factors that, if manipulated, produce a

change in the outcome. Randomised controlled trials, are typically used to prove a

causal link between antecedent and outcome. In these studies, participants are

randomly assigned to a particular intervention or a control condition. If the

intervention produces a change in the outcome, then the risk factor that was addressed

is said to be causally related to the outcome. Another strategy commonly used to

investigate whether a risk factor is causally involved in an outcome is to test for

potential dose-response relationships. The finding that greater exposure to a risk

factor produces a higher incidence or severity of a subsequent outcome is consistent

with (but not sufficient to prove) a causal relation.

The ultimate goal for prevention research is to identify causal risk factors

(Kraemer et al., 1997), for it is through addressing these factors that the greatest gains

to children’s mental health may be achieved. However, concern with demonstrating

causality represents only part of the literature on antecedent-outcome relationships.

Also of interest is the identification of mediating and moderating factors that further

define the nature of the relationship between antecedent and outcome. Mediating

factors are processes or mechanisms through which a given risk factor may operate to

produce an outcome (Baron & Kenny, 1986; Kazdin et al., 1997; Kerig, 1998). That


14

is, mediators play a causal role in the relationship between two variables. For

example, parental depression is a risk factor for children’s mental health problems

(Downey & Coyne, 1990). Yet, this relationship is at least partly due to the

potentially detrimental impact that parental psychopathology has on parenting

practices (Cummings & Davies, 1991). Thus, parenting practices mediate the

relationship between parental psychopathology and children’s mental health problems

(Harnish, Dodge, Valente, & Conduct Problems Prevention Research Group, 1995).

Moderating factors, on the other hand, are those variables that influence the

degree or direction of the relationship between risk factor and outcome but are not

responsible for causing the observed relationship (Baron & Kenny, 1986; Kazdin et

al., 1997; Kerig, 1998). For example, exposure to negative life events is a risk factor

for depressive symptoms in children. However, children with good social problem

solving skills report lower levels of depression when exposed to life stress than

children with less effective problem solving skills (Goodman, Gravitt, & Kaslow,

1995). Therefore, problem solving skills can be said to moderate the relationship

between negative life stress and depression.

A final distinction to be made is the proximal versus distal influence of risk

factors. Proximal factors are close to the individual and/or close in time to the

outcome (Stanley, Sanson, & McMichael, 2002), and therefore often represent

mediating variables that are more malleable and influential than their distal

counterparts. Distal factors are further removed from the individual in either time or

place. In the above example, parenting practices constitute the proximal influence,

while parental psychopathology is the more distal factor. Family socioeconomic

status is commonly described as a distal factor (e.g., Stanley et al., 2002).

Types of Risk (and Protective) Factors Epidemiological research has lead to the identification of an array of

individual and environmental risk and protective factors for children’s mental health

problems. A number of researchers have recently reviewed the range of risk and

protective factors that have been linked to negative outcomes (Dadds, Seinen, Roth, &

Harnett, 2000; Davis, Martin, Kosky, & O'Hanlon, 2000; Doll & Lyon, 1998; Durlak,

1998; Fergusson, Horwood, & Lynskey, 1997; Lewinsohn et al., 1994; National

Crime Prevention, 1999; Sanders, Gooley, & Nicholson, 2000a; Spence, 1996;


15

Spence, 1998; Vimpani, Patton, & Hayes, 2002). Tables 2.1 and 2.2 present a

synthesised list of the risk and protective factors identified from these reviews. Risk

and protective factors for internalising and externalising mental health problems have

been listed together due to the generic nature of many factors. It should be noted that

some arbitrary decisions have been made in positioning several of the risk and

protective factors. For example, security of attachment has been listed as a child

factor, however, it could just as appropriately have been conceptualised as a family

factor. While many of the factors listed have obtained the status of ‘risk factor’ (i.e.,

precede the outcome), many of them have not (yet) been demonstrated to be causal

risk factors.

As can be seen from Table 2.1 and 2.2, risk and protective factors may be

characteristics of the child or his or her environment. Environmental risk factors

operate at several levels of influence, including the family, school context, and

community, as well as life events that impact upon the child. For illustrative

purposes, examples are given below of the research that has identified several of the

better-known risk and protective factors.

Child factors. There are many characteristics of the individual child that

increase the probability of poor mental health outcomes. For example, a child’s

cognitive style and skills may increase his or her risk. On the one hand, children with

negative cognitions about their self, the world, or the future are more vulnerable to

depression (Jaycox, Reivich, Gillham, & Seligman, 1994) or anxiety (Kendall, 1991).

One the other hand, children with hostile attributions concerning the actions of others

or positive evaluations of aggression and its outcomes, show increased rates of

aggressiveness and conduct disorder (Dodge, 1993; Lochman & Dodge, 1994).

Furthermore, cognitive biases such as these have been causally linked with mental

health outcomes, such that experimental manipulation of these processes has resulted

in reductions in internalising and externalising mental health problems (Dodge, 1993).

Children with poor problem solving skills are also at increased risk. Although

a child’s problem solving ability is partly determined by their intelligence, itself a risk

or protective factor (Fergusson & Horwood, 1995), Evans and Short (1991) showed

that boys with conduct disorder generated significantly fewer effective alternative

responses to hypothetical stories than non-conduct disordered boys, even after

accounting for the effects of intelligence and verbal reasoning skills.


16

Related to children’s cognitive and problem solving abilities are their social

skills, a lack of which may also place children firmly on the path to psychopathology.

Social competence refers to children’s ability to accurately interpret social cues,

respond appropriately to others, and to show assertiveness and resist peer pressures

when necessary. It is reflected in the degree to which children develop and maintain

satisfactory interpersonal relationships with peers and adults (Gresham, 1998). Social

competence has variously been described as a risk (Elliot & Gresham, 1993; Greene

et al., 1999) or protective factor (Luthar, 1991) depending upon its absence or

presence, respectively. It is likely that deficits in social competence result in peer

rejection (Newcomb, Bukowski, & Pattee, 1993), which further decreases a child’s

opportunities for learning appropriate social behaviour or integrating successfully

with a prosocial peer group (Patterson, Capaldi, & Bank, 1991).

Another important predictor of mental health outcomes is children’s

attachment style. The concept of attachment refers to reciprocal bonds between

parent and child from infancy (Sanders et al., 2000a) and is reflected in the quality of

the parent-child relationship. Infants who are insecurely attached, as operationalised

by clingy or avoidant behaviour during the ‘Strange Situation’ observational

assessment task (Ainsworth, Blehar, Waters, & Wall, 1978), are at increased risk for

developing later internalising (Warren, Huston, Egeland, & Sroufe, 1997) and

externalising problems (Greenberg, Speltz, & DeKlyen, 1993).

Gender is an example of a fixed risk marker. In fact, it is a robust predictor

for particular types of disorder (Sanders et al., 2000a). During middle childhood,

males are more likely than females to develop externalising problems (Prior, Smart,

Sanson, & Oberklaid, 1993), however, during adolescence, females are at higher risk

of developing depression and anxiety than males (Fergusson et al., 1997a; Lewinsohn

et al., 1993).

Perhaps though, of all the individual child factors, the single, best predictor of

later mental health problems is early behavioural or emotional problems (Fergusson &

Horwood, 1993; Fergusson et al., 1995; Keenan et al., 1998; Loeber, 1991; McGee,

Partridge, Williams, & Silva, 1991; Monohan, 1981). Children displaying early

anxiety or depressed symptoms are at increased risk of developing later internalising

disorders (Kovacs & Devlin, 1998) and children with early hyperactive, impulsive, or

disruptive symptoms are at increased risk of developing later externalising problems

(Campbell, 1995). Such early behavioural symptoms may represent a continuation of


17

earlier temperamental characteristics, which similarly convey increased vulnerability

for later mental health problems. A difficult temperament in infancy, characterised by

irritability, high levels of crying, high activity levels, and irregular sleep and eating

patterns, is predictive of externalising behaviour problems in later childhood (Caspi,

Henry, McGee, Moffitt, & Silva, 1995; Prior, 1992). In contrast, ‘behavioural

inhibition’, a temperamental style characterised by shyness, withdrawal, and

emotional restraint when exposed to unfamiliar people or places (Asendorpf, 1993;

Kagan, Reznick, Clarke, Snidman, & Garcia-Coll, 1984) predisposes children to

internalising disorders (Biederman et al., 1993; Hirshfeld et al., 1992; Rosenbaum et

al., 1993).

It has been suggested that the clear continuities that exist between early

temperament, later behavioural and emotional problems, and subsequent mental

health disorders may reflect common genetic influences (Rutter et al., 1990).

Alternatively, these continuities may occur because of interacting individual and

environmental factors that act to sustain and exacerbate mental health problems

throughout the life course (Fergusson et al., 1997a). Evidence for a genetic

contribution to risk of mental health problems is suggested in the strong familial

associations for childhood internalising and externalising disorders. Parents with

mental health disorders are more likely than parents without psychopathology to have

offspring who also develop mental health problems (Fergusson, Horwood, &

Lynskey, 1994; Kazdin, 1987; Turner, Beidel, & Costello, 1987; Weissman et al.,

1987; Weissman, Leckman, Merikangas, Gammon, & Prusoff, 1984; West & Prinz,

1987). Conversely, children with mental health problems are more likely than

children without problems to have parents who meet the criteria for a psychiatric

diagnosis (Last, Hersen, Kazdin, Francis, & Grubb, 1987). However, such familial

associations may also be explained by common environmental factors and are

therefore not enough, by themselves, to prove a genetic contribution. More

convincing demonstrations of hereditary have been provided by twin studies, showing

an increased rate of disorder between monozygotic than dizygotic siblings (Schmitz,

Fulker, & Mrazek, 1995; Wierzbicki, 1987). Nevertheless, Fergusson et al. (1994)

argue that “whilst genetic factors may play some role in predisposing young people to

multiple problem behaviours, the effects of a disadvantaged, disorganised and

dysfunctional childhood probably make a far greater contribution to the development


18

of such behaviours” (p. 1136). The influence of the family environment is discussed

next.

Family factors. Of all the environmental variables, family risk factors are

believed to make the greatest contribution to young children’s mental health

(Richman, Stevenson, & Graham, 1982; Sanders, 1995). In particular, poor parenting

practices arguably have the strongest and most direct impact upon the development of

a range of negative mental health outcomes in early and middle childhood, including

anxiety (Dadds & Roth, 2001), depression (Kaslow & Racusin, 1994), conduct

disorder (Dadds, 1997; Patterson, 1996; Sanders & Markie-Dadds, 1992), and ADHD

(Barkley, 1989). Children exposed to excessively harsh, abusive, or rejecting

parenting practices are at increased risk relative to children whose parents have a less

coercive discipline style (Kazdin, 1987; Patterson, 1982; Patterson et al., 1991;

Patterson et al., 1989). Similarly, children whose parents are uninvolved, lacking in

warmth, failing to provide adequate supervision, or neglectful are also at substantially

higher risk of developing negative outcomes than children whose parents are more

responsive and nurturing (Bradley, Caldwell, & Rock, 1988; Fergusson et al., 1994;

Pettit & Bates, 1989). Several of these parenting factors, including poor supervision,

parental rejection, and ‘family management techniques’ have been found to predict

delinquency as strongly as children’s current behaviour. Loeber & Dishion (1987)

rank ordered factors predicting delinquency and found that composite measures of

parental management techniques showed stronger associations with delinquency than

any of the behavioural predictors.

Interestingly, parental over-control or overprotectiveness is also a risk factor.

Children exposed to an overly restrictive parenting style are more likely to develop

anxiety disorders than other children (Rapee, 1997). It is thought that this type of

parenting style may decrease children’s opportunities to learn to solve problems for

themselves, with the consequence that these children do not learn to deal successfully

with stressful life experiences (Krohne & Hock, 1991).

Parent training interventions have reliably achieved decreases in children’s

behavioural and emotional symptoms (Hawkins, VonCleve, & Catalano, 1991;

Sanders et al., 2000b; Webster-Stratton, 1996), demonstrating the causal relationship

between parenting factors and children’s mental health. Indeed, effective parenting

may be a powerful protective factor irrespective of broader family, school, and

community conditions. The research evidence suggests that the presence of a warm,


19

supportive relationship with at least one parent protects children against the effects of

multiple environmental risk factors (Jenkins & Smith, 1990; Pettit, Bates, & Dodge,

1997; Seifer, Sameroff, Baldwin, & Baldwin, 1992; Werner, 1989; Wyman, Cowen,

Work, & Parker, 1991).

Parental psychopathology is another strong predictor of mental health

problems in children (Downey & Coyne, 1990; Lynskey, Fergusson, & Horwood,

1994; Rutter et al., 1990). This may be partly due to genetic influences or the effects

of parents modelling antisocial, depressed, or anxious behaviours to their children.

However, there is evidence that the influence of parental psychopathology on

children’s mental health is at least partially mediated through parenting factors (e.g.,

Harnish et al., 1995; Snyder, 1991). For example, depressed parents respond to their

children with less warmth, supervise their children’s activities less effectively, and are

generally less responsive, less consistent, and more irritable and harsh in their

interactions with their children than non-depressed parents (Gelfand & Teti, 1990;

Hops, 1992).

Children exposed to marital discord and high levels of physical or verbal

conflict between parents are more likely to develop mental health problems than

nonexposed children (Forehand et al., 1987; Grych & Fincham, 1990; Loeber &

Stouthamer-Loeber, 1986; Peterson & Zill, 1986; Sanders, Nicholson, & Floyd,

1997b). There are complex interactions that occur between marital discord, family

conflict, parental psychopathology, and parenting practices. Marital difficulties and

family conflict are likely to affect parents’ mood and level of psychopathology, and

similarly parental psychopathology is likely to increase discord and conflict (Spence,

1998). Marital discord may have a direct impact on children if they openly observe

the conflict between parents or an indirect impact through its influence on parental

mood or parenting practices (Cummings, Zahn-Waxler, & Radke-Yarrow, 1981;

Emery, 1982; Emery, Fincham, & Cummings, 1992).

Finally, the relationship between socioeconomic disadvantage and mental

health problems deserves mention. Researchers have consistently found that children

from lower SES families have higher rates of behavioural and emotional problems

than children from higher SES families (Farrington, 1978; Farrington, 1991;

Fergusson, Horwood, & Lawton, 1990; Patterson, Kupersmidt, & Vaden, 1990;

Rutter, 1981). It seems likely that this relationship is mediated primarily through

variables such as parenting practices and parental psychopathology (Dodge, Pettit, &


20

Bates, 1994; Patterson, 1996). Nevertheless, even after accounting for the effects of

numerous family factors, SES still explains unique variation in mental health

inequalities (Fuligni & Brooks-Gunn, 2000).

School context. Peer and school factors play a progressively more important

role as children get older (Englund, Levy, Hyson, & Sroufe, 2000; Hartup, 1992;

Mrazek & Haggerty, 1994). By adolescence, children are highly motivated by the

need to ‘fit in’ and for acceptance. However, children with a combination of

disruptive behaviours, poor social skills, and/or negative cognitive style are

predisposed to poor peer relationships and peer rejection (Loeber, 1990). If teenagers

are unable to gain support or acceptance from parents or prosocial peers, then the

result is often association with deviant peers (Ary, Duncan, Duncan, & Hops, 1999;

Brendgen, Vitaro, & Bukowski, 2000; Dishion, Patterson, Stoolmiller, & Skinner,

1991), a powerful predictor of delinquency (Brendgen et al., 2000; Keenan, Loeber,

Zhang, Stouthamer-Loeber, & van Kammen, 1995; Snyder, Dishion, & Patterson,

1986). It is also at this age that children with poor social or problem solving skills or

who stand out in some way are susceptible to being bullied (Carney & Merrell, 2001;

McClure & Shirataki, 1989). Within these contexts, the likelihood of new or

continuing adjustment problems is high. By the same token, peer friendship and

acceptance can serve as an important protective factor for at-risk children. Criss,

Pettit, Bates, Dodge, and Lapp (2002) found that peer acceptance moderated the

impact of negative family factors such as ecological disadvantage, violent marital

conflict, and harsh discipline on children’s externalising behavior. For children with

high levels of positive peer relationships, family adversity was not significantly

associated with externalising behaviour.

Academic problems also seem to be associated with poor outcomes. Although

conduct problems frequently predate academic problems (McGee, Share, Moffitt,

Williams, & Silva, 1998; McMichael, 1979; Stott, 1981), longitudinal research has

also demonstrated that reading delay can predispose children to delinquent outcomes

(Maugham, Gray, & Rutter, 1985).

The importance of the school environment to children’s mental health has also

been recognised (e.g., Patton et al., 2000). Poor quality schools are characterised by

poor working conditions, low teacher expectancies, lack of praise and recognition,

inadequate behaviour management, and poor relationships amongst teachers and

students (Durlak, 1998). Under such conditions, children are at higher risk of


21

academic failure, school dropout, and mental health problems (Durlak, 1998). In

comparison, schools with a positive school climate, characterised by a sense of

belonging and connectedness enhance children’s resilience and decrease the risk of

mental health problem development (e.g., Resnick, Harris, & Blum, 1993). Indeed,

positive school environments may act as important compensatory mechanisms for

children from disadvantaged family backgrounds (Alexander & Entwisle, 1996).

Community and cultural factors. The research into community- and cultural-

level determinants of mental health is at an earlier stage of scientific sophistication

than research into individual-, family-, and school-level factors (Institute of Medicine,

2000). However, there is increasing recognition of the impact of these more

‘upstream’ factors on children and families (e.g., Stanley et al., 2002). Community

factors such as lack of employment opportunities, lack of access to support services,

cultural discrimination, and neighbourhood disorganisation, violence, and crime have

all been identified as contributing to the burden of mental health problems (e.g.,

Elliott & Menard, 1996; Leventhal, 2000; Rutter & Giller, 1983).

Life events. Exposure to stressful life events is also related to the

manifestation of mental health symptoms. Anxious children have been found to

experience a greater rate of environmental stressors than non-anxious children

(Benjamin, Costello, & Warren, 1990; Bernstein & Hoberman, 1989; Goodyer &

Altham, 1991; Kashani et al., 1990). Examples of life events that have been linked

with elevated rates of mental health problems include divorce or family break-up

(Atkeson, Forehand, & Rickhard, 1982; Emery, 1982; Emery, 1988; Fergusson et al.,

1990; Hetherington, Cox, & Cox, 1979; Wallerstein, 1983), bereavement (Berlinsky

& Biller, 1982), natural disasters, such as bush fires or violent storms (Dollinger,

O'Donnell, & Staley, 1984; McFarlane, 1987), and life transitions, such as the change

from primary to high school (Blyth, Simmons, & Carlton-Ford, 1983; Felner,

Primavera, & Cauce, 1981; Seidman, Allen, Aber, Mitchell, & Feinman, 1994;

Wigfield, Eccles, MacIver, Reuman, & Midgley, 1991).

The effects of each of these environmental stressors are likely to be at least

partly mediated through their impact on parent-child relations (Dadds et al., 2000).

For example, parental divorce frequently has negative effects on parental mood and

consistency of parenting practices and may be accompanied by disruptions to

children’s daily routines (Emery, 1982). Children’s adjustment to traumatic natural

disasters is predicted by mothers’ responses to the event, with overprotective styles of


22

parenting being associated with more post-traumatic symptoms in children

(McFarlane, 1987).

In summary, a large body of research now exists that identifies the risk factors,

and more recently, the protective factors for children’s mental health problems. Of all

the risk factors identified, family risk factors such as parental psychopathology,

marital discord, and in particular, parenting practices, have the greatest influence on

young children’s mental health, and their status as causal risk factors has been clearly

demonstrated. Furthermore, due to their proximal relationship to children’s mental

health outcomes, family factors are frequently the proposed mediators through which

other variables operate. Prior to discussing the implications of this risk factor

research for the development of effective screening methods, further detail is provided

on the nature of risk and protective factors.

Table 2.1

Risk Factors Associated with Children’s Internalising and/or Externalising Mental Health Problems

Child Factors Family Factors School Context Community & Cultural Factors Life Events

Prematurity

Low birth weight

Genetic influences

Gender

Disability

Prenatal brain damage

Birth injury

Low intelligence

Difficult temperament

Predisposition to shyness/behavioural inhibition

Chronic illness/poor physical health

Insecure attachment

Poor problem solving

Beliefs about aggression

Cognitive style - negative attributions, cognitive distortions, pessimism

Poor social skills

Low self-efficacy

Parental characteristics:

Teenage mothers

Single parents

Step-parents

Parental psychopathology, e.g., depression

Negative cognitive style

Substance abuse

Criminality

Antisocial models

Poor health/nutrition

Low parental education

Family environment:

Family conflict, violence or disharmony

Marital discord

Disorganised

Social isolation

Large family size

Father absence

School failure

Negative school climate

Dissatisfaction with grades

Failure to complete homework

Normative beliefs about aggression

Deviant peer group

Bullying

Peer rejection

Poor attachment to school

Inadequate behaviour management

Low teacher expectancies

Lack of praise and recognition

Little emphasis on individual responsibility

Little emphasis on academic work

Poor working conditions

Unavailability of teacher to deal with problems

Socioeconomic disadvantage

Population density and housing conditions

Urban area

Neighbourhood disorganisation

Neighbourhood violence and crime

Cultural norms concerning violence as acceptable response to frustration

Media portrayal of violence

Lack of access to support services

Social or cultural discrimination

Lack of employment or educational opportunities

Indigenous, immigrant, or minority status

Ineffective social policies

Parental separation and divorce

War or natural disasters

Death of a family member

Illness of family member

Life transitions (e.g., change of school or residence)

Stress

Risk Factors for C

hildren’s Mental H

ealth Problems

23

Table 2.1 cont.


Lack of empathy

Alienation

Hyperactivity

Impulsivity

Learning & language difficulties

Early or current behavioural or emotional problems

Long-term parental unemployment

Low SES/poverty

Presence of sibling with antisocial behaviour

Parenting style:

Poor supervision and monitoring of child

Harsh discipline style

Inconsistent discipline

Lack of rules or limits

Rejection of child

Abuse

Lack of warmth and affection

Low involvement in child’s activities

Neglect

Maternal overprotection or over-control

Child care by someone other than the mother

Large class sizes

Few parent-teacher meetings

Poor relationships with teachers

Sources: Dadds et al. (2000); Davis et al. (2000); Doll & Lyon (1998); Durlak (1998); Fergusson et al. (1997a); Lewinsohn et al. (1994); National Crime Prevention (1999);

Sanders et al. (2000a); Spence (1996; 1998); Vimpani et al. (2002).

Risk Factors for C


ealth Problems

24

Table 2.2

Protective Factors Associated with Children’s Internalising and/or Externalising Mental Health Problems


Social competence

Above average intelligence

Secure attachment to family

Empathy

Problem solving skills

Optimism/positive future expectations

Easy temperament

Internal locus of control

Moral beliefs/faith

Values

Self-related cognitions

Good coping style

Self-efficacy

Early history of positive functioning

Supportive, caring relationship with at least one parent

Family harmony

More than two years between siblings

Responsibility for chores or required helpfulness

Secure and stable family

Supportive relationship with adult outside of family

Small family size

Strong family norms & morality

Social support

Effective parenting - warmth, structure, and high expectations

Parental supervision/regulation of activities outside the home

Higher level of parental education

Higher socioeconomic status

School achievement

Positive school climate

Close friendships with prosocial peers

Responsibility and required helpfulness

Sense of belonging or connectedness

Opportunities for some success at school and recognition of achievement

School norms re violence

Larger number of classroom friends

High quality child care

Warm and open relationships with teachers

Access to support services

Community networking

Attachment to the community

Participation in church or other prosocial organisation/ engagement in productive activities

Community/cultural norms against violence

A strong cultural identity and ethnic pride

Many opportunities for education & employment

Effective social policies

Meeting a significant person

Moving to a new area

Opportunities at critical turning points or major life transitions

Sources: Dadds et al. (2000); Davis et al. (2000); Doll & Lyon (1998); Durlak (1998); Fergusson et al. (1997a); Lewinsohn et al. (1994); National Crime Prevention (1999);

Sanders et al. (2000a); Spence (1996; 1998); Vimpani et al. (2002).

Risk Factors for C


ealth Problems

25

Identification of At-risk Children

26

Nature of Risk (and Protective) Factors The relationships between risk factors and mental health outcomes are

complex, and despite considerable progress in specifying developmental pathways

(e.g., Loeber, 1990), they are still poorly understood (Durlak, 1997). Nevertheless, on

the basis of existing evidence, several conclusions can be drawn about the nature of

risk and protective factors, as follows:

1. Particular risk factors are rarely specific to the development of a single

disorder (Coie et al., 1993; Mrazek & Haggerty, 1994). Rather, the same risk

factor can produce a range of mental health outcomes (multifinality) and a

given mental health outcome can develop from a variety of different risk

factors (equifinality) (Cicchetti & Rogosch, 1999; Sroufe, 1989). The generic

(‘multifinal’) nature of risk factors is illustrated by the example of parental

divorce which may result in one child becoming upset, tearful and withdrawn,

and another child becoming argumentative, defiant and aggressive.

Equifinality occurs when exposure to diverse risk factors such as poor

parenting practices, parental psychopathology, or family violence all increase

the risk of developing a particular mental health outcome, such as conduct

disorder.

2. Risk status increases multiplicatively with exposure to an increasing number

of risk factors (Coie et al., 1993; Durlak, 1997; Fergusson & Lynskey, 1996;

Rutter, 1978; 1979; Spence, 1998; Werner, 1989). In the example given

above, parental divorce increases the risk of developing both internalising and

externalising problems. However, these outcomes are more likely if parental

divorce is accompanied by other risk factors such as disrupted parent-child

relationships, and family conflict (Sanders, Markie-Dadds, & Nicholson,

1997a).

3. It has also been observed that the salience of risk factors may fluctuate

developmentally (Coie et al., 1993). As described above, family risk factors,

in particular, tend to have a greater impact while children are young and are

amongst the strongest predictors of subsequent psychological distress for this

age group (Richman et al., 1982). In contrast, school and peer factors become

more important at older ages.


27

4. Risk factors and mental health problems are transactional in nature, such that

there is a continuous, dynamic interaction between the child and his or her

environment (Cicchetti & Richters, 1993; Davis et al., 2000; Early, Gregoire,

& McDonald, 2002; Sameroff & Chandler, 1975). The transactional model of

development posits that not only does the environment shape the child, but

that the child may also shape the nature of the risk and protective factors to

which he or she is further exposed. For example, an infant with a difficult

temperament or conduct problems may trigger negative parenting responses

(Barkley, 1989; Dumas, 1989), resulting in poor parent-child attachment,

which, in turn, further increases the likelihood of maladjustment as the child

gets older. Thus, the child and caregiver have a mutual, interactive influence

on each other.

5. Some risk factors are more amenable to change than others. So far, the risk

factors that have shown the greatest promise in this regard appear to be the

more proximal factors, such as family functioning (e.g., Sanders, 1999) and

children’s cognitive style (Gillham, Reivich, Jaycox, & Seligman, 1995;

Jaycox et al., 1994).

6. The risk factors that are related to the onset of mental health problems may be

different to the risk factors that predict the persistence of psychopathology.

Although often ignored, this is an important distinction, because knowledge of

the variables associated with the onset of problems is crucial for the purposes

of effective prevention, while identification of the factors associated with

continuing problems is more important for successful treatment (Zubrick,

Silburn, Burton, & Blair, 2000a). To date, there has been little research that

has explored these distinctions.

7. Finally, it should be remembered that many children who are exposed to high

risk environments do not develop mental health problems (National Crime

Prevention, 1999; Spence, 1998). The study of these children may help to

elucidate the protective mechanisms responsible for such resilience.

A model of risk and protective factors, depicting their relationship with poor

child mental health outcomes, is presented in Figure 2.1. To simplify the model,

school and community factors have been grouped together under the heading of

‘social factors’, and only a few examples of risk and protective factors have been

included at each level. Life events are not represented in the model because they


28

Family & Social • Family structure & size • Low SES • Parental

psychopathology • School climate • Neighbourhood crime

operate across all levels of risk (Durlak, 1998). The model distinguishes between

distal and proximal determinants. Closer inspection of the distal risk factors suggests

that these variables are typically more difficult to modify than the proximal factors.

In addition, the model depicts several important features of the relationship between

risk and mental health outcomes, including its representation of: (1) the different

types of risk factors operating at different levels of influence, (2) the way in which the

impact of certain risk factors on mental health is mediated by other more proximal

factors, and (3) the importance of family risk factors as mediating influences in the

development of young children’s mental health problems. Not represented in the

model is the developmental complexity of the process or the transactional nature of

the relationships between risk factors and children’s behaviour. The model is

therefore best conceptualised as a cross-sectional snapshot of the aetiology of

children’s mental health problems.

Figure 2.1. Model of the development of mental health problems in children.

Child • Genetic influences • Temperament • Intelligence

Distal Determinants

Child • Cognitive style • Problem solving skills• Social skills

Proximal Determinants (Mediators)

Mental Health Outcomes

Family • Parenting practices • Supportive parent-

child relationship • Marital discord

Social • Relationship with peers

& adults • Sense of belonging &

connectedness

Internalising problems • Depression • Anxiety Externalising problems • CD • ADHD


29

Implications for Screening Methods Risk factor research has a number of implications for the identification of

children at increased risk of developing mental health problems. A screening device

for mental health problems should identify at-risk individuals on the basis of the

number, type, and combination of risk factors present. It should identify children

being exposed to multiple risk factors, especially risk factors which have been shown

to have the strongest causal links to the development of mental health problems.

These are the children at highest risk. It should identify children being exposed to

risk factors that are generic or common to a range of adverse mental health outcomes.

These are the children at risk of developing a variety of adjustment problems. It

should identify children being exposed to modifiable risk factors. These are the

children who stand to gain the greatest benefit from participation in an intervention.

Ideally, it should also identify children as early as possible, and if the screen occurs

while the child is of preschool or primary school age, then it should focus on detecting

risk factors present in the family setting, given the importance of family risk factors

during early and middle childhood. Finally, if the aim of the subsequent intervention

is prevention, then screening instruments should also identify the children who are

being exposed to risk factors that are associated with the onset of mental health

problems. The next chapter describes the current screening methods used to identify

at-risk children.


30

CHAPTER 3 - IDENTIFICATION OF AT-RISK CHILDREN

Aims of Screening One of the aims of screening is to assess the risk status of individuals in order

to identify the most appropriate recipients for targeted (selective and indicated)

prevention programs. Screening children to select those at elevated risk may enhance

the cost-effectiveness of interventions (Boyle & Offord, 1990), particularly if children

are selected on the basis of their exposure to modifiable, mediating risk factors

(Pillow, Sandler, Braver, Wolchik, & Gersten, 1991). Screening may also reduce

potential harms by excluding those individuals who are unlikely to benefit from

intervention. This latter issue is not a trivial concern, as demonstrated by recent

prevention research which reported increased rates of delinquent behaviour amongst

non-problem youth involved in peer group programs for delinquent youth (Dishion et

al., 1996).

However, the screening process will only achieve these aims if it is able to

identify the children most likely to go on to develop mental health disorders; that is, if

it has high predictive accuracy. The predictive accuracy of a screening method is

commonly assessed by its sensitivity and specificity. Sensitivity refers to the

proportion of children who end up with mental health problems who were correctly

identified as being at high risk by the screening instrument at an earlier assessment.

Specificity describes the proportion of children who do not develop mental health

problems who were correctly identified as being at low risk at the earlier assessment

(Gordis, 1996). Although sensitivity and specificity are the predictive indices used in

the current thesis, it should be noted that predictive accuracy of a screening

instrument may also be gauged by its positive and negative predictive value, which,

respectively, refer to the proportion of children who are screened positive (high risk)

who go on to develop mental health problems and the proportion of children who are

screened negative (low risk) who do not develop mental health problems.

Unfortunately, the screening process invariably leads to misclassified children,

which reduces the impact of targeted interventions. There is usually a trade-off

between sensitivity and specificity, such that the higher the sensitivity of a measure,

the lower its specificity and vice versa (Bennett, Lipman, Racine, & Offord, 1998;

Costello & Angold, 1988). Low specificity is usually associated with a high


31

percentage of false positive misclassifications (children who are classified as high risk

but who have adequate mental health outcomes), which results in wasted resources

and unnecessary labelling. In comparison, low sensitivity is associated with a high

percentage of false negative misclassifications (children who are classified as low risk

but who develop mental health problems), which denies resources to children who

would potentially benefit from them (Bennett et al., 1999).

Accurate classification of children is even more of a challenge when screening

for selective interventions. This is because selective prevention requires that the risk

factors chosen for screening should be correlated prospectively to negative mental

health problems as opposed to concurrently (Emery, 1991; Pillow et al., 1991).

Ideally, at-risk children should be identified for selective interventions before any

significant behavioural symptomatology has emerged.

Nevertheless, a prevention approach that involves the accurate selection of

individual children for targeted intervention programs of proven efficacy may still

have a disappointing potential impact on the overall burden of disorder in the

community (Rose, 1992). This is because a risk factor that is important at an

individual level may not be important at a community level. A risk factor that has a

large effect on individuals, but is very uncommon, may have little impact on the

overall number of cases in the community. Conversely, a risk factor that has a small

effect on individuals, but is very common, may have a large impact on the overall

rates of disorder in the community (Mason, Scott, Chapman, & Shihfen, 2000). Thus,

the most valuable role for screening tools in prevention science may lie not in

assessing individual risk, but in helping to evaluate the impact of risk factors on the

overall burden of disorder within the community and informing the selection of the

most appropriate interventions for that community.

Screening for Risk Factors at a Community Level Determining the most appropriate interventions for a community involves

establishing a ‘risk factor profile’ for that community. Interventions are unlikely to

impact on the overall community burden of disorder, unless they address risk factors

that are of: (1) high prevalence, (2) high relative risk, and (3) high attributable risk.

The prevalence of a risk factor refers to the proportion of the population

exposed to the risk factor at any one time (Gordis, 1996). The relative risk (RR) of a


32

risk factor is a measure of the excess risk of developing a disorder in exposed

populations compared with nonexposed populations (Gordis, 1996). RR is calculated

by comparing the incidence of disorder in exposed populations to the incidence of

disorder in nonexposed populations (Gordis, 1996). Individuals who are exposed to a

risk factor with a high RR have an increased probability of mental health problem

onset relative to individuals who are not exposed to the risk factor. Thus, targeting

risk factors of high prevalence and high RR has a greater potential to reduce the

burden of disorder on the community than interventions that focus on risk factors of

lower prevalence or RR. However, targeting risk factors that have only a small RR

may also substantially decrease the overall burden of disorder for the community if a

large proportion of the population is exposed to it.

Attributable risk (AR) refers to the extent to which an outcome (e.g.,

psychosocial distress) is due to exposure to a given risk factor. It is possible to

calculate how much of the total risk of developing a disorder in exposed persons is

due to the exposure and how much of the risk in the total population is due to

exposure. In the latter case, population AR refers to the proportion of the disease

incidence in the total population (both exposed and nonexposed) that can be attributed

to exposure to a specific risk factor (Gordis, 1996). Knowledge of a risk factor’s AR

is especially useful for planning interventions, as it gives an indication of the potential

reduction in the incidence of an outcome that may occur by removing exposure to the

risk factor. Despite its potential usefulness, relatively little is known about the AR of

risk factors for children’s mental disorders (Offord, 1996).

RR and AR can only be determined by longitudinal examination of the

relationship between risk factors and subsequent mental health outcomes. Together,

these data can be used to build up a risk factor profile of the community to help make

informed choices about the most appropriate interventions for that community.

Screening Methods

Screening for behavioural or emotional symptoms. A variety of measurement

and screening approaches have been employed for identifying mental health risk

factors and at-risk children. The most common approach is to screen children for

current behavioural or emotional problems (e.g., Bird, Gould, Rubio-Stipec,

Staghezza, & Canino, 1991; Braswell et al., 1997; Marcus, Fox, & Brown, 1982;

Mattison, Lynch, Kales, & Gamble, 1993; Verhulst, Koot, & Van der Ende, 1994). If


33

a child scores above a prerequisite cut-off for the range or frequency of problem

behaviours displayed, he or she is considered to be at risk (Achenbach, 1991a;

Achenbach, 1991b). The popularity of behavioural screening is partly due to

evidence that early problem behaviour is one of the best predictors of future mental

health problems, particularly for externalizing disorders (Loeber, 1991; Lynam, 1996;

Patterson, 1993). However, despite consistent evidence for the continuity of early

problem behaviours (Fergusson et al., 1995; Keenan et al., 1998), the predictive

accuracy of externalizing behaviour symptoms is modest at best (Bennett et al., 1998),

and even lower for early internalising symptoms (Verhulst et al., 1994). In a review

of 13 studies, Bennett et al. (1998) reported sensitivity ranging from 28% to 77% and

specificity ranging from 65% to 97% for the prediction of parent-, teacher-, or youth-

rated conduct problems from earlier externalising symptoms. Although less data is

available on the sensitivity or specificity of internalising symptoms, several studies

have shown that internalising problems are less stable over time than externalising

problems (Fischer, Rolf, Hasazi, & Cummings, 1984; Koot & Verhulst, 1992;

Mattison & Spitznagel, 1999). Perhaps though, the major disadvantage of screening

for behavioural or emotional symptoms is that this method fails to detect all those

children not currently displaying behavioural symptoms but who are at risk of

developing mental health problems due to exposure to risk factors in their family

environment, thus excluding from the intervention a group who may have gained

significant benefit. This method of screening can therefore, by definition, only be

used to select children for indicated interventions.

Multiple gate screening. A second method, called multiple gate screening,

involves sequential assessments in which individuals who are identified on one risk

factor are subsequently screened for the presence of other risk factors (Loeber,

Dishion, & Patterson, 1984). The first screen is used to narrow the potential pool of

at-risk individuals prior to administering more expensive methods of screening. This

approach has proved relatively economical and has been employed in a number of

recent prevention trials (August, Realmuto, Crosby, & MacDonald, 1995; Lochman &

Conduct Problems Prevention Research Group, 1995; Loeber & Dishion, 1987;

Loeber et al., 1984). Multiple gate screening potentially has higher predictive

accuracy than behavioural screening alone, due to its identification of an end pool of

children who are at risk on multiple indicators of adjustment. For example, based on

a three-step multiple gate procedure, Loeber and Dishion (1987) obtained a sensitivity


34

of 56.5% and a specificity of 87.5% for the three year prediction of delinquency (as

defined by at least one police contact) in a sample of 102 seventh and tenth grade

boys. In this study, the risk factor screened at the first gate was based on teacher-

report of the child’s current behaviour, followed by parent report of the child’s

behaviour at the second gate, and finally mothers’ report of child-rearing methods at

the third gate. Although the screening methods used for the second and subsequent

screening gates have varied across studies, to date, the risk factor screened at the first

gate has typically been the child’s current behaviour. Thus, multiple gate screening

has so far only been used to select children for indicated interventions.

Simple nomination of at-risk children. A third screening method involves the

simple nomination of children considered to be at risk. Teacher nominated aggressive

or withdrawn children have been found to perform more poorly academically and

have more behaviour problems than well-adjusted children at five year follow-up

(Ollendick, Greene, Weist, & Oswald, 1990). In addition, Ollendick et al. (1990)

showed that teacher nomination has relatively high sensitivity (84%) but low

specificity (39%) for identifying children who later commit delinquent offences.

However, the predictive accuracy of parent or teacher nomination for detecting

individual children at risk of more general internalising or externalising mental health

problems is unknown. Although this screening method could potentially be used to

identify children for selective interventions if parents or teachers based their

nominations on knowledge of the children’s exposure to family risk factors, there is

evidence that teachers using this method often identify children who already have

clinically significant adjustment problems (Mertin & Wasyluk, 1994). It therefore

seems likely that the use of the simple nomination method would result in the

identification of a pool of children best suited for indicated interventions.

The three screening methods discussed above have therefore all been used to

identify children with current behavioural or emotional problems. If children are to

be identified earlier for selective interventions, alternative methods of screening are

required.

Screening for exposure to a single risk factor. A fourth approach concerns the

identification of at-risk individuals on the basis of their exposure to a single, easily

observable risk factor, such as the recent experience of parental divorce (Pedro-

Carroll & Cowen, 1985), the death of a parent (Sandler et al., 1992), or the transition

from primary to high school (Felner et al., 1981). Although this method of screening


35

is relatively easy to use, it is now well established that single risk factors alone are

relatively poor predictors of mental health outcomes (Coie et al., 1993; Rutter, 1978;

Rutter, 1979). So, despite the fact that this screening method can be used to identify

children for selective interventions, it would be expected to result in high rates of

false positive misclassification errors.

Screening for exposure to multiple (family) risk factors. A final approach

which overcomes the limitations of each screening method described above, concerns

the assessment of multiple, concurrent risk factors. In particular, this approach

enables the identification of children for selective interventions and potentially has

higher predictive accuracy than screening based on single risk factors. In addition,

this method may also enhance the prediction of negative mental health outcomes over

behavioural methods alone. This latter issue has been addressed by studies employing

a multiple gate screening process. For example, Lochman (1995) used a two-step

screening procedure based on teacher-report of children’s externalising behaviours,

followed by parent-report of children’s behaviour plus parenting practices. In this

study, the parenting practices screen did not substantially add to the prediction of

mental health problems after variance due to both the teacher and parent behavioural

screens had been taken into account. As Lochman (1995) pointed out, this was partly

due to the high correlation between parents’ reports of parenting practices and

children’s externalising behaviour, and it is consistent with the status of current

behaviour problems as a robust predictor of future disorders (Fergusson et al., 1995;

Keenan et al., 1998; Loeber, 1991). However, consistent with the notion of

cumulative risk, it seems likely that if, instead of one family risk factor, multiple risk

factors had been considered, prediction may have been improved beyond that afforded

by the child’s behaviour alone. Bennett et al. (1999) provided some evidence to

support this supposition. These researchers screened children for current behaviour

problems and their exposure to six family risk factors and achieved some gain in

predictive accuracy over externalising symptoms alone, by including different

combinations of family factors in the prediction models (although it should be noted

that several of the models that included only one extra family factor in addition to

children’s externalising symptoms also showed improved prediction relative to

models that included only the children’s behaviour problems). The inclusion of

multiple family risk factors in the screening process may be especially important for

the prediction of internalising disorders for which behavioural indicators are more


36

difficult for parents and teachers to identify (see next section on the evidence

regarding parents’ and teachers’ ability to identify at-risk children). Furthermore,

screening for exposure to multiple family risk factors is the only method with the

potential to provide a community risk factor profile, thereby facilitating appropriate

matching of interventions to the needs of the community.

While screening for multiple family risk factors has considerable potential for

enabling the identification of children for selective interventions, improving the

prediction of negative mental health outcomes over and above children’s behaviour,

and informing intervention planning, only a handful of prevention studies have

attempted to do so. The reason for the near absence of such a promising approach is

likely due to the effort and costs associated with collecting information on multiple

family risk factors using existing methods. Past prevention and risk factor studies

have employed telephone interviews (e.g., Dishion & Andrews, 1995), face-to-face

interviews (e.g., Blanz, Schmidt, & Esser, 1991), home observations (e.g., Bradley &

Caldwell, 1977; Bradley et al., 1988), multiple paper-and-pencil questionnaires

(Bennett et al., 1999; Pillow et al., 1991), or some combination of the above (e.g.,

Seifer et al., 1996; Shaw, Vondra, Hommerding, Keenan, & Dunn, 1994). While

these methods may be appropriate for use in clinical settings (Bray, 1995), there are

several reasons for why they are not suitable for population-level screening.

First, of the hundreds of paper and pencil self-report instruments that assess

family functioning (for reviews see: Grotevant & Carlson, 1989; Touliatos,

Perlmutter, & Straus, 1990), many assess only a single construct or risk factor.

Examples include the Social Support Inventory (SSI; Procidano & Heller, 1983)

which assesses perceived social support, and the Abbreviated Dyadic Adjustment

Scale (ADAS; Sharpley & Rogers, 1984) which assesses global marital satisfaction.

As already discussed, screening based on a single risk factor, albeit a family risk

factor, is a poor option for accurate identification of individual at-risk children. In

order to screen for multiple risk factors, several of such instruments, typically one per

risk factor, need to be administered. Clearly, the time and expense involved in

administering so many instruments create an undue burden on participants and

preclude the use of such an approach for community-level risk profiling.

Second, several instruments that do assess for more than one family risk factor

nevertheless fail to provide adequate coverage of the range of family factors

potentially influencing children’s mental health. The well-known, 60-item Family


37

Assessment Device (Epstein, Baldwin, & Bishop, 1983) has good psychometric

properties (Miller, Epstein, Bishop, & Keitner, 1985) and has even been used for

screening purposes to identify at-risk families who might benefit from receiving

preventive interventions (Akister & Stevenson-Hinde, 1992). However, it assesses

only seven areas of risk, all concerned with internal family processes, and therefore is

not suitable for informing community intervention planning. Similarly, although the

Family Environment Scale (Loveland-Cherry, Youngblut, & Leidy, 1989; Moos &

Moos, 1974; Moos, Insel, & Humphrey, 1974) is comprised of 90 items that assess 10

dimensions of family functioning, the instrument was designed to assess only a small

subset of the family risk factors that are considered important in determining

children's mental health (see Table 2.1). Neither the Family Assessment Device or

the Family Environment Scale assess children’s exposure to important family risk

factors such as parental psychopathology or antisocial behaviours such as criminal

activity or alcohol or illicit drug abuse. Nor do they assess children’s exposure to

chronic adversity such as multiple changes of parent figures or high family mobility.

Third, those methods that do assess a wider range of family risk factors are

usually too long and provide more information than is required for a screening

instrument. For example, Zubrick, Williams, Silburn and Vimpani (2000b)

constructed an instrument to assess indicators of social and family functioning, based

on an amalgamation of several paper-and-pencil measures. Named the ‘Indicators of

Social and Family Functioning Reference Instrument’ (ISAFF-RI), it consists of 69

items1 that assess different dimensions of family and community functioning. The

authors recommend that their indicators should regularly be included in social and

health surveys conducted by key government agencies on children. Although the

indicators chosen are known to be on the causal pathways of poor mental health

outcomes, and the instrument undeniably has the potential to provide a useful

snapshot of the health of a community, it is nevertheless doubtful that such a long

instrument could feasibly be used for ongoing, population-level screening. In any

case, this instrument was not available when the present research was conducted.

Another approach taken by several researchers has been to combine

information from several different sources to create an index of the number of risk

factors present in the family. This risk score is derived from questionnaire, interview,

1 This is taking into account that two ‘items’ on the instrument actually consist of 12 questions each.


38

and sometimes observational data. Risk factors from the different sources are

dichotomised and scored ‘1’ if present and ‘0’ if absent. Examples of such risk

indices include Sameroff’s and colleagues’ Multiple Risk Index (MRI) that identifies

children’s exposure to 10 different family risk factors (Sameroff, Seifer, Baldwin, &

Baldwin, 1993; Seifer et al., 1996) and Rutter and Quinton’s Family Adversity Index

(FAI) that identifies children’s exposure to 6 family risk factors (Rutter, 1978; Rutter

& Quinton, 1977). Studies using this approach (e.g., Blanz et al., 1991; Seifer et al.,

1996; Shaw et al., 1994) typically have to undergo a lengthy data collection process

before the final risk index can be calculated. In addition, such assessment methods

often require expert scoring and interpretation. As a result, these methods are all too

costly and cumbersome for widespread use in school or community settings.

Finally, many family assessment instruments do not assess current family

functioning. For example, the Family Background Questionnaire (Melchert &

Sayger, 1998) assesses adults’ retrospective accounts of their own family of origin

experiences (as opposed to parent-report of their child’s family of origin). Melchert

(1998) reviews a host of other instruments that assess family history in a similar

manner. Such instruments do not provide an indication of the risk factors currently

present within the community.

In sum, screening approaches based on assessing children’s exposure to

multiple family risk factors have great potential. However, to date, existing

measurement methods intended for this purpose are impractical and do not achieve

the aims of population-level screening. The development of practical risk assessment

methods remains a neglected area (Bickman, 1996). Clearly, what is required is a

relatively brief screening instrument that nonetheless assesses children’s exposure to a

broad range of family risk factors. The Family Risk Factor Checklist (FRFC) is a new

paper-and-pencil instrument that was developed to address this need. Before

describing this new screening instrument, the evidence on parents’ and teachers’

ability to identify at-risk children and the psychometric properties relevant to its

development are reviewed.

For young children, each of the above screening methods relies upon parent-

or teacher-report of the relevant information. So regardless of whether the instrument

is suitable for identifying children for indicated or selective interventions and

regardless of its cost-efficiency, its ultimate usefulness is dependent upon parents’ or


39

teachers’ accuracy in detecting the risk factors that place children at risk of

developing mental health problems.

Evidence Regarding Parents’ and Teachers’ Ability to Identify At-risk

Children

Based on observation of behavioural indicators. Despite their important role

in facilitating access to preventive mental health interventions for children, parents or

teachers may lack confidence and/or accuracy in identifying those children at risk of

developing mental health problems. Several studies have reported that teachers can

accurately identify children at risk of developing externalising disorders by observing

the child’s behaviour (Dishion et al., 1996; Loeber & Dishion, 1983; Nelson, 1971;

Stouthamer-Loeber & Loeber, 1989; Walker & Severson, 1991). However, the

evidence regarding teachers’ ability to identify children at risk of developing

internalising disorders is mixed, with some authors (Dadds, Spence, Holland, Barrett,

& Laurens, 1997; Strauss, Frame, & Forehand, 1987) reporting that teachers can

accurately detect these children and others (Nicholson et al., 1999a; Reynolds, 1990;

Ritter, 1989; Weist, 1997) reporting that teachers may be less likely to detect

behavioural predictors for internalising disorders, especially when they are manifested

by girls (Dadds et al., 1997). For example, Dadds et al. (1997) found that when

teachers were asked to nominate the children in their class who were showing signs of

anxiety, the resultant list contained a significantly larger percentage of boys, despite

the fact that more girls end up developing anxiety disorders. In Nicholson et al.’s

(1999b) study, primary school teachers reported concerns that they may ‘overlook’

potentially damaging psychosocial problems in socially withdrawn or depressed

children because these children did not disrupt the activities of the class. Children

with, or at risk of developing, internalising disorders may therefore be under-

identified in schools. This may be due to either teachers’ failure to recognise the

developmental precursors for anxiety or depression or the disproportionate amount of

time that teachers spend in dealing with the more disruptive children who are at risk

for externalising disorders.

There is evidence that parents, too, may be poorer at identifying internalisers

than externalisers. While parents appear to be good at reporting behaviour and

conduct problems (Edelbrock, Costello, Dulcan, Conover, & Kala, 1986; Herjanic &


40

Reich, 1982; Verhulst & Van der Ende, 1991a), there are frequently large

discrepancies seen between parent and child report of children’s anxious or depressed

symptoms (Angold et al., 1987; Fergusson et al., 1993; Kashani, Orvaschel, Burk, &

Reid, 1985; Mesman & Koot, 2000; Weissman et al., 1987). The tendency is for

parents to under-report internalising symptoms (Wrobel & Lachar, 1998). Moreover,

when such symptoms are reported, they are usually the more overt behavioural

indicators of depression or anxiety (Kazdin, Esveldt-Dawson, Sherick, & Colbus,

1985; Wrobel & Lachar, 1998).

Thus, given the evidence that parents and teachers are able to identify children

whose behaviour stands out as being difficult, it is likely that their accuracy in

selecting children for indicated interventions that aim to prevent externalising

disorders is relatively high. On the other hand, parents’ and teachers’ ability to

identify children for indicated programs that aim to prevent internalising disorders is

less certain. Despite suggestions that parents and teachers are less accurate at

identifying children at risk of developing internalising disorders than externalising

disorders, this has not been directly tested. Even less certain is teachers’ or parents’

ability to identify children for selective interventions.

Based on knowledge of family background characteristics. For teachers to be

able to identify children for selective interventions, they must have adequate

knowledge of children’s family backgrounds. While earlier qualitative research

suggests that teachers use their knowledge of family risk factors to respond to the

problems of individual children (Nicholson et al., 1999a), no quantitative research has

systematically examined the extent to which teachers are aware of the family

backgrounds of children in their class or whether they are able to reliably draw on this

knowledge to identify children at risk of developing subsequent internalising or

externalising problems.

It is likely, however, that the extent and accuracy of teachers’ family

background knowledge varies systematically in relation to several variables. An

obvious candidate is the amount of contact between teachers and parents. Teachers of

lower year levels have greater direct contact with parents than teachers of higher year

levels (e.g., Izzo, Weissberg, Kasprow, & Fendrich, 1999), and may therefore be

expected to know more about children’s family backgrounds. Given the evidence that

parents of lower SES typically show less involvement with their children’s schooling

than parents of higher SES (Eccles & Harold, 1996; Kohl, Lengua, & McMahon,


41

2000), it may also be hypothesised that teachers will know more about the family

backgrounds of children from higher SES families. It is unclear whether variables

such as the child’s behaviour or gender will be related to teachers’ family background

knowledge. On the one hand, children with behaviour problems are more likely, than

their non-problem peers, to come from disadvantaged families (Fergusson et al., 1994;

Shaw et al., 1994) who, in turn, have less contact with the school (Eccles & Harold,

1996; Kohl et al., 2000). On the other hand, it seems reasonable to assume that

teachers initiate communication with the parents of behaviour problem children more

frequently than non-problem children. Following this latter reasoning, teachers may

know more about the family backgrounds of males than females, given that a higher

proportion of boys come to the teacher’s attention for misbehaviour in class (Childs &

McKay, 1997; Weissberg et al., 1987). Finally, the accuracy of teachers’ perceptions

of family risk factors might also be affected, in part, by teachers’ ‘natural’ or ‘niaive’

theories of the causes of children’s behaviour problems. If this were true, a teacher

who believed that behaviour problems occurred more often in children from low SES

families, might assume that low SES was present in the family of a child who had

obvious behaviour problems.

For parents, the issue is obviously not how much they know about their own

child’s family background, but rather, how much they are willing to report about their

child’s family environment and the extent to which these reports are influenced by

social desirability factors. The content of instruments that assess family functioning

is is often sensitive and personal, so it would be expected that responses may be

biased in a socially desirable direction (Melchert, 1998). However, there are

promising indications that these influences may not be as strong as expected.

Correlations between various family history instruments and social desirability scales

have mostly been nonsignificant (Melchert, 1998).

Part of the challenge, then, in developing an instrument that screens young

children for their exposure to multiple family risk factors lies in making the wording

of items as acceptable (and as non-threatening) as possible to both parents and

teachers. Adequate consideration of item wording and layout is subsequently

reflected in the psychometric properties of the instrument. It should by now be

evident that the usefulness of a risk factor screening instrument is dependent upon

parents’ or teachers’ ability to identify and report the risk factors that predispose

children to developing mental health disorders. However, it is also important to


42

realise that parents’ and teachers’ apparent accuracy in identifying at-risk children

will depend, to a large extent, upon the psychometric properties of the screening

instrument used.

Psychometric Properties Several psychometric properties may have a large influence on the overall

performance of a screening measure. In particular, a good screening instrument

should be both reliable and valid. A brief summary of the different reliability and

validity tests is provided below.

1. Test-retest reliability refers to the extent to which an instrument yields the

same results on repeated trials (Carmines & Zeller, 1979). It is an estimate of

the extent to which measurement is free from random errors (Ottenbacher &

Tomchek, 1993).

2. Internal consistency is a form of reliability that refers to the inter-relatedness

of items on a test. Cronbach’s alpha (Cronbach, 1951) is still one of the most

popular estimates of internal consistency and is based on the mean inter-item

correlation of an instrument.

3. Content validity is the extent to which items within an instrument are relevant

to, and representative of, the targeted construct (Walsh, 1995). An instrument

with good content validity accurately reflects the domain it was designed to

measure.

4. Construct validity is the extent to which the variables measured by an

instrument correlate with other variables in a manner consistent with theory

(Carmines & Zeller, 1979; Stewart & Ware, 1992). There are two forms of

construct validity: convergent and discriminant. Convergent validity is

demonstrated when two instruments, purported to measure the same

constructs, provide similar results. Discriminant validity is demonstrated

when two instruments, measuring unrelated constructs, do not correlate highly

(Campbell & Fiske, 1959).

5. Criterion-related validity refers to the relationship between scores on the

instrument and an external criterion (Silva, 1993). Good concurrent (criterion-

related) validity is indicated by convergence between scores on the instrument

and an external criterion that are collected at the same point in time. On the


43

other hand, good predictive (criterion-related) validity is supported by

convergence between scores on the instrument collected at one time point and

scores on the external criterion collected at a later time point. In practice,

when the only available criterion is that afforded by another questionnaire,

then construct and criterion-related validity are essentially equivalent

concepts.

Determining the reliability or validity test/s to apply should be based upon the

aims of the instrument. Costello and Angold (1988) describe two different uses for

paper-and-pencil instruments that correspond to the individual- and population-level

aims of screening described earlier. First, they may be used to identify individual at-

risk children. These types of instruments may be regarded as ‘screening measures’,

for which successful differentiation of ‘at-risk’ individuals from the rest of the

population, or criterion-related validity is required. Second, they may be used to

quantify the range of symptoms or risk factors present at the community (or

individual) level. For this function, such instruments are best conceptualised as

‘checklists’ for which adequate coverage of the relevant material, or content validity

is of primary importance (Costello & Angold, 1988). The current thesis examines the

performance of the FRFC in both roles.

The Family Risk Factor Checklist (FRFC) The Family Risk Factor Checklist-Parent (FRFC-P) and the Family Risk

Factor Checklist-Teacher (FRFC-T), were developed to overcome some of the

limitations associated with current screening methods. Specifically, on the basis of

parent-report and teacher-report respectively, these new instruments were designed to

screen children for their exposure to a broad range of family risk factors in a relatively

brief format (48 & 44 items, respectively). Each item assesses a different family risk

factor. The current research sets out to determine if it is possible for this type of

screening instrument to provide a sufficiently accurate picture of a child’s exposure to

risk to be a useful screening tool at both an individual and community level.

Both the FRFC-P and FRFC-T were developed and tested as part of the

Promoting Adjustment in Schools (PROMAS) Project. The next four chapters present

the methods and findings from the PROMAS Project, relevant to the development,

testing, and use of these instruments. Specifically, study methods are presented in


44

Chapter 4 and results in Chapters 5-7. Each of these latter chapters represents a paper

that has been accepted or submitted for publication. The first paper (Chapter 5)

examines the performance of the FRFC-P in its role as a ‘checklist measure’ for

identifying risk factors of high prevalence, relative risk, and/or attributable risk in the

school community. After determining the relative importance of different risk factors,

the FRFC-T is then used in Paper 2 (Chapter 6) to investigate the types of risk factors

that teachers are able to reliably identify in children’s family backgrounds. Finally,

the third paper (Chapter 7) examines the performance of both the FRFC-P and FRFC-

T in their role as ‘screening measures’ by comparing their predictive accuracy in

identifying individual at-risk children against the performance of other screening

methods. Of particular interest is whether the FRFC-P or FRFC-T are able to identify

at-risk children before the development of significant behavioural or emotional

symptoms.

Combined, these three papers contribute to the ‘science of prevention’ by

providing insights into: (1) the relative importance of different risk factors for

predicting the onset or persistence of children’s mental health problems, (2) the

validity of a new screening instrument for establishing community risk factor profiles

and identifying at-risk individuals, (3) the selection of optimal screening instruments

for targeted interventions, and (4) the feasibility of offering selective preventive

interventions within the school setting. Finally, this body of research lays the

foundations for the development and selection of interventions that address the risk

factors that are prevalent and influential in the community.

24/12/04 Methods

45

Chapter 4 - Methods

Design

The design and procedure of the Promoting Adjustment in Schools

(PROMAS) Project have been described elsewhere (Nicholson, Oldenburg, Dwyer, &

Battistutta, 2002; see Appendix C). Below is a more detailed account of the methods

relevant to the current thesis. Figure 4.1 provides an overview of the PROMAS

research design and timeline. Phase One of the PROMAS Project employed a

longitudinal cohort design with three periods of data collection and one year between

each assessment. Schools were recruited in two waves from 11 education districts in

South East Queensland. As determined by the availability of funding, 10 schools

were recruited in 1998 (Wave A), and 18 schools in 1999 (Wave B). Data were

collected from parents and teachers in both waves, Wave A in Semester 2 of 1998,

1999, and 2000, and Wave B in Semester 2 of 1999, 2000, and 2001.

As shown in Figure 4.1, data to test the validity of the Family Risk Factor

Checklist - Parent (FRFC-P), were collected from both Wave A and Wave B parents

during their Time 1 data collection periods (1998 and 1999, respectively). Data to test

the reliability of the FRFC-P were collected only from Wave A parents during their

one year follow-up in 1999.

Phase Two of the PROMAS project is currently being planned. It will utilise

information gathered in Phase One to implement environmental and parent

engagement interventions, individually tailored to meet the needs of different schools.

Half the schools will be randomly assigned to a standard intervention group

(environmental interventions only), while the other half will be assigned to an

enhanced intervention group (environmental plus parent engagement interventions).

Subject to funding, interventions are scheduled to begin in all schools for children

entering preschool in 2003. The effectiveness of interventions will be assessed

longitudinally, with data collection continuing until 2006, when the cohort is in Year

3.

The current thesis is concerned with Phase One only, and uses data collected at

Time 1 and Time 2 for both waves of schools (see shaded section on Figure 4.1). The

24/12/04 Methods

46

Figure 4.1. PROMAS project research design and timeline. Note. FRFC-P = Family Risk Factor Checklist - Parent; FRFC-T = Family Risk Factor Checklist - Teacher; CBCL = Child Behavior Checklist; TRF = Teacher Report Form; IOS-P = Intervention Options Survey - Parent; IOS-T = Intervention Options Survey - Teacher; IVN = intervention; Sem = semester.

YEAR

1998

1999

2000

2001

2002

2003

2004

2005

WAVE A WAVE B

Recruitment & Time 1 data collection • FRFC-P & CBCL to parents • Validation questionnaires to

parents

Time 2 data collection • FRFC-P, CBCL, & IOS-P to

parents • Reliability FRFC-P to parents

FRFC T TRF & IOS T t

Recruitment & Time 1 data collection • FRFC-P, CBCL, & IOS-P to

parents • Validation questionnaires to

Time 3 data collection • FRFC-P & CBCL to parents • FRFC-T & TRF to teachers



• Planning interventions for both Wave A & B schools

Random allocation of Wave A & B schools to standard or enhanced interventions

STANDARD INTERVENTION SCHOOLS Enhanced Intervention Sem 1: Pre-assessment for IVN Sem 2: Environmental IVN

Sem 1: Pre-assessment for IVN Sem 2: Environmental & Parent Engagement IVN

Sem 1 & 2: Continue IVN Sem 2: Annual data collection

Sem 1 & 2: Continue IVN Sem 2: Annual data collection

Sem 2: Annual data collection Sem 2: Annual data collection

Sem 2: Final data collection Sem 2: Final data collection 2006

24/12/04 Methods

47

Intervention Options Survey (IOS; collected in 1999 for both Waves), and the Time 2

Family Risk Factor Checklist-Teacher (FRFC-T) data were not used in the current

thesis.

Sample Selection

Schools were selected from the 11 Education Queensland districts that were

closest (within one hour’s drive) to Brisbane, a major metropolitan centre in South

East Queensland, Australia. Wave A schools were selected from the Stafford,

Corinda, Mt Gravatt, Coopers Plains, and Logan Beaudesert districts, and Wave B

schools were selected from the Ipswich, West Moreton, Bayside, Geebung, Murrumba

Downs, and Gold Coast North districts. On average, Wave B districts were further

from Brisbane than Wave A districts, and were therefore more likely to come from

country areas. All schools within these districts that had an enrolment size between

200-7002 children, an onsite preschool (i.e., a preschool situated on the school

grounds), and an Index of Relative Socioeconomic Disadvantage (IRSED)3 of 213 or

lower were sent a recruitment package. These criteria respectively ensured that: (1)

the ratio of the number of children selected per school (60), to the school enrolment

size, was large enough to provide a meaningful profile of the most common mental

health problems and risk factors present in each school community, (2) preschool

children were included in the sample, and (3) PROMAS resources were not

concentrated on the quartile of schools with the highest SES (IRSEDs ≥ 214), because

these schools were expected to have the fewest at-risk children and the best access to

services. In total, 40 schools were posted recruitment packages in 1998, and 79

schools were posted recruitment packages in 1999.

From this potential pool of 119 schools, 28 agreed to participate (10 Wave A;

18 Wave B). One Wave A school withdrew from the study after the parent data

collection at Time 1. This left 27 schools from which both Time 1 and Time 2 parent

and teacher data were collected. Following school recruitment, 60 children from

2 Enrolment size information was obtained from the Education Queensland Schools Directory on the Internet. Schools Directory information was based on July 1998 census data. 3 IRSEDS were based on 1991 Australian Census data and are an indicator of SES. They are derived from community attributes such as average income, educational attainment, unemployment rate, and occupational prestige. IRSEDs range from 40-240, and roughly 80% of schools have IRSEDs between 190-210. Schools with IRSEDs of 190 or lower are generally considered to be of low SES, while schools with IRSEDs of 210 or higher are considered to be of high SES (personal communication, Performance Measurement Office, Education Queensland, 1998).

24/12/04 Methods

48

preschool to Year 3 were randomly selected from each school. These children were

stratified by grade and gender, and to avoid overburdening any one teacher, also by

class. Participants were the parents and teachers of these children. Children who had

moved school prior to the Time 1 data collection or whose parents refused

participation were replaced with additional randomly selected participants.

Measures Key Instruments

Parents and teachers each completed two instruments on each child. Parents

completed the Family Risk Factor Checklist - Parent (FRFC-P) and the Child

Behaviour Checklist (CBCL; Achenbach, 1991a). These are described in Chapter 5.

The teacher versions of these instruments were the Family Risk Factor Checklist -

Teacher (FRFC-T) and the Teacher Report Form (TRF; Achenbach, 1991b),

respectively. Because the TRF was designed to obtain teachers’ reports of 5-18 year

old children, a different instrument was required for the teachers of children who were

younger than 5 years of age. Consequently, preschool teachers completed the

Caregiver-Teacher Report Form (C-TRF; Achenbach, 1997), which is suitable for 2-5

year old children. The TRF and C-TRF have many items in common. Both

instruments, as well as the FRFC-T, are described in Chapter 6. It should be noted

that the version of the FRFC that was sent to parents was not labelled the ‘Family

Risk Factor Checklist - Parent’. To reduce the impact of social desirability on

parents’ responses, the instrument was given the less threatening title of ‘Family

Background Checklist’. However, throughout this thesis, it will be referred to as the

FRFC-P, for consistency with the teacher version.

Both the FRFC-P and FRFC-T were piloted with the parents and teachers of

50 children attending one state primary school, and subsequently revised prior to the

main study4. A brief description of the pilot study and the resulting list of changes

made to the risk factor instruments can be found in Appendix E. The final versions of

the instruments measured children’s exposure to five broad domains of risk,

including: (1) adverse life events and instability (ALI), (2) family structure and SES

(SES), (3) parenting practices (PAR), (4) parental verbal conflict and mood problems

(VCM), and (5) parental antisocial and psychotic behaviour (APB). Within each risk

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domain, children were classified as being at low, medium, or high risk of developing

mental health problems in the future, on the basis of the number of risk factors they

were exposed to within the domain (see Chapter 5 for further details). Copies of the

FRFC-P and FRFC-T that were used in the main study can be found in Appendix F.

The analyses described in Chapter 6 indicated that teacher reports of some family

background domains did not meet acceptable measurement standards. Therefore, the

teacher version was further revised to include only the ALI and SES risk domains.

Full scoring instructions for the FRFC-P are provided in the form of an SPSS syntax

file in Appendix G.

Validation Instruments Several other parent-report instruments were used to test the convergent

construct validity of the FRFC-P. The instruments were selected to provide validation

data for each risk factor measured by the FRFC-P. Table 4.1 provides a summary of

the FRFC-P items for which validation data were collected, the risk factor assessed,

and its corresponding validation instrument. The instruments are described briefly in

Chapter 5, however, more detail on their reliability and validity is given below. The

validation instruments were divided into two booklets, which were administered to

different parent subsamples in order to minimise participant burden.

Validation Booklet 1 consisted of the following questionnaires:

Short Form - 36 (SF-36). The SF-36 (Ware & Gandek, 1994; Ware &

Sherbourne, 1992; Ware, Snow, Kosinski, & Gandek, 1993) measures physical and

mental health. The 10-item physical functioning subscale measures limitations in

behavioural performance of everyday physical activities and the 4-item role-physical

subscale measures the extent of disability in everyday activities due to physical

problems (Ware et al., 1993). These subscales have good validity as measures of

physical health status and their alpha reliability coefficients are reported to be .93 and

.89, respectively (Ware et al., 1993). The physical functioning and role-physical

subscales were used to validate the FRFC-P item on physical condition (item 30).

Alabama Parenting Questionnaire (APQ). The 42-item APQ (Shelton, Frick,

& Wootton, 1996) assesses five dimensions of parenting style: involvement, positive

parenting, poor monitoring and/or supervision, inconsistent discipline, and corporal

4 Data from the pilot school were not included in analyses for the main study.

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punishment. It also contains an additional seven items measuring specific discipline

practices, other than corporal punishment. The APQ has good convergent validity

with measures of parenting efficacy and satisfaction (McMahon, Munson, & Speiker,

1997) and differentiates families with children with disruptive behaviour disorders

from normal control families (Shelton et al., 1996). For a sample containing both

clinic and normal volunteer children, the internal consistency estimates for the

involvement, positive parenting, poor monitoring and/or supervision, inconsistent

discipline, and corporal punishment subscales are .80, .80, .67, .67, and .46,

respectively (Shelton et al., 1996). Each subscale (except monitoring and

supervision) and two items from the ‘other discipline practices’ subscale, concerned

with whether parents yell when their child misbehaves, were used to validate the

FRFC-P parenting practices items with which they corresponded (items 31-33, 36-

40).

Abbreviated Dyadic Adjustment Scale (ADAS). This 7-item, abbreviated form

of the Dyadic Adjustment Scale (Sharpley & Rogers, 1984) assesses global marital

satisfaction. It has been found to discriminate reliably between maritally distressed

and non-distressed couples and the alpha reliability coefficient of the instrument is .76

(Sharpley & Rogers, 1984). The ADAS was used to validate the relationship

happiness item of the FRFC-P (item 22).

Offending History (OH). This 45-item questionnaire assesses three types of

criminal offending in the previous year: property crimes (e.g., damaging property,

breaking into a house, stealing a car, shoplifting or other theft); violence (e.g., assault,

fighting, cruelty to animals, using physical coercion) and; ‘other’ crimes (e.g.,

possession of drugs, failing to obey court orders). The OH was used as part of the

interview given annually to teenagers in the Christchurch Health and Development

Study (Fergusson, Lynskey, & Horwood, 1997b). Many of the questions contained in

this interview were originally derived from the Self-Report Early Delinquency

Instrument used in the Dunedin Multidisciplinary Health and Development Study

(Moffitt & Silva, 1988), which, in turn contained items from the US National Youth

Survey (Elliott, Ageton, Huizinga, Knowles, & Canter, 1983). Both latter instruments

are of high quality. For example, the Self-Report Early Delinquency Instrument has

test-retest reliability of .85, internal consistency of .90, and has respectable

convergent validity (Pearson’s correlations) of .45 with parent- and .28 with teacher

reports of adolescent antisocial behaviour (Moffitt & Silva, 1988). One item on

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running away from home was removed for the current study, to make the OH more

relevant to adults. The remaining 44 items were used to validate the FRFC-P item on

criminal activities (item 25).

Validation Booklet 2 consisted of the following questionnaires:

Depression, Anxiety, Stress Scale21 (DASS21). The 21-item DASS21 was

developed from the standard 42-item DASS (Lovibond & Lovibond, 1995b) and

assesses adult depression, anxiety, and stress on three subscales. The full DASS has

good convergent validity with Beck’s (1987; 1990) Depression and Anxiety

Inventories (Lovibond & Lovibond, 1995a). The alpha reliability values for the three,

7-item subscales are .81, .73, and .81, respectively (Lovibond & Lovibond, 1995b).

These subscales were used to validate the FRFC-P items on depression, anxiety, and

stress, respectively (items 23-24, 35).

Psychoticism Subscale from the Symptom Check List - 90 (SCL-90). The 10-

item psychoticism subscale of the SCL-90 attempts to measure ‘first rank’ symptoms

of schizophrenia as well as symptoms of interpersonal distance and alienation

(Derogatis & Cleary, 1977). Although the items on the psychoticism subscale do not

‘hang together’ as well as those of other SCL-90 subscales, it has reasonable construct

validity (Derogatis & Cleary, 1977), and good reliability coefficients. Test-retest

reliability was .84, and internal consistency was .77 (Derogatis, 1983). It was used to

validate the FRFC-P item on serious parental mental health problems (item 29).

Social Support Inventory (SSI). This 40-item questionnaire assesses perceived

social support from friends and family (Procidano & Heller, 1983). Procidano and

Heller (1983) showed that perceived social support from friends and perceived social

support from family were separate, but valid constructs, that were both related to a

range of psychopathological symptoms in a sample of university students. For this

same population, the friends and family subscales have Cronbach’s alphas of .88 and

.90,

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Table 4.1

Family Risk Factor Checklist - Parent (FRFC-P) Items and their Corresponding

Validation Questionnaires

FRFC-P itema

Risk Factor Validation Questionnaire

19 Social support Social Support Inventory (SSI)

20 21

Serious verbal conflict between adults Physical conflict or violence between adults

Conflict Tactics Scale (CTS)

22 Relationship happiness with partner Abbreviated Dyadic Adjustment Scale (ADAS)

23 24 35

Stress Depression or anxiety Mood influencing discipline

Depression, Anxiety, Stress Scale-21 (DASS21)

25 Criminal offence Offending History (OH)

29 Serious mental health problem Psychoticism subscale from the Symptom Check List - 90 (SCL-90)

30 Parental physical/medical condition Short Form - 36 (SF-36)

31 32 33 36 37 38 39 40

Warm relationship with child Involvement with child Child praised for doing something well Agreement over discipline Set and enforce rules Yell or speak harshly to child Physical punishment Severe physical punishment

Alabama Parenting Questionnaire (APQ)

a Validation data were not collected for FRFC-P items 1-18, 26-28, or 41-48 because these were constructed in a single item format typical of the measures employed in epidemiological research. Data to validate item 34 (monitoring & supervision of child) were provided by the APQ, but when this item was found to explain zero variance, it was dropped from the validity analyses (see Chapter 5).

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respectively. Both subscales were used to validate the FRFC-P item on perceived

social support in times of need (item 19).

Conflict Tactics Scale (CTS). The CTS (Straus, 1979) assesses the frequency

with which various conflict resolution tactics have been employed between couples in

the past year. Straus’ (1979) version of the CTS is comprised of ‘verbal aggression’

and ‘violence’ subscales. The rates of verbal and aggressive acts reported on the CTS

are consistent with the rates obtained from in-depth interview studies, providing

evidence of its construct validity (Straus, 1979). Based on a nationally representative

sample of US couples, Cronbach’s alpha coefficients for the combined couple scores

are .88 and .88 for the verbal aggression and violence subscales, respectively. A

revised, 35-item version of the CTS was used in the current study. This version

consists of four subscales that assess progressively more maladaptive methods of

handling conflict: positive communication, aversive verbal, physical threat, and

physical abuse. The 11-item aversive verbal and 12-item physical abuse subscales

have many items in common with the verbal aggression and violence subscales of the

earlier version. They were used, respectively, to validate the FRFC-P item on serious

verbal conflict (item 20), and the FRFC-P item on physical conflict between adults in

the home (item 21).

Procedure

Time 1 Based on several difficulties experienced with the Wave A engagement and data

collection process in 1998, improvements were made to Wave B procedures in 1999,

resulting in several differences between the Wave A and Wave B procedures at Time

1. These differences and their impact on response rates and sample characteristics are

described below. A draft manuscript on the impact of the different recruitment

strategies can be found in Appendix D.

Initial Approvals and Support Gain project approval. Two types of approval were sought and obtained. Ethical

clearance was granted by the Queensland University of Technology (QUT) Human

Research Ethics Committee (Ref. No. 1353/2H), and permission to conduct research in

Queensland state schools was obtained from Education Queensland (Ref. No. 38/98).

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Establishment of PROMAS Advisory Committee. Letters were sent to relevant

people inviting them to become members of the PROMAS Advisory Committee. The

Committee was formed to provide: (1) consultation on project development, subject

recruitment, program implementation and evaluation, and dissemination of findings; and (2)

practical support by facilitating access to the target population. The group met twice a year

and consisted of 21 members, including: health promotion academics, psychologists,

representatives from Education Queensland and Queensland Health, and principals and

guidance officers (not from PROMAS schools).

Engagement of Schools The recruitment package for schools contained information sheets that outlined the

PROMAS goals, timeline, measurements, school requirements, and incentives for school

participation (see Appendix H). One week after the recruitment packages had been posted

to schools, telephone calls were made to each school to ascertain if the package had been

received and if the Principal was interested in participating in the project. Because of the

busy nature of school offices and the difficulty in catching Principals at a free moment,

often several phone calls were made to each school, before speaking to the Principal.

The first telephone contact with Principals usually involved a brief summary of the

aims and procedures of the project. On the basis of this telephone conversation, Principals

responded in one of three ways: (1) agreed straight away to participate in the project, (2)

immediately declined the invitation to participate (most commonly cited reason being the

heavy work load of teachers), or (3) requested a few extra days to discuss the project with

other school personnel before making a decision. Wave A schools were ‘recruited’ if and

when the Principal gave verbal consent for the school to participate.

Due to negative comments made by some Wave A teachers when they were asked to

complete questionnaires, and resentment expressed over not explicitly being consulted as to

whether they wanted to participate (independent of their Principal’s approval), the procedure

for recruiting Wave B schools differed in one major respect. The PROMAS Project and its

requirements were fully explained to teachers prior to school recruitment for all Wave B

schools. This took the form of a recruitment presentation for the teachers at each school

(see Appendix I). Wave B schools were ‘recruited’ if, after the recruitment presentation,

both the Principal and the teachers supported the project and gave verbal consent for the

school to participate.

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Each school was asked to nominate a Liaison Officer (LO) who agreed to be the

school contact person for the PROMAS team. It was preferable that LOs were not

Principals (who were seen as too busy), and that they have an interest in mental health. The

majority of LOs attended a half day training session at QUT in 1999. For Wave A schools,

the training took place in the year of their one year follow-up, and for Wave B schools, the

training occurred immediately prior to their first teacher data collection. The purpose of the

training was fourfold: (1) to familiarise (or remind) LOs with the main aims of PROMAS,

(2) to familiarise LOs with the questionnaires to be used in that year of data collection, (3)

to outline the timeline for data collection and the role and expected tasks of LOs, and (4) to

elicit suggestions from LOs concerning how to maximise response rates and remain

sensitive to the culture and needs of individual participating schools. Lunch was provided at

the end of the training session. For those LOs who were not able to attend the training,

individual follow-up meetings were arranged and conducted at the relevant schools.

Engagement of Parents In the two weeks following the recruitment of a school (before mailing out

questionnaires to parents), the procedure was as follows. (1) The school was asked to

fax their Participation Contract (see Appendix H) to the PROMAS research team; (2)

The school was then asked to provide the research team with a list of children

enrolled from preschool to grade 3; (3) The rolls were stratified by gender, grade, and

class, and across the strata, equal numbers of children were randomly selected (60 per

school). A list of the selected children was then faxed back to each school; (4) Flyers

were given out to all preschool through grade 3 children to take home to notify their

parents that the school had agreed to participate in the PROMAS project, that they

may receive an assessment package in the next week, and that they should contact

either the research team or the school if they did not wish to participate; and (5) Each

school was asked to fax the research team the names and addresses of the parents

whose children had been randomly selected.

Due to two complaints from Wave A parents who were posted questionnaires

without ever having received the PROMAS flyer, modifications were made to the

Wave B parent engagement procedure to better inform parents about PROMAS and

give them extra opportunities to opt out of the project prior to receiving

questionnaires. First, extra effort was made to include information about PROMAS in

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each school newsletter. Second, there was greater contact with Wave B LOs prior to

parent data collection. Specifically, the list of randomly selected children was

checked with the LO from each school in order to determine if the family of any child

on the list was ‘unsuitable’ for participation in the PROMAS Project. A small

number of children were replaced whose parents: (1) were considered likely to

complain about being sent sensitive questionnaires; or (2) did not speak enough

English to complete questionnaires and were considered unlikely to obtain help to do

so. A final modification was sending a letter home through the school to the parents

of the 60 children who had been randomly selected, prior to the mail out of

questionnaires. This letter, which had a QUT return address, explained the project in

more detail (including the sensitive nature of the questionnaires) and asked parents to

contact the research team or the school if they did not want their address released to

the research team. Posting the letter from the school directly to the parents of the

selected children helped to overcome some of the limitations of relying solely on the

children to take the flyer home to their parents. Any letter that was ‘Returned to

Sender’ was posted back to the school to be given to the child to take home to his/her

parents.

Data Collection Key instruments. Following school and parent engagement, assessment

packages were posted out to 1717 parents at Time 1. These packages contained the

FRFC-P, CBCL, an information sheet explaining the research (see Appendix J), and

two consent forms, one for parent participation and contact details, and the other

requesting parents’ consent for teacher completion of questionnaires about their child

(see Appendix K). In addition, each assessment package contained a Reply Paid

envelope to enable parents to return their questionnaires directly to the research team

and an AUD $1 scratch-it lottery ticket to thank parents in anticipation of receiving

back their completed questionnaires.

Two weeks after the assessment packages were mailed out, thank you letters

were posted to the parents who had returned completed questionnaires and reminder

letters were posted to parents who had not yet returned questionnaires. Four weeks

after the initial mail out, thank you letters were sent out to parents who had since

returned their completed questionnaires and a replacement survey was mailed to

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parents who still had not returned the questionnaires. Final thank you letters were

posted to parents 7 weeks after the initial mail out.

For assessment packages that were ‘Returned to Sender’ (due to incorrect

address), several attempts were made to obtain an updated address from the school. If

an updated address was not available, the package was sent to the school to be given

to the child to take home to his/her parents. Due to the lack of addresses for these

parents, reminders and replacement surveys could not be posted to this small group (N

= 18 in Wave A; N = 28 in Wave B).

After obtaining active parental consent, questionnaire packages were sent to

the classroom teachers of 920 children at Time 1. Each teacher was asked to

complete questionnaires for an average of three children (range = 1-12). Teacher

packages contained a copy of the FRFC-T and TRF (or C-TRF for preschool teachers)

for each participating child, one Teacher Details Sheet (to collect basic demographic

information; see Appendix L), and one information sheet explaining the research (see

Appendix J). Similar to the parent packages, each teacher assessment package

contained a Reply Paid envelope and an AUD $1 lottery ‘scratch-it’ ticket. Each set

of child questionnaires was accompanied by a signed parent consent form for teacher

participation.

Three weeks after the initial parent mail out, the first round of teacher

assessment packages were given to teachers. The Time 1 procedure for distributing

the first round of teacher questionnaires differed for the Wave A and B schools. For

Wave A schools, the packages were physically handed over to teachers at the end of a

meeting arranged at each school to explain the project to teachers. This meeting was

the first time most Wave A teachers had heard about the PROMAS project5. For

Wave B schools, packages were couriered out to each LO to distribute and explain to

teachers (who had already heard about the PROMAS project during their recruitment

presentations).

After all the parent questionnaires had been received (7 weeks after the initial

parent mail out), the second round of assessment packages were given to teachers.

The packages were posted to or dropped off at each school for LOs to distribute to the

teachers involved. Teachers were given the option of returning their questionnaires in

5 Only a few Principals had informed their teachers about PROMAS prior to this meeting. It was partly due to this lack of consultation that, during one of these meetings, a Wave A school decided to drop out

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a reply paid envelope or depositing their questionnaires in a PROMAS box left in the

office at each school. Two weeks after the second round of questionnaires had been

given to the teachers, reminder letters were posted to teachers who had not yet

returned their questionnaires and thank you letters were posted to those teachers who

had. Four weeks after the second round of teacher packages had been distributed,

replacement surveys were sent to teachers who still had not returned the

questionnaires and thank you letters were posted to teachers who had since returned

the measures. Teachers were given a separate set of replacement questionnaires for

each child for whom they had not yet returned questionnaires. Before reminders or

replacement surveys were sent, the PROMAS box at each school office was checked

to ensure that reminders/replacement questionnaires were not given to teachers who

had already completed questionnaires. Final thank you letters were sent to teachers 7

weeks after distributing the second round of assessment packages. The procedure for

distributing reminder, replacement, and thank you letters was very similar for Wave A

and B schools, the only difference being that for Wave A schools, packages were sent

to each teacher individually, whereas for Wave B schools, the packages for each

teacher were always bundled together and sent directly to the LOs to hand out to

teachers.

Validation instruments. To avoid overburdening parents, the validation

questionnaires were divided into two booklets. Of the 1022 parents who returned

their completed FRFC-P at Time 1, 530 (52%) were randomly selected to be sent a

validation booklet, with half the subsample receiving Booklet 1 and the other half

receiving Booklet 2. Similar to the procedure used to encourage parents to return key

questionnaires, reminder letters were sent at 2 weeks and replacement validation

booklets were sent at 4 weeks after the initial validation mail out.

Time 2 Prior to collecting one year follow-up data, it was important to successfully

reengage the schools. This was particularly the case for the Wave A schools, who had

not received the initial teacher recruitment presentation at Time 1.

of the PROMAS project. Therefore, for this school, no teacher data were collected, and parent data were available only for Time 1.

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Reengagement of Schools and Parents PROMAS newsletters. From 1999 onwards, PROMAS newsletters were

produced twice a year to provide parents and teachers with project updates and other

useful information concerning children’s mental health (see Appendix M for a copy of

the first issue). These newsletters were timed to help reengage schools immediately

prior to follow-up and also served to keep track of parents’ addresses (see below).

Meetings with Principals and Liaison Officers. In June 1999, a letter was sent

out to the Principal of each Wave A school, explaining that the one year follow-up

was due shortly and requesting a meeting to update the Principal and LO on

PROMAS activities. Phone calls were then made to each Wave A school to set an

appointment time. The resulting meetings took about half an hour and included

discussion of several topics. First, Principals and LOs were informed that, since the

Time 1 data collection, PROMAS had received substantial funding from the National

Health and Medical Research Council (NH&MRC), the Australian Rotary Health

Research Foundation, and the Australian Research Council (ARC). It was explained

that this funding would enable the PROMAS team to provide professional

development opportunities for school staff and training for LOs to improve

communication between the school and the PROMAS team. Second, PROMAS plans

for the remainder of the year were outlined. This involved describing the proposed

timeline for the one year follow-up and the steps necessary to prepare parents and

teachers for data collection. Third, Principals were consulted in relation to their

method of choice for recontacting parents and updating mailing addresses. The

options entailed mailing out PROMAS newsletters through the schools or sending

them out directly through QUT. The latter option required checking school records in

order to update parents’ addresses and to determine which children had transferred to

new schools. Since participating parents had provided signed consent at Time 1 to

participate in PROMAS for the duration of the project, nearly all Wave A schools

(eight out of nine) took the second option, and agreed to the PROMAS team updating

parents’ addresses against school records. This option also required less work for the

school. Finally, a time was arranged to present the results of the Time 1 data

collection to teachers. These feedback presentations were scheduled to occur shortly

before the Time 2 data collection.

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The Wave B Principals and LOs were also provided with a similar PROMAS

update prior to their Time 2 data collection. However, since the PROMAS team had

held meetings with Wave B principals and LOs at Time 1, it was deemed unnecessary

to repeat these at Time 2. For Wave B schools, information concerning the one year

follow-up was provided to LOs via telephone contact, and parents’ addresses were

checked by faxing the LOs lists of the participating parents and their addresses held

from the previous year. The LOs, themselves, then checked these details against their

school records and provided the PROMAS team with updated lists, which were then

used for the newsletter mail out.

Feedback presentations. Feedback presentations for teachers were held at all

schools in preparation for the Time 2 data collection. These presentations served to

update teachers on PROMAS activities, discuss the findings from Time 1, and explain

the Time 2 data collection tasks. At the end of each presentation, an AUD $50 cheque

was handed over to Principals, in conjunction with a certificate of participation for

each school. To further engender good will about the project, the PROMAS team

provided cake for teachers to eat during the presentations.

Tracking parents. A major challenge of longitudinal survey-based research is

keeping track of families as they change schools or move homes. The following steps

were taken to minimise the loss to follow-up of participants who had moved house.

(1) As described above, parents’ addresses were checked and updated against school

records. (2) PROMAS newsletters were sent out to parents just prior to the one year

follow-up. The cover letter that was enclosed with the newsletter included a tear-off

slip for parents to return if they had moved address since the Time 1 data collection.

(3) The parents of any newsletters that were ‘Returned to Sender’ were telephoned,

using the phone numbers parents had written on their consent forms the previous year.

(4) In the event that a parent’s telephone number had changed or was disconnected,

the next option was to phone a friend or relative of the family, who had been

nominated on the original consent form. (5) If, after these efforts, a parent’s address

could not be obtained, the questionnaires were sent to the school to be handed to the

child to take home to his/her parents. If the child had moved school, the

questionnaires were sent to the child’s new school (if known). (6) Finally, if the new

school was unknown, then the Brisbane White Pages telephone book was searched for

contact details.

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Data Collection Key instruments. The Time 2 data collection for both Waves of schools

followed the same procedure as that described for Time 1 (Wave B), with the CBCL

and FRFC-P mailed out to 987 parents and the TRF to the teachers of 749 children.

Reliability FRFC-P. Of the 315 Wave A parents who were sent the FRFC-P

at Time 2, 255 (81%) had returned these within a 9 week period and were sent a

second, reliability FRFC-P to complete. The gap of no more than 9 weeks between

the first and second administration of the FRFC-P was judged to be adequate for

ensuring that most questionnaires would be returned, but not so long that changes in

risk factor exposures would be expected. Reminder and replacement questionnaires

were posted at 2 and 4 weeks, respectively.

Sample Response Rates and Characteristics Response Rates

Response rates to mail surveys are affected by many factors (for reviews see:

Armstrong, White, & Saracci, 1994; Harvey, 1987; Yammarino, Skinner, & Childers,

1991). Important features of the current study that aimed to maximise both parent and

teacher response rates included the reminder letters, replacement surveys, and the

enclosure of Reply Paid envelopes and AUD $1 lottery ‘scratch-it’ tickets within the

questionnaire packages. Previous research has shown that enclosing a small gratuity

with the initial questionnaires achieves higher response rates than when the gratuity is

promised contingent upon the return of questionnaires (Church, 1993; Hopkins &

Gullickson, 1992). However, the offering of participation in a prize draw for the

completion and return of questionnaires has also been found to increase response rates

(Hubbard & Little, 1988). For parents, the major incentive to return questionnaires

was entry into a random draw for an AUD $200 Myer Shopping voucher. For

teachers, the incentive was the school being entered into a draw for a new computer if

the school achieved a teacher response rate of 100%. Schools that achieved teacher

response rates of over 90% were sent a platter of chocolates to thank them for their

efforts. In addition, chocolates and cards were sent to administration staff at the end

of each year of data collection and all schools received AUD $50, regardless of the

teacher response rate.

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Other potential influences on response rates were also addressed. For

example, the cover letters sent out with the questionnaires were personalised so that

they contained the respective parent’s or teacher’s name, and each cover letter was

hand-signed by a member of the PROMAS research team. In addition, all

questionnaire cover sheets were brightly coloured to attract attention and contained

simple instructions on questionnaire completion. Finally, ‘nonresponder’ letters,

requesting permission for the release of parents’ telephone numbers, were mailed out

to parents who had not returned their questionnaires 8 weeks after the initial mail out

each year (see ‘Nonresponders’ section below for further details). Telephone calls

were subsequently made to a random subsample of parents who had not refused

permission. During these calls, parents were invited to answer FRFC-P items over the

telephone and were reminded to return their other questionnaires by mail.

Table 4.2 provides a summary of the response rates achieved for each type of

questionnaire at both Time 1 and Time 2. At Time 1, any parent who returned

questionnaires by mail was considered to be a PROMAS ‘participant’ and was sent

questionnaires again at Time 2, whereas any parent who answered FRFC-P items by

telephone was considered a ‘nonresponder’, and their data were not included in the

main analyses described in this thesis. In contrast, at Time 2, ‘participants’ were any

parent who provided questionnaire responses either by mail or telephone, since all of

these persons had provided data by mail at Time 1.

Table 4.2 Questionnaire Response Rates Across Times, Waves, and Respondents

Questionnaire Wave A Wave B Total

Sent (N)

Returned

(N)

Response

Rate (%)

Sent (N)

Returned

(N)

Response

Rate (%)

Sent (N)

Returned

(N)

Response

Rate (%)

Time 1

nt Questionnaires

FC-P CL idation Booklet 1 idation Booklet 2

her Questionnaires

FC-T F/C-TRF

634 634 125 125

291 291

343 345 106 103

206 208

54.1 54.4 84.8 82.4

70.8 71.5

1083 1083

140 140

629 629

679 677 121 122

550 551

62.7 62.5 86.4 87.1

87.4 87.6

1717 1717

265 265

920 920

1022 1022

227 225

756 759

59.5 59.5 85.7 84.9

82.2 82.5

Time 2

nt Questionnaires

FC-P CL iability FRFC-P

her Questionnaires

F

315 315 255

247

277a 258 221

181

87.9a 81.9 86.7

73.3

672 672 ----

502

577a 547 ----

415

85.9a 81.4 ----

82.7

987 987 255

749

854a 805 221

596

86.5a 81.6 86.7

79.6

Note. FRFC-P = Family Risk Factor Checklist - Parent; CBCL = Child Behaviour Checklist; FRFC-T = Family Risk Factor Checklist - Teacher; TRF = Teacher Report Form; C-TRF = Caregiver-Teacher Report Form.

a Includes participants who answered FRFC-P items over the telephone at Time 2.

Methods

63

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As shown in Table 4.2, at Time 1, parents provided FRFC-P data for 1022 (60%)

children and CBCL data also for 1022 children (60%). Teachers completed the FRFC-T

for 756 (82%) of these children, and the TRF/C-TRF for 759 (83%) children. Therefore,

Time 1 data were obtained from both parents and teachers for 44% of the original eligible

sample of 1717 children. Time 2 data were obtained from both parents and teachers for

596 (35%) of the original eligible sample. Response rates for the validation booklets and

reliability FRFC-P were all above 80%.

There were differences between the Wave A and Wave B response rates, probably

as a result of the different engagement procedures (described above). At Time 1, Wave A

parent response rates for the key questionnaires were lower than the Wave B parent

response rates (54% vs 63%, respectively). This difference disappeared at Time 2 (both

waves - 82%). For teachers, there was a large difference in Wave A versus Wave B

response rates at both Time 1 (71% vs 87%, respectively) and Time 2 (73% vs 83%,

respectively). These findings underscore the importance of the school engagement

process to maximising subsequent response rates. This issue is explored further in a draft

paper on recruitment strategies, found in Appendix D.

Nonresponders Although the parent response rates are relatively high and compare favourably to

other studies involving school-based surveys (e.g., Atkins et al., 1987), it was considered

important to test the representativeness of the current sample by comparing the

characteristics of participants with those of nonresponders. At Time 1, the

‘nonresponder’ letters were sent to a random subsample (30%) of Wave A nonresponders

8 weeks after the initial mail out. Of these parents, all who did not refuse consent were

selected to telephone. In comparison, nonresponder letters were sent to all Wave B

nonresponders (whose addresses were known) and a random subsample of these parents

was selected to telephone. The posting of these letters to all Wave B nonresponders was

due to the finding the previous year that several Wave A nonresponders were prompted to

finally return their questionnaires after receiving the nonresponder letter.

Table 4.3 shows details of the number of nonresponders who were sent letters at

Time 1 and subsequently contacted by telephone to answer FRFC-P questions. Of the

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65

parents who were posted nonresponder letters, only a small proportion refused to release

their phone number (8% and 6% for Wave A and B parents, respectively). Nearly every

Wave A nonresponder actually contacted (92%) and the majority of Wave B

nonresponders contacted (67%) agreed to answer FRFC-P questions over the telephone.

The difference between the proportion of Wave A and B parents agreeing to

answer FRFC questions over the phone is most likely due to the different emphasis

placed on obtaining answers to FRFC-P items versus reminding parents to return their

questionnaires. The priority for Wave A was to have as many nonresponding parents

answering FRFC-P items by phone as possible, and every effort was made to achieve

this. However, for Wave B parents, the nonresponder phone call was used more

frequently as a reminder to return the key questionnaires.

Table 4.3

Summary of the Number of Nonresponders Selected and Contacted to Complete FRFC-P

Questions over the Telephone at Time 1

Wave No. sent

nonresponder letter

No. refused permission for release of telephone number

No. selected to contact by telephone

No. actually contacted by telephone

No. answered FRFC-P questions over telephone

A 88 7 81 49 45

B 374 24 100 81 54

Sample Characteristics: Schools

Table 4.4 shows the characteristics of the 27 participating schools. Wave A

schools tended to be smaller than Wave B schools, as evidenced by smaller enrolment

sizes (mean total enrolment size = 334.4 vs 495.1), fewer staff (23.7 vs 28.4), and fewer

students per staff member (14.6 vs 17.4). However, Wave A and B schools were similar

in socioeconomic status (IRSEDs = 198.1 vs 201.5), and in the mean percentage of non-

english speaking (7.9% vs 5.5%) and indigenous students (0.3 vs 0.6).

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Table 4.4

Characteristics of Participating Schools at Time 1

School IDa Enrolment Size Staff Numbers

Student/ Staff ratio

IRSED % NESB or ESL

% Indig-enous

P-3 total

Total

Wave A

1 184 397 27 14.7 213 0.0 0.0

2 207 425 37 11.5 177 22.1 0.5

3 201 356 27 13.2 189 0.8 0.0

4 241 359 19 18.9 209 0.0 0.0

5 155 309 17 18.2 206 0.0 0.0

6 190 316 19 16.6 209 0.6 0.0

8 121 212 22 9.6 183 29.7 1.9

9 223 357 21 17.0 199 1.1 0.0

10 117 279 24 11.6 198 17.2 0.0

Wave B

11 249 592 35 16.9 207 4.4 0.3

12 166 314 23 13.7 204 0.0 1.0

13 251 464 23 20.2 202 0.4 0.6

14 153 297 18 16.5 211 0.0 0.0

15 178 309 17 18.2 186 2.9 6.5

16 373 704 34 20.7 213 0.4 0.4

17 274 556 33 16.8 213 20.7 0.0

18 218 442 29 15.2 198 1.1 0.0

19 218 441 24 18.4 196 0.7 0.0

20 239 491 23 21.3 208 3.7 0.0

21 299 588 37 15.9 180 23.1 0.7

22 168 351 23 15.3 204 6.6 0.3

23 335 672 36 18.7 187 0.7 1.8

24 302 586 32 18.3 213 1.9 0.0

25 295 631 41 15.4 192 7.9 0.0

26 168 316 19 16.6 201 22.5 0.0

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Table 4.4 cont.

School IDa Enrolment Size Staff Numbers

Student/ Staff ratio

IRSED % NESB or ESL

% Indig-enous

P-3 total

Total

27 399 692 36 19.2 207 0.7 0.0

28 223 465 29 16.0 205 1.1 0.0

Wave A Mean 182.1 334.4 23.7 14.6 198.1 7.9 0.3

Wave A SD 40.7 60.3 5.8 3.1 11.9 11.1 0.6

Wave B Mean 250.4 495.1 28.4 17.4 201.5 5.5 0.6

Wave B SD 70.8 135.3 7.2 2.0 9.6 7.8 1.5

TOTAL MEAN 227.7 441.5 26.9 16.5 200.4 6.3 0.5

TOTAL SD 70.2 138.4 7.1 2.8 10.6 9.1 1.3

Note. ID = Identification; IRSED = Index of Relative Socioeconomic Disadvantage; NESB = Non-English Speaking Background; ESL = English as a Second Language; SD = Standard Deviation. a Wave A schools = 1-10; Wave B schools = 11-28.

Sample Characteristics: Parents Table 4.5 shows the characteristics of both participating and nonresponding

parents (and children). The only significance difference between the demographic

characteristics of participants and nonresponders was the gender of the parent completing

the FRFC-P. A slightly higher percentage of females returned the FRFC-P by mail

(participants; 89%) than answered questions over the telephone (nonresponders; 80%).

Similarly, with the exception of the SES risk domain, there were few differences between

the percentage of participants and nonresponders at medium or high levels of risk across

any of the risk domains, and participants did not differ from nonresponders in the total

number of risk factors reported. A higher percentage of nonresponders were at medium

risk in the SES domain than participants (40.8% vs 27.5%, p = .020). The difference

between the percentage of nonresponders and participants at medium risk in the ALI

domain approached significance (42.1% vs 31.5%, p = .067).

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Table 4.5

Demographic Characteristics and Proportions of Children at Different Levels of Risk in Each

FRFC-P Domain by Participating Versus Nonresponding Parents at Time 1

Participants (N=1022a)

Non-responders

(N=99a)

Sig (p)b

Demographic Characteristics Mean (SD) Mean (SD)

Child age 6.9 (1.2) 6.8 (1.2) .416

Parent age 35.5 (5.7) 34.7 (6.1) .203

Children living with family 2.6 (1.1) 2.7 (1.4) .160

% (N) % (N)

Gender of parent completing FRFC-P (female) 89.0 (910) 79.8 (79) .013

Single parent families 18.6 (190) 22.2 (22) .419

Aboriginal or Torres Strait Islander origin 4.2 (42) 4.0 (4) 1.00

Language other than English spoken at home 11.0 (112) 16.2 (16) .186

Families with either parent unemployed 8.5 (87) 8.2 (8) 1.00

Parent did not complete high school 53.4 (542) 58.2 (57) .397

Income less than AUD$20,800 per year 23.7 (232) 24.1 (19) 1.00

FRFC-P Risk Domains % (N)

(N=1022)

% (N)

(N=76c)

Adverse life events & instability (ALI)

Low

Medium

High

63.4 (648)

31.5 (322)

5.1 (52)

56.6 (43)

42.1 (32)

1.3 (1)

.067

Family structure and SES (SES)

Low

Medium

High

58.4 (597)

27.5 (281)

14.1 (144)

42.1 (32)

40.8 (31)

17.1 (13)

.020

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Table 4.5 cont.

Participants (N=1022a)

Non-responders

(N=99a)

Sig (p)b

Parenting practices (PAR)

Low

Medium

High

53.0 (542)

40.1 (410)

6.8 (70)

55.3 (42)

32.9 (25)

11.8 (9)

.233

Parental verbal conflict and mood problems (VCM)

Low

Medium

High

29.7 (303)

46.8 (478)

23.5 (240)

32.9 (25)

50.0 (38)

17.1 (13)

.419

Parental antisocial & psychotic behaviour (APB)

Low

Medium

High

72.6 (742)

25.3 (259)

2.1 (21)

76.3 (58)

21.1 (16)

2.6 (2)

.694

Total FRFC-P risk score

Low (0-6 risk factors)

Medium (7-12)

High (13 or more)

63.8 (652)

30.8 (315)

5.4 (55)

59.2 (45)

32.9 (25)

7.9 (6)

.589

a Total N; percentages are based on the available N, which fluctuates slightly for each item. b t-tests used to test the differences between means and chi-squared tests used to test the differences between proportions. c Seventy-six equals 99 less 23 nonresponders who answered the basic demographic items but did not complete items for the five FRFC-P risk domains.

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Table 4.6

Demographic Characteristics and Proportions of Children at Different Levels of Risk in Each

FRFC-P Domain by Wave A Versus Wave B Parents at Time 1

Wave A (N=343a)

Wave B (N=679a)

Sig (p)b


Child age 6.9 (1.2) 6.9 (1.2) .891

Parent age 36.5 (5.7) 35.0 (5.7) <.001

Children living with family 2.6 (1.1) 2.6 (1.1) .559

% (N) % (N)

Gender of parent completing FRFC-P (female) 87.2 (299) 90.0 (611) .203


Aboriginal or Torres Strait Islander origin 5.0 (17) 3.7 (25) .405

Language other than English spoken at home 13.5 (46) 9.8 (66) .089

Families with either parent unemployed 9.1 (31) 8.3 (56) .722

Parent did not complete high school 41.3 (140) 59.5 (402) <.001

Income less than AUD$20,800 per year 25.2 (85) 22.9 (147) .429

FRFC-P Risk Domains % (N) % (N)

Adverse life events & instability (ALI)

Low

Medium

High

62.1 (213)

30.9 (106)

7.0 (24)

64.1 (435)

31.8 (216)

4.1 (28)

.161

Family structure and SES (SES)

Low

Medium

High

59.8 (205)

25.1 (86)

15.2 (52)

57.7 (392)

28.7 (195)

13.5 (92)

.423

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Table 4.6 cont.

Wave A (N=343a)

Wave B (N=679a)

Sig (p)b

Parenting practices (PAR)

Low

Medium

High

50.7 (174)

40.8 (140)

8.5 (29)

54.2 (368)

39.8 (270)

6.0 (41)

.296

Parental verbal conflict and mood problems (VCM)

Low Medium High

26.8 (92) 43.4 (149) 29.7 (102)

31.1 (211) 48.5 (329) 20.4 (138)

.004

Parental antisocial & psychotic behaviour (APB)

Low Medium High

72.6 (249) 24.8 (85)

2.6 (9)

72.6 (493) 25.6 (174)

1.8 (12)

.684

Total FRFC-P risk score

Low (0-6 risk factors) Medium (7-12) High (13 or more)

61.2 (210) 31.5 (108)

7.3 (25)

65.1 (442) 30.5 (207)

4.4 (30)

.140

a Total N; percentages are based on the available N, which fluctuates slightly for each item. b t-tests used to test the differences between means and chi-squared tests used to test the differences between proportions.

The characteristics of participating families were also examined across school

waves, as shown in Table 4.6. This analysis revealed no differences in the demographic

characteristics of participants at Wave A versus Wave B schools, except for the number

of parents completing high school. A higher proportion of Wave B parents (59.5%) did

not complete high school compared with Wave A parents (41.3%). The difference

between the mean age of Wave A parents (36.5 years) and Wave B parents (35.0 years)

was also statistically significant, but the magnitude of the difference was small and not

meaningful. Similarly, there was only one significant difference between Wave A and

Wave B children across the FRFC-P risk domains. This was for the VCM domain, for

which there were a higher proportion of Wave A children at high risk (29.7%) than Wave

B children (20.4%).

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Sample Characteristics: Teachers Table 4.7 shows the characteristics of teachers who participated at Time 1. They

were mostly female (95.5%), in their late thirties (overall mean = 38.7, SD = 9.6), and

had been teaching for an overall average of 13.3 years (SD = 8.4). The majority of

teachers had completed a four year degree or higher (64.7%).

As shown in Table 4.7, Wave A teachers were slightly older and had slightly

more teaching experience than Wave B teachers, but the differences were small.

Differences in the proportion of female teachers or level of qualifications across the two

waves were nonsignificant.

Table 4.7

Characteristics of Teachers who Returned Completed Questionnaires at Time 1 (N =

220a)

Characteristic Wave A Wave B Sig (p)b Total

Mean (SD) Mean (SD) Mean (SD)

Age 41.0 (8.6) 38.0 (9.9) .049 38.7 (9.6)

No. of years teaching 15.5 (8.2) 12.6 (8.3) .021 13.3 (8.4)

% (N) % (N) % (N)

Gender (female) 96.4 (53) 95.2 (157) .744 95.5 (210)

Highest qualifications

Certificate

Diploma

3 year degree

4 year degree

Masters degree

5.5 (3)

10.9 (6)

10.9 (6)

69.1 (38)

3.6 (2)

3.1 (5)

21.5 (35)

13.5 (22)

59.5 (97)

2.5 (4)

.443

3.7 (8)

18.8 (41)

12.8 (28)

61.9 (135)

2.8 (6)

a Total N; percentages are based on the available N, which fluctuates slightly for each item. b t-tests used to test the differences between means and chi-squared tests used to test the differences between proportions.

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Data Management and Analysis Data Management

The administration of data collection to parents and teachers was managed using

the Microsoft Access 97 database. This enabled the PROMAS team to keep track of

where individual participants were up to, specifically, who had been sent questionnaires,

who had returned them, and when reminders and replacement letters were due. It also

recorded the most recent details for parents and was used through mail merge procedures

to generate the appropriate covering letters to each mail out.

Quality Assurance. To aid in data cleaning and help ensure consistency of coding

from one year to the next, a Data Checking Manual was developed (available upon

request). This manual describes, in detail, the coding decisions made for each

questionnaire, such as the range of possible responses for each item and the rules for

handling missing values or for when more than one response is circled. All

questionnaires were checked and cleaned prior to sending them to a professional data

entry company, where they were double entered and checked for discrepancies. Data

files were sent to the PROMAS team in ASCII format and then imported into SPSS,

where they were further checked and cleaned.

Data Analysis All analyses were undertaken using SPSS (version 10.0) or SUDAAN (release

7.5), and are explained in Chapters 5-7. The criterion for statistical significance was set

at the conventional level of p < .05 (two-tailed) for all analyses. Given the small number

of differences between the characteristics of Wave A and Wave B schools, parents, and

teachers, participants from both waves were combined for all subsequent analyses.

Prospective sample size calculations. Two prospective sample size calculations

were conducted prior to the main study. The first calculation was based on analyses

planned for Phase One and employed the formula to estimate a single proportion to a

specified level of precision (Kirkwood, 1988). The prevalence of childhood mental

health problems in the community ranges from 15-20% (e.g., Sawyer et al., 2000) and

longitudinal research has shown that mental health risk factors such as parental divorce,

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74

family financial difficulties, maternal depression, and high rates of negative life events

range in annual prevalence from 3-15% (Fergusson, Dimond, Horwood, & Shannon,

1984a; Fergusson & Horwood, 1984; Fergusson, Horwood, & Shannon, 1984b). To

reliably detect (at p < .05 level of significance) a risk factor prevalence of 3% (the least

prevalent risk factor), with a standard error of 1%, the required group size is 291 children.

The sample size was then adjusted to allow for 25% parental nonresponse rate at

Time 1 and 10% attrition each year. In order to retain a final sample of 291 children by

Time 3, nearly 500 children were required at Time 1. Calculation: 500 - 125 (25%

nonresponse at Time 1) - 38 (10% attrition between Time 1 & Time 2) - 34 (10% attrition

between Time 2 & Time 3) = 303.

Finally, the sample size was further inflated to account for additional sampling

variability introduced by the clustering of children within schools. Assuming a moderate

intra-class correlation coefficient of .03 for clusters of 50 children per school, the

inflation factor, or design effect (Carlin & Hocking, 1999), was 2.47. This inflation

factor resulted in a required initial sample size of 1235 children (500 × 2.47). Thus, an

initial sample size of 1235 children was considered large enough to provide accurate

prevalence estimates after taking into account projected sample losses and sampling

variability due to clustering within schools.

The second power calculation was based on analyses planned for Phase Two and

used the formula for comparing two means (Kirkwood, 1988). For this intervention

phase, it is important that meaningful changes in the primary outcome variables (CBCL

& TRF scores) and differences between the two intervention groups will be reliably

detected. In the absence of available data on the distribution of CBCL or TRF change

scores, the comparison was based on testing for differences between the standard and

enhanced intervention groups at the final follow-up (in 2006), and assumed equality of

means and standard deviations at the start of the intervention (in 2003). To have 90%

power to reliably detect (at the p < .05 level) a difference between groups of 10.0 in the

total CBCL score, given a population t-score standard deviation of 9.9 (Achenbach,

1991a), a sample size of 21 per intervention group (or total of 42) is required.

Similar to the first sample size calculation, the above figure was then adjusted

upwards to allow for 25% nonresponse to questionnaires at the start of Phase Two and

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75

10% attrition each subsequent year. This produced a new sample size estimate of 80

children. Calculation: 80 - 20 (25% nonresponse in 2003) - 6 (10% attrition between

2003 & 2004) - 5 (10% attrition between 2004 & 2005) - 5 (10% attrition between 2005

and 2006) = 44. Further inflation for the design effect gave a final sample size estimate

of 198 children (80 × 2.47).

Thus, a sample size of 12 schools per condition with a minimum of 50 children

per school was considered sufficient to obtain precise prevalence estimates at Phase One

and detect significant differences between intervention groups at Phase Two.

Based on pilot study figures, which showed a higher parental nonresponse rate

than anticipated (36%; see Appendix E), a larger number of schools (27) and participants

(60 per school) were accordingly recruited to the main study. In addition, retrospective

power calculations were conducted for results that were considered meaningful, but were

nonsignificant. These are reported in Chapters 5-7.

Population Assessment of Family Risk Factors

76

CHAPTER 5 - POPULATION LEVEL ASSESSMENT OF THE

FAMILY RISK FACTORS RELATED TO THE ONSET OR

PERSISTENCE OF CHILDREN’S MENTAL HEALTH

PROBLEMS

Sarah B. Dwyer, Jan M. Nicholson, Diana Battistutta

halla

This article is not available online. Please consult the hardcopy thesis available from the QUT Library

Teachers’ Knowledge of Family Risk Factors

104

CHAPTER 6 - TEACHERS’ KNOWLEDGE OF CHILDREN’S

EXPOSURE TO FAMILY RISK FACTORS: ACCURACY,

SOURCES, AND USEFULNESS

Sarah B. Dwyer, Jan M. Nicholson, Diana Battistutta, Brian Oldenburg

halla


Identification of Children at Risk

127

CHAPTER 7 - IDENTIFICATION OF CHILDREN AT RISK

OF DEVELOPING INTERNALISING OR EXTERNALISING

MENTAL HEALTH PROBLEMS: A COMPARISON OF

SCREENING METHODS

Sarah B. Dwyer, Jan M. Nicholson, Diana Battistutta

halla


24/12/04 General Discussion

156

CHAPTER 8 - GENERAL DISCUSSION

This body of research investigated the family risk factors related to the onset

or persistence of children’s mental health problems, teachers’ ability to identify these

family risk factors, and parent- and teacher-report screening methods for identifying

at-risk children. There were six key aims. The results relevant to each aim are

summarised below.

Summary of Results Paper 1 (Chapter 5) investigated the psychometric properties of the FRFC-P

and the potential for its use at a population level to establish community risk factor

profiles that may facilitate intervention planning. Specifically, it had two aims:

(1) To determine the reliability and validity of a new screening tool designed

to assess children’s exposure to family risk factors at a population level.

(2) To determine the relative importance of different family risk factors to the

onset versus persistence of children’s mental health problems.

Results showed that the FRFC-P had satisfactory test-retest reliability and

construct validity, but modest internal consistency. With respect to the second aim,

risk assessed by the parenting practices (PAR) domain was the most important

determinant of mental health problem onset, while the PAR, parental verbal conflict

and mood problems (VCM), and parental antisocial and psychotic behaviour (APB)

domains were the strongest predictors of mental health problem persistence.

Paper 2 (Chapter 6) examined teachers’ knowledge of children’s exposure to

family risk factors using the FRFC-T. This paper had three aims, corresponding to

the third, fourth, and fifth aims of the dissertation:

(3) To determine the extent and accuracy of teachers’ family background

knowledge and whether this knowledge varies by the child’s year level,

SES, gender, or behaviour.

(4) To determine the sources that teachers access when assessing a child’s

exposure to family risk factors.

(5) To determine the usefulness of teachers’ ratings of children’s exposure to

family risk factors by examining the relationship between teacher-rated

family risk factors and children’s future mental health outcomes.


157

While teachers had accurate knowledge of children’s exposure to risk factors

within the adverse life events and instability (ALI) and family structure and SES

(SES) domains, they were not able to reliably detect children’s exposure to risk

factors within the PAR, VCM, or APB domains - the types of risk factors found in

Paper 1 (Chapter 5) to be the most strongly related to children’s mental health

problems. In terms of the extent of teachers’ family background knowledge, they

knew more about the family backgrounds of preschool children, females, and children

who did not have significant behaviour problems, than children of older year levels,

males, or children with clinically significant mental health problems, respectively.

Teachers knew less about the family backgrounds of children whom they perceived to

be of low SES compared with children from middle or high SES families. In terms of

the accuracy of teachers’ knowledge, this varied only by the child’s behaviour and

perceived SES, with teachers answering fewer items ‘correctly’ for children whose

behaviour was in the clinical range or children from families of perceived lower SES.

Results relevant to the fourth aim showed that for all five risk domains

teachers relied strongly on their own observation, the child’s parents, and the child for

obtaining information on children’s exposure to family risk factors. For the ALI and

SES domains, teachers were also able to obtain information from school records.

With respect to the fifth aim, it was found that teachers’ knowledge of

children’s exposure to risk factors within the ALI and SES domains remained a

significant predictor of children’s future mental health problems, even after

accounting for children’s behaviour at the Time 1 assessment.

Paper 3 (Chapter 7) investigated the potential of both the FRFC-P and FRFC-

T, along with behavioural and simple nomination screening methods, for identifying

individual, at-risk children. It had one aim:

(6) To compare the predictive accuracy of different parent and teacher

screening methods for identifying children at risk of developing internalising versus

externalising mental health problems.

Results indicated that for both parents and teachers, the behavioural screening

methods were superior, however, the simple nomination method also showed promise

for teachers. Both parents and teachers were more accurate at identifying children at

risk of externalising mental health problems than children at risk of internalising

problems. The performance of the FRFC and simple nomination methods in

identifying children for selective interventions, before the development of significant


158

behavioural or emotional problems, were also tested. Both the FRFC and simple

nomination methods showed only modest predictive accuracy for these children.

These results have already been discussed at the end of each paper. Below is a

synthesis of results across the three papers.

Synthesis of Results The combined results from these three papers have several implications. First,

the FRFC had good predictive criterion-related validity for groups of children, but

relatively low criterion-related validity for individual children. This was exemplified

in the current research by the high (in epidemiological standards) odds ratios (ORs)

between the FRFC risk factor domains and mental health problems at one year

follow-up (see Paper 1 & 2), but the relatively low sensitivity of the measure for

screening individual children (see Paper 3). These results are consistent with

observations that predictions based upon screening instruments are valid for groups of

children but much less powerful for individuals within those groups (Costello &

Angold, 1988; Keogh, 2000). Thus, although the FRFC-P may be useful in

establishing community risk profiles to inform intervention planning, it cannot be

used to reliably identify individual at-risk children for interventions.

In some respects, the low predictive accuracy for individual children is not

surprising, given the complex interplay between risk and protective factors. However,

the poor performance at the individual level is also due to limitations inherent in any

‘high risk’ approach to prevention. In almost any group of ‘high risk’ individuals,

only a small minority will develop disorder over a specified time period, and average

risk will thus be low (Rockhill, Kawachi, & Colditz, 2000). This is one of the

arguments used in favour of a population-level approach to prevention (Rockhill et

al., 2000; Rose, 1992). The association between exposure and outcome is weak when

applied at the individual level, but if a large population of individuals is exposed to a

weak causal factor, then prevention efforts to interrupt this exposure may have a large

benefit at the population level (Doll, 1996; Zubrick, 2002). It is acknowledged that

population-level approaches may ultimately have a greater impact on the prevalence

of children’s mental health problems in the community than high risk approaches to

prevention, particularly if proximal risk exposures are addressed. Nevertheless, with

mental health practitioners continuing to work in settings of high demand with long


159

waiting lists (Zubrick, 2002), there will always remain a need for targeted preventive

interventions to fill the gap between universal and treatment services.

An important question therefore arises: how can we improve risk estimation at

the individual level? There are several potential options, depending upon whether the

aim is to identify children for selective or indicated interventions. For selective

interventions, mediational identification strategies (Pillow et al., 1991) may be used.

Mediational screening involves identifying children on the basis of their exposure to

modifiable mediating variables that are the target of the subsequent intervention. This

approach therefore differs from selective intervention strategies because it specifically

focuses on modifiable factors believed to be causally related to pathology, and differs

from indicated screening in that the children selected do not necessarily have

concurrent behavioural or emotional symptoms (Pillow et al., 1991). Pillow et al.

(1991) argue that screening on modifiable mediators may increase statistical power to

detect program effects, enhance the cost-effectiveness of intervention trials, and

decrease the possibility of iatrogenic effects. Similar to screening children for their

exposure to multiple concurrent risk factors, mediational screening also has the

potential to identify at-risk children earlier on the developmental pathway to disorder

than indicated strategies (Emery, 1991). It may improve predictive accuracy for

selective interventions because of its focus on causal risk factors. By identifying only

those children exposed to causal risk factors, this method potentially selects a group

of children at higher risk than children identified on the basis of non-causal risk

factors. Using a mediational screening approach, Pillow et al. (1991) obtained

respectable sensitivity (92.3%) but low specificity (31.8%) for cross-sectional

prediction of depression. Further research is needed to clarify the sensitivity and

specificity of this screening method within a longitudinal design.

A second option for improving predictive accuracy for selective interventions

may involve rethinking the wording of items on particular screening instruments. The

current research has shown that teachers were either not able, or not prepared, to

report on children’s exposure to family risk factors within the PAR, VCM, or APB

domains. In future versions of the FRFC-T, items could be rephrased to help

overcome the problems associated with teachers’ understandable aversion to rating

the presence of family risk factors without concrete evidence. For example, instead of

soliciting teachers’ specific responses about the frequency or presence of a risk factor,

items could be rephrased to reflect teachers’ opinions about children’s exposure to


160

risk factors. Teachers may be more inclined to answer items within the PAR, VCM,

and APB domains if their responses are clearly defined as opinions, as opposed to

facts. Presumably, if more reliable information could be obtained from teachers

concerning children’s exposure to risk within these domains, the predictive accuracy

of the FRFC-T may be enhanced. Future research must establish the feasibility and

acceptability of each of these options for teachers.

For indicated interventions, predictive accuracy may be improved by

screening for exposure to multiple family risk factors in conjunction with current

behavioural or emotional problems, either concurrently, or using a multiple gate

approach. The poorer performance of behavioural screening methods in detecting

children at risk of internalising disorders compared with externalising disorders has

led some researchers to suggest that this approach may be especially important when

screening for risk for internalising disorders (e.g., Lochman & the Conduct Problems

Prevention Research Group, 1995). Combinations of the most powerful predictors,

such as parenting practices and early behavioural or emotional problems, may have

better predictive validity than using such predictors individually. While Bennett et al.

(1999) provided evidence to support this possibility for the prediction of externalising

disorders, at least two other studies have found that early adverse family

circumstances or parenting characteristics do not contribute to the prediction of later

externalising disorders (Lochman & the Conduct Problems Prevention Research

Group, 1995; Mesman & Koot, 2001) or internalising disorders (Mesman & Koot,

2001), when child characteristics are accounted for. The teacher-report screening

results of the current study (see Paper 2) suggest that for prediction to be enhanced,

multiple family risk factors need to be screened. The FRFC-T ALI and SES risk

factor domains added significantly to the prediction of mental health problems over

and above children’s behaviour at Time 1, but only for those children exposed to high,

as opposed to medium levels of risk, in these domains. Nevertheless, the odds ratios

(ORs) produced to explore these relationships are only relevant for aggregate data and

caution is needed in inferring that they reflect improved prediction (beyond a child’s

current behaviour) at the individual level. Kraemer et al. (1999) go so far as to say

that it is difficult to justify the use of ORs for interpreting the clinical or policy

significance of a risk factor (p. 268). A better test of the potential for multiple family

risk factors to improve prediction for individual children beyond the child’s current

behaviour would involve using logistic regression techniques to derive linear


161

predictors based on different combinations of predictor variables. Significant linear

predictors could then be tested using Receiver Operating Characteristic (ROC) curve

methods to evaluate the predictive accuracy of each combination (see Bennett et al.,

1999 for an example of the application of this methodology).

A second possibility for improving screening accuracy for indicated

interventions is to develop a screening method that better captures the transactional

nature of risk and protective factors. Such an undertaking represents a significant

challenge for prevention researchers. It requires an understanding of the risk and

protective factors operating at multiple time points. Conceivably, observational

screening methods may enable the assessment of interactions between the child and

his or her environment, but this type of assessment is not usually practical for

population-level screening. While, on the other hand, paper-and-pencil screening

instruments may be more practical for large scale screening, by their very nature, they

provide a snapshot of risk at only one point in time. However, periodic assessment of

risk and protective factors may partially overcome this problem. Administering

paper-and-pencil instruments at more than one point in time may have a better chance

of capturing the dynamic processes occurring between child and environment and

may therefore predict negative mental health outcomes with greater accuracy than

screening methods administered at only a single time point. This screening method

may also have the advantage of detecting children who are exposed to chronic

environmental risk factors as well as at-risk children who may have been missed by

earlier assessments. While several longitudinal studies have involved repeated

assessments of children’s behaviour and family environment characteristics to

investigate risk and protective factors in children’s development (e.g., Fergusson et

al., 1994; Silva, 1990), very few have specifically used such multiple assessments

with screening in mind. Multiple assessment studies that have investigated screening

issues have so far focused on behavioural predictors of risk and have involved six

monthly or annual assessments that may be too far apart to measure child-

environment interactions. For example, Bennett et al. (1999) evaluated the gain in

predictive accuracy achieved as a result of collecting behavioural data at baseline and

6 month follow-up. These researchers found that persistent behavioural symptoms

alone, as determined by elevated rates of behavioural problems at both time points,

did not yield high levels of predictive accuracy for externalising mental health

problems at 30 month follow-up (sensitivity was approximately 20%). Further


162

research is needed to evaluate firstly whether periodic assessment of environmental

risk factors is better able to measure the transactional nature of risk, and secondly,

whether such an approach produces higher predictive accuracy for individual

children.

Another avenue for improving predictive accuracy for individuals involves

considering the choice of informant. Parents and teachers identify different groups of

children as high risk (Offord et al., 1996). Not surprisingly, teacher screens tend to

have stronger relationships with teacher-rated outcomes and parent screens have

stronger relationships with parent-rated outcomes (e.g., Achenbach, McConaughy, &

Howell, 1987; Lochman & the Conduct Problems Prevention Research Group, 1995).

It has been suggested that children with pervasive disorder (i.e., identified by both the

parent and teacher) may be at highest risk for poor mental health outcomes (Bennett et

al., 1998). While this may be true, Bennett et al. (1999) found the predictive accuracy

of a pervasive variable alone to be low. Nevertheless, the combined reports of parents

and teachers may ultimately predict mental health outcomes better than either

informant alone. Lochman and the Conduct Problems Prevention Research Group

(1995) found that combined teacher and parent behavioural reports explained more

outcome variance in externalising problem outcomes for Year 1 children than teacher

screening alone. As children get older, self-report may become increasingly

important in identifying high risk groups, particularly for internalising problems. Peer

assessments too may have potential. It is presently unclear how best to combine

information from different informants and across different environments to estimate

risk. Future research should experiment with optimal ways of combining the reports

of different informants to maximise predictive accuracy.

All these suggestions for improving prediction at the individual level must be

considered in the context of arguments that a risk factor must be associated with a

very strong relative risk (i.e., > 50) before it can serve as a useful screening tool at the

individual level (Rockhill et al., 2000). On this basis, even using the most powerful

combination of risk factors is unlikely to boost measures of sensitivity and specificity

to levels regarded as ‘adequate’ for screening purposes. Even so, our expectations

regarding what constitutes ‘adequate’ screening accuracy may be too high. As argued

in Paper 3 (Chapter 7), provided that the majority of misclassification errors fall into

the false positive category and intervention coordinators guard against the negative

effects of labelling, then the benefits of this type of screening may still outweigh the


163

costs. Of those children destined to develop a mental health disorder, current

screening methods, including the FRFC, detect about half of them. This represents a

great improvement on the small percentage who would otherwise receive only

treatment-oriented services (e.g., Andrews, Henderson, & Hall, 2001; Sawyer et al.,

2001; Zubrick et al., 1995). To illustrate, Bennett et al. (1999) reported that

population coverage of the children in need to care may be boosted from 20% to 40%

when the presence of externalising symptoms was used to designate children as high

risk. Furthermore, compared with universal programs, targeted interventions offer a

more efficient method of allocating resources (Bennett et al., 1999). Any successful

effort to improve accuracy at the individual level will increase potential benefits

associated with targeted interventions.

This body of research also provides insight into the feasibility of offering

selective preventive interventions within the school setting. The combined results are

initially discouraging. Teachers knew the least about the risk factors that were most

important in predicting mental health problems (Paper 2) and the FRFC-T performed

relatively poorly in identifying individual at-risk children (Paper 3). However, there

were also promising indications that there may yet be opportunities for selective

interventions within schools. First, those risk factors that teachers could identify (ALI

& SES), while not being the strongest predictors, were nonetheless predictive of

children’s mental health problems at Time 2, over and above teachers’ observation of

children’s behaviour at Time 1. Second, simple teacher nomination showed relatively

high sensitivity for identifying individual at-risk children. Although there is evidence

that teachers using this method often identify children who already have clinically

significant adjustment problems (Mertin & Wasyluk, 1994), in separate analyses

(reported in Appendix P), it was found that teachers use their knowledge of children’s

exposure to family risk factors when making a judgement concerning a child’s future

risk of developing a mental health problem. In fact, in these analyses, teachers’

knowledge of a child’s family background remained a significant predictor of

teachers’ judgement of a child’s future risk, even after taking into account teachers’

observation of the child’s behaviour. On the basis of these findings, it seems

reasonable to conclude that selective interventions may be feasible within the school

setting - either by asking teachers to identify those children being exposed to ALI- or

SES-type risk factors, or by asking teachers to simply nominate the children whom


164

they think are most likely to develop significant behavioural or emotional problems in

the future.

Finally, these results prepare the way for designing and/or delivering

subsequent interventions that address the most influential risk factors in the

community. The importance of good parenting practices for preventing the onset of

children’s mental health problems was highlighted by the high attributable risk (AR)

of the PAR risk domain. This finding is consistent with a large body of literature on

the relationship between parenting practices and children’s mental health problems

(Dadds & Roth, 2001; Keating & Hertzman, 1999; Patterson, 1982; Pettit & Bates,

1989; Sanders, 1995; Silburn et al., 1996) and suggests that parenting interventions

have the greatest potential to reduce the prevalence of children’s mental health

problems in the population. An example of a parenting intervention that may meet

the needs of the studied community is the Triple P-Positive Parenting Program

(Sanders, 1999; Sanders et al., 2000b). Zubrick (2002) describes a population-level

application of this program and its promising features with respect to reach, cost, and

impact. Based on the observed effect sizes, he concludes that a reduction of about

36.5% in the total proportion of children in the clinical range (on the Eyberg Child

Behavior Inventory; Eyberg & Pincus, 1999; Eyberg & Ross, 1978) should be

achievable by two years post intervention (see also: Zubrick et al., 2002). This figure

corresponds closely to the AR of 40% obtained for the PAR domain in the current

research.

Strengths and Limitations The research described in this dissertation has several strengths and

limitations. These will be briefly outlined in relation to issues associated with the

study design, sample selection, measurement, procedures, and results of the research.

Study design and sampling. The PROMAS Project is a large, longitudinal

school-based study. A state-of-the-art, multi-level recruitment procedure was used to

engage and maintain schools and families in the research. The recruitment strategy is

the subject of a separate (draft) paper (see Appendix D). Despite the relatively large

sample size, there were several ways in which the representativeness of the final

sample may have been compromised. First, only 27 out of a potential pool of 119

eligible schools agreed to participate. While this may reflect the realities of busy


165

schools, it could also indicate a biased sample, with participating schools potentially

being more motivated and organised than nonparticipating schools.

Second, the initial parent response rate was 60%. As explained in the papers,

this figure compares well with response rates obtained in other school-based research,

but immediately raises the question of whether responding parents differed in

systematic ways to nonresponding parents. To test whether participating parents

differed from nonparticipating parents, data were collected from a random sample of

nearly 100 nonparticipating parents via the telephone. The comparison of participants

and nonparticipants revealed very few differences between the two groups, with the

exception that the nonparticipants tended to be of lower SES than participants. By the

time teacher nonreponse was taken into account, those children with both parent and

teacher data available at Time 1 represented only 44% of the original randomly

selected sample of children. Unfortunately, data could not be collected on

nonparticipating teachers.

Third, attrition resulted in further sample losses. About 20% of parents were

lost to the one year follow-up. In Paper 3, further comparisons were conducted to

determine if participants with full Time 1 and Time 2 parent data differed from

participants who were missing (mostly Time 2) data. Similar analyses were also

conducted for children with full Time 1 and Time 2 teacher data and children who

were missing teacher data (see Appendix N). These analyses revealed a more

pronounced pattern of disadvantage for the participants who were missing data. The

effect of sampling bias on the results has been discussed in each of the papers. There

may have been an attenuation of the indices of predictive accuracy reported. Another

side-effect of sample losses was a lack of power to test the direct influence of

children’s gender, SES, behaviour, and year level on the predictive accuracy of the

screening measures at an individual level. This was unfortunate given that the extent

of teachers’ family background knowledge had been found in Paper 2 to vary as a

function of these variables. Finally, there was only a small number of children (25)

who met the criteria for mental health problem onset over the 12 month period of the

study. The attributable risk and relative risk findings need to be replicated with a

larger sample to test their robustness.

Measurement. The reliability and validity of the FRFC-P were reported in

Paper 1. Construct validity was tested by correlating each risk domain with scores

from the relevant combination of validation instruments. Problems with the


166

measurement process occurred when the validation instruments were grouped into

two different booklets prior to deciding on the final item combinations for each

FRFC-P risk domain. The two validation booklets were distributed to different

participants to minimise participant burden, but unfortunately, some of the validation

instruments that were used to validate items within the one risk domain ended up in

different booklets. This necessitated conducting two correlations for some risk

domains (VCM & APB) - first with the relevant validation score from Booklet 1 and

then with the relevant validation score from Booklet 2. Ideally, each risk domain

should have been correlated with the one composite validation score derived from

validation instruments completed by the same group of participants.

As documented in Paper 1, most of the reliability and validity indices for the

FRFC-P were adequate, with the exception of the internal consistency, which was

expected to be fairly low from the outset. It was argued that the relatively low

internal consistency (especially for the APB risk domain) was not particularly

concerning, given the intended use of the instrument to inform intervention planning.

Even so, further thought should be given to the items comprising each FRFC-P risk

domain and their wording. Several suggested improvements to the FRFC-P are listed

in Appendix Q.

The validation process for the FRFC also suffered from a lack of gold

standards. First, there was no gold standard for assessing the risk factors occurring in

the home. Such a gold standard would have been of great use in Paper 2 for

determining the true level of teachers’ family background knowledge. Instead, parent

reports on the FRFC-P had to be relied upon for family risk factor information, with

the consequence that any mismatch between parent and teacher reporting of family

background factors could not necessarily be interpreted as a lack of knowledge on

behalf of the teacher. Nevertheless, in the absence of a gold standard, parent

reporting of their child’s family background is probably an adequate means of

assessment, short of observing the family in their home environment. Particularly

relevant are findings that social desirability influences may not affect parent-report of

family functioning as much as expected (Melchert, 1998). Second, there was no gold

standard for assessing children’s mental health status at one year follow-up. Ideally,

the sensitivity and specificity of the different screening instruments should have been

tested in relation to an independent assessment of children’s mental health outcomes

at Time 2.


167

Offsetting these limitations, there were several strengths to the measurement

approach taken in this research. Considerable effort was made when selecting

validation instruments to choose well-known, well-validated measures. The use of

the CBCL and TRF has enabled comparisons between the prevalence of mental health

problems obtained in the current study with the nationally representative sample of

children surveyed in the Child and Adolescent Component of the National Survey of

Mental Health and Wellbeing (Sawyer et al., 2000) (see paper on PROMAS

methodology in Appendix C). Another strength of the research was the development

of a data checking manual that documented all coding decisions made with respect to

each questionnaire used in the study. This helped to ensure the consistency of

questionnaire scoring from year to year.

Procedure. The operationalisation of mental health problem onset and

persistence in Paper 1 was a strength of the research. To meet the criteria for mental

health problem onset, not only did children need to cross the CBCL clinical cut-off,

they also needed to meet Jacobson and Truax’s (1991) criteria for reliable change

(upwards). These criteria helped to ensure that children in the ‘onset’ group had truly

developed new problems in the course of the year and had not simply gone from

scoring just below the clinical cut-off at Time 1 to scoring just above it at Time 2.

The definition of mental health problem persistence (see Paper 1, p. 87) also made use

of Jacobson and Truax’s (1991) reliable change index, this time ensuring that children

who scored just below the clinical cut-off at one time point and just above at the other

were included in the ‘persistent’ group. This operationalisation of mental health

problem persistence has not been used before. With increasing emphasis being placed

on the importance of distinguishing between risk factors that predict the onset versus

persistence of mental health problems (e.g., Zubrick et al., 2000a), the definition of

persistence used in Paper 1 may prove useful in future research.

The ROC curve techniques applied in Paper 3 constituted another strength of

the research, allowing the predictive performance of the FRFC and behavioural

screening measures to be evaluated across their full range of cut-points. This

procedure helped to ensure that conclusions regarding the sensitivity and specificity

of the screening instruments were not based upon arbitrarily chosen cut-points. A

limitation of this same paper was its failure to report the positive predictive value

(PPV) and negative predictive value (NPV) of each of the screening instruments. The

main reason for this omission was space limitations, but both PPV and NPV provide


168

important additional information about the performance of a screening instrument at

the individual level and should be included in future comparisons of the instruments.

Similarly, the performance of the screening instruments could also have been

compared using an index known as the Relative Improvement Over Chance (RIOC;

Loeber & Dishion, 1987). To avoid confusion associated with trying to reconcile

different patterns of results across different screening instruments and different

predictive indices, the RIOC analyses were not included in the paper, but may be

worth including in future comparisons of the instruments. A final limitation of Paper

3 was its examination of screening data from only one informant at a time. It would

have been interesting to experiment with different ways of combining the screening

data from both parents and teachers to optimise prediction of mental health outcomes.

As mentioned earlier in this chapter, this is an important next step in screening

research.

Results. The data reported in this dissertation help to fill major gaps in the

prevention literature. First, little is known about the AR of risk factors for children’s

mental health problems (Offord, 1996). The results of Paper 1 are of interest for this

reason. Second, no previous research has explored the extent to which teachers are

aware of the family backgrounds of children in their class. The results of Paper 2

have important implications for the feasibility of selective preventive interventions

within the school setting. Third, very little research has directly compared the

predictive performance of behavioural screening methods with risk factor methods,

or, for that matter, investigated the performance of simple nomination. The results of

Paper 3 have practical implications for the selection of appropriate screening

instruments for targeted interventions.

Perhaps one of the major limitations of the present results is their rather static

representation of risk and protective processes. Doll and Lyon (1998) describe three

iterations of risk factor studies. The first generation studies were concerned with

demonstrating that negative life experiences impact upon psychological development.

The second generation studies developed more detailed conceptualisations of how

different types of risk relate to different mental health outcomes and has had

numerous implications for the design of intervention strategies. The third generation

of risk factor studies provides a more dynamic consideration of the interactions

occurring between risk and protective factors. Using this taxonomy, the current

research would be classed as a second generation study - it adds to our knowledge of


169

the AR and RR of different types of risk factors and differentiates between risk factors

that predict the onset versus persistence of mental health problems, but it does not

‘unpack’ the transactional nature of risk nor attempt to measure the dynamic

interactions occurring between risk and resilience factors. Second generation studies

are still necessary, but third generation studies may offer more promise towards

understanding the development of mental health problems and improving methods for

identifying at-risk children. Future research should strive to achieve such third

generation goals.

Future Research Many avenues of future research have been identified throughout the

discussion sections of each paper and above. These are summarised in Table 8.1.

Many of the listed recommendations involve continuing with efforts to improve the

predictive accuracy of screening methods for individual children. This is particularly

important for children at risk of internalising problems, who are frequently missed by

current screening methods.

Table 8.1

Recommendations for Future Research

1. Test the RR and AR of the different FRFC-P risk domains with a larger sample.

2. Establish the psychometric properties and acceptability of a reworded version of the FRFC-T designed to reflect teachers’ opinions regarding children’s exposure to family risk factors as opposed to absolute knowledge of family background.

3. Investigate methods for improving the identification of children at risk of internalising mental health problems.

4. Experiment with optimal ways of combining the reports of different informants to maximise predictive accuracy.

5. Further investigate how variables such as children’s gender, SES, behaviour, and year level influence predictive accuracy, particularly at the individual level.

6. Further investigate the performance of mediational screening techniques using a longitudinal study design.

7. Investigate whether periodic assessment of environmental risk factors is better able to capture the transactional nature of risk and whether this approach is associated with improved predictive accuracy at the individual level.

8. Examine the screening performance of the FRFC over longer follow-up periods.

9. Compare the PPV, NPV, and RIOC of the different screening instruments.


170

10. Examine the predictive accuracy of the screening instruments in relation to an independent assessment of mental health outcomes at follow-up.

Note. RR = relative risk; AR = attributable risk; PPV = positive predictive value; NPV = negative predictive value; RIOC = relative improvement over chance.

A major challenge of future research is to develop risk factor screens that

adequately assess children’s exposure to multiple risk and protective factors, but that

also remain practical to administer at a population level. Attention to the former

objective helps to ensure that screening methods adequately fulfil their function of

discriminating between at-risk and not at-risk children, while attention to the latter

objective helps to ensure that opportunities exist for matching appropriate

interventions to community needs. However, as Costello and Angold (1988) point

out, a scale that is optimal as a screening measure may not provide an adequate

symptom checklist. It may be that different instruments may be needed for each

purpose. If so, then the FRFC-P should be used for the latter purpose, as a checklist

for determining community distributions of risk exposures to provide an evidence

base for planning population-level interventions. During the course of the current

study, several potential improvements to the FRFC have been identified which may

enhance its functioning in this role. These are listed in Appendix Q.

Conclusions This study has contributed to the ‘science of prevention’ by advancing our

understanding of the risk factors related to the onset and persistence of children’s

mental health problems, teachers’ ability to identify family risk factors, and parent-

and teacher-report screening methods. In line with previous research, parenting

practices emerged as an important target for future preventive interventions. The

results suggest that while on the one hand, the FRFC-P is useful for population-level

screening to inform intervention planning, on the other hand, it falls short of

achieving good predictive accuracy for individual children. Further research is

needed to improve screening methods for identifying individual children, particularly

those at risk of developing internalising disorders. A challenge is to develop

screening methods that better assess the transactional nature of risk, but that remain

practical to complete at a population level. Finally, despite the fact that teachers knew

the least about the family risk factors that were most important in predicting mental


171

health problems, there were several indications that selective preventive interventions

may be feasible within the school setting.

“We are still a long way from being able to predict with great accuracy who

will develop a particular disorder given a set of risk and protective variables” (Spence,

1996, p. 14). The development of accurate risk assessment methods to identify at-risk

children (preferably before the onset of significant mental health problems) is a

difficult task, but will produce greater preventive effects and cost-efficiency of

interventions. The current body of research is an important step towards achieving

this goal.

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Appendices

209

APPENDICES

Appendix A 210

APPENDIX A - PAPER 1

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Appendix B 224

APPENDIX B - CORRESPONDENCE CONCERNING

JOURNALS’ RECEIPT OF MANUSCRIPTS

Appendix B 225

X-Received: 4 Sep 2002 18:08:38 GMT Date: Wed, 04 Sep 2002 14:04:02 -0400 From: "Kasel, Keitha" <[email protected]> Subject: AJCP Manuscript Acknowledgement To: "'[email protected]'" <[email protected]> X-Mailer: Internet Mail Service (5.5.2650.10) Delivered-to: dwyer.net%[email protected] Dear Dr. Dwyer, Thank you for thinking of the American Journal of Community Psychology for dissemination of your work. This letter is to formally acknowledge that we have received your manuscript "Teachers' knowledge of children's exposure to family risk factors: Accuracy, sources, and usefulness" (Manuscript #2002089). We have sent it out for review and requested a response by 11/05/2002. Should you have questions now or in the future, please feel free to be in touch. Sincerely, William S. Davidson II Editor, American Journal of Community Psychology

Appendix B 226

From: [email protected] X-Received: 1 Aug 2002 15:59:32 GMT Date: Thu, 01 Aug 2002 11:58:29 -0400 Subject: Manuscript Submitted To: [email protected] Delivered-to: dwyer.net%[email protected] Dear Sarah Dwyer: We are in receipt of your manuscript titled "Identification of children at risk of developing internalising or externalising mental health problems: A comparison of screening methods". You should receive some notice of the status of your manuscript within 60 to 90 days. During this period you should not submit your manuscript to another journal. If your manuscript is accepted for publication, you will be required to transfer your copyright to APA, provide full disclosure of any conflict of interest, and certify compliance with APA ethical principles. Your manuscript number is 2002-0217. To receive an email detailing the history of your manuscript visit http://www.jbo.com/jbofusebox/dsp_checkhistory.cfm?journal_code=ccp and enter your lastname as your username and your manuscript number as your password. Also, please read the APA's Open Letter to Authors located at http://www.jbo.com/jbofusebox/dsp_OpenLetter.cfm. Sincerely, Journal of Consulting & Clinical Psychology Editorial Office

Appendix C 227

APPENDIX C - BASELINE PAPER

Appendix C 228

The Promoting Adjustment in Schools (PROMAS) Project:

Study Design, Methods and Baseline Findings

Jan M. Nicholson, Brian Oldenburg, Sarah B. Dwyer, Diana Battistutta

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Appendix D 258

APPENDIX D - RECRUITMENT PAPER

Appendix D 259

DRAFT MANUSCRIPT - 12th November, 2002

A Successful Multilevel Strategy for Improving Response

Rates in School-based Research

Diana Battistutta, Sarah B. Dwyer, Jan M. Nicholson, Brian Oldenburg

(others and order to be decided)

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Appendix E 277

APPENDIX E - PILOT STUDY

Appendix E 278

Pilot Study Aims 1. To trial the procedure for gaining school approval and distributing the questionnaires to

parents and teachers.

2. To determine the likely parental and teacher questionnaire response rates in order to

provide an estimate of the sample size necessary for the main study.

3. To provide initial data on the content and construct validity of the Family Risk Factor

Checklist - Parent (FRFC-P) and Family Risk Factor Checklist - Teacher (FRFC-T).

Participants The pilot school was a Brisbane state primary school chosen for its convenient

location near QUT. The school was situated in a suburb within the Stafford education

district, and was comprised of primarily white, middle class families. The school had a total

enrolment size of 269. Students from the school were stratified by grade and sex. An even

number of males and females were randomly selected from preschool to grade 5 to make a

total sample size of 50 students. Participants were the parents and classroom teachers of

these 50 children.

Procedure The procedure was very similar to that described for the main study (see

Chapter 4), except that it involved only one assessment period. Questionnaires were

distributed to the parents and then teachers of participating children, with reminder

letters and replacement questionnaires being sent out at two and four weeks after the

initial receipt of assessment packages, respectively.

Results Trial of questionnaire distribution procedure. The reminder letters and

replacement surveys substantially boosted response rates. For example, the teacher

response rate two weeks after they received their final batch of questionnaires (before

the reminder letters were sent out) was 40%. Two weeks later, after having sent out

the reminder letters (but before sending the replacement surveys) the response rate

was 74%.

Appendix E 279

Consultation with the Principal of the pilot school helped to clarify the

appropriate procedure for contacting nonresponding parents. For this school, the

Principal personally phoned all nonresponders to gain their permission for the release

of their phone number to the PROMAS research team. Those nonresponding parents

who did not refuse the release of their phone number were subsequently contacted by

telephone to determine the reason for their nonresponse and to obtain some family

background details.

Final response rates. Of the 50 parents who were mailed assessment

packages, 32 returned completed questionnaires (64%). Teachers completed

questionnaires for 25 of these children, representing a response rate of 78%.

Therefore data were obtained from both parents and teachers for 50% (25 out of 50)

of the original eligible sample.

Initial data on FRFC-P and FRFC-T. As a result of the piloting process,

numerous small problems were identified with the FRFC-P and FRFC-T. A list of the

subsequent changes made to these instruments can be found in Table E.1.

Discussion The pilot study confirmed the importance of sending reminder letters and

replacement surveys to participants. In addition, the necessity of obtaining

permission for the release of the nonresponding parents’ phone numbers was

highlighted, resulting in the use of ‘nonresponder letters’ in the main study that gave

parents the option of refusing the release of their phone number to the research team.

Despite being consistent with other school-based research, the response rates

were lower than hoped for. On the basis of these results, the number of schools and

participants per school necessary to achieve adequate power for the main study were

revised upwards.

Finally, the pilot study enabled several problems with the FRFC-P and FRFC-

T to be fixed prior to the main study. Such changes likely improved the reliability

and validity of the instruments used in the main study.

Appendix E 280

Table E.1

List of Changes Made to the FRFC-P and FRFC-T as a Result of the Pilot Study

Item Change Made

FRFC-P

18 Broadened the time frame to inquire as to whether the child had ever lived with someone other than the parent (as opposed to currently living with someone else)

29 Similar to above, broadened the time frame to inquire as to whether parents had ever suffered from a mental health problem (as opposed to currently suffering from a mental health problem).

27 (a) & (b)

Added ‘including weekends’ to clarify that parents’ estimate of the amount of alcohol they drink in one week was to include weekends.

28 This item on illicit drug use was added.

38-40 Changed the phrase ‘When managing your child’s behaviour’ to ‘When your child has done something wrong’.

20-21 Added the words ‘between adults’ to clarify the nature of the conflict being asked about.

35 Changed a confusing item on the frequency of consistent consequences across time to an item on the extent to which mood influences discipline practices.

37 Reversed the order of response categories so that the highest risk response receives the highest score (consistent with other items).

FRFC-T

16 Reversed the order of response categories so that the highest risk response receives the highest score (consistent with other items).

23 Added an inquiry about illicit drug use to this question to match up with the new illicit drug item added to the parent instrument

33 Italicised the word ‘fail’ to improve comprehension of the item.

7 Added in a ‘no sibling’ response category, and recoded the ‘don’t know’ response for this item from the number ‘7’ to the number ‘9’ (to more clearly differentiate the ‘don’t know’ response from the other response options).

Added extra instructions to the front cover, emphasising that ‘don’t know’ should be circled only if the teacher had no idea of the answer (see instruction point 4). This was done in an attempt to get teachers to give their best estimate of the child’s exposure to risk on each item rather than being constrained by the need for hard evidence before committing to an answer.

Deleted a redundant question on the child’s age (information on children’s grade was collected from teachers and children’s age was collected from the parents).

Deleted a superfluous box for the teacher number on the front cover of the FRFC-T.

Appendix F 281

APPENDIX F

Family Risk Factor Checklist - Parent (FRFC-P)

(Family Background Checklist)

Family Risk Factor Checklist - Teacher (FRFC-T)

Appendix F 282

Child Number

FAMILY BACKGROUND CHECKLIST

Child’s name: __________________________________________________________________________ Parent’s name: __________________________________________________________________________ Name of school your child attends: _________________________________________________________ Name of your child’s teacher: _____________________________________________________________

Instructions 1. This questionnaire is about the family background of your child. Your individual

answers will not be shared with anyone. 2. Please answer this questionnaire in relation to your child named below. 3. Please answer the questions by circling a number. While we would like you to

answer all the questions, you do not have to answer a question if you do not want to.

4. There are no right or wrong answers. If you are unsure how to answer a question,

please circle the number closest to what you think best describes your family and make a comment in the margin.

5. Many questions have two parts. On the left side of the page please answer the

question for yourself. Only answer the questions on the right side of the page if you have a partner.

6. When you have answered all the questions, feel free to provide comments in the

space provided at the end of the questionnaire. For example, we would like to know if you found the questionnaire easy to read and understand, and if you were comfortable with the type of questions and the length of time it took to complete the answers.

7. When you have completed the questionnaire, please place it in the reply paid

envelope provided and put it in the post. It will then be posted directly to the researcher and will not be seen by anyone else.

Your time and effort are greatly appreciated,

Thank you.

Appendix F 283

Child Number Family Background Checklist

Section One is a small section about child and parent details. Section Two is about your child’s family background. Section Three is about events that may have happened to you or your child in the last year. Please complete each section to the best of your knowledge for your child named on the cover sheet. SECTION ONE: Child and Parent Details Please write your answer in the space provided or circle the correct number. 1. Your date of birth:____________________ 2. Your child’s date of birth:___________________ 3. Your gender:

4. Your child’s gender:

female 1 female 1 male 2 male 2 5. Which of the following best describes your relationship to your child?

Biological parent 1 Adoptive parent 2 Step parent 3 Foster parent 4 Guardian 5 Other ______________________ 6

6. Do you have any concerns about the current wellbeing and adjustment of your child?

yes 2 no 1 don’t know 0 7. Do you think that your child has a higher chance than average of developing a behavioural,

emotional or mental health problem in the future?

yes 2 no 1 don’t know 0 8. How many children do you have living with you?_________________ SECTION TWO: Child’s Family Background 9 (a) Which of the following best describes your child’s present family situation?

two-parent family 2 [Go to question 9 (b)] one-parent family 1 [Go to question 9 (c)]

Appendix F 2849 (b) If your child is living in a two-parent family, which of the following best describes your child’s

family situation? original two-parent family 3 stepfamily (i.e., parent and non-biological partner) 2 other (e.g., foster family, grandparents) 1

9 (c) If your child is living in a one-parent family, which of the following best describes your child’s

family situation? sole parent from the child’s birth 3 sole parent following divorce or separation 2 sole parent following death of spouse 1

10. Is your child of Aboriginal or Torres Strait Islander origin?

yes 2 no 1

11. Does your family speak a language other than English at home?

yes 2 no 1

12. Do you have problems finding enough money each week to provide your family’s basic needs such

as food and rent? yes 2 no 1

13 (a) Which of the following best describes your current employment status?

Full-time employment 1 What is your occupation? __________________ Part-time employment 2 [Give full title] __________________ Home duties 3 Student 4 [Office use only] Unemployed 5

13 (b) Which of the following best describes the current employment status of your partner?

Full-time employment 1 What is his/her occupation? __________________ Part-time employment 2 [Give full title] __________________ Home duties 3 Student 4 [Office use only] Unemployed 5 No partner 9

14 (a) What is your highest level of education?

Less than grade 10 5 Grade 10-11 4 Grade 12 3 Tertiary degree 1-4 years 2 Masters/Doctorate 1

Appendix F 28514 (b) What is the highest level of education of your partner?

Less than grade 10 5 Grade 10-11 4 Grade 12 3 Tertiary degree 1-4 years 2 Masters/Doctorate 1 No partner 9

15. What is the total amount of income received by ALL members of your household (including

pensions, allowances, and investments)? PER YEAR OR PER FORTNIGHT $1-2,079 $1-79 14 $2,080-4,159 $80-159 13 $4,160-6,239 $160-239 12 $6,240-8,319 $240-319 11 $8,320-10,399 $320-399 10 $10,400-15,599 $400-599 9 $15,600-20,799 $600-799 8 $20,800-25,999 $800-999 7 $26,000-31,199 $1,000-1,199 6 $31,200-36,399 $1,200-1,399 5 $36,400-41,599 $1,400-1,599 4 $41,600-51,999 $1,600-1,999 3 $52,000-77,999 $2,000-2,999 2 $78,000 or more $3,000 or more 1

16. How many times has your family moved house in the last five years?

zero 0 one 1 two 2 three 3 four 4 five 5 six or more 6

17. How many changes of parent figures has your child had in the last five years? [Count one change

for each time a parent figure moved out of, or into the same household as your child.]

zero 0 one 1 two 2 three 3 four 4 five 5 six or more 6

18. Has your child ever lived with someone other than you (e.g., foster home, other care)?

If yes, please describe yes 2 ___________________________________________ no 1

Appendix F 28619. Do you have friends and/or relatives who can be relied on to provide support in times of need?

yes 1 no 2

20. How frequently does serious verbal conflict (e.g., heated arguing) occur between adults in your

home?

never/seldom 1 sometimes 2 often 3

21. How frequently does physical conflict or violence (e.g., slapping, punching) occur between adults

in your home?

never/seldom 1 sometimes 2 often 3

22. [Only answer question 22 if your child lives in a two-parent family.]

Below, the middle number, “happy” represents the degree of happiness of most relationships. Please circle the number which best describes the degree of happiness, all things considered, of your relationship.

7 6 5 4 3 2 1

Extremely unhappy

Fairly

unhappy

A little

unhappy

Happy

Very happy

Extremely

happy

Perfect

no partner 9

23. How often do you (or your partner) feel stressed?

You

Your partner

never/seldom 1 never/seldom 1 sometimes 2 sometimes 2 often 3 often 3 no partner 9

24. How often do you (or your partner) suffer from depression or anxiety?

You

Your partner


Appendix F 28725. How often have you (or your partner) been in trouble with the law or committed an offence (e.g.,

assault, stealing)? [Do not include minor traffic offences such as speeding or parking tickets.]

You

Your partner

never 1 never 1 seldom 2 seldom 2 sometimes 3 sometimes 3 often 4 often 4 no partner 9

26. On average, how often do you (or your partner) drink alcohol?

You

Your partner

never/seldom 1 never/seldom 1 special occasions 2 special occasions 2 once or twice a week 3 once or twice a week 3 3-4 days per week 4 3-4 days per week 4 5-6 days per week 5 5-6 days per week 5 every day 6 every day 6 no partner 9

27 (a) On the average, write in the number you would normally drink in a week (including weekends): Beer No. Wine No. Spirits/

Liqueurs No. Sherry No. Cocktails No.

glass/can glass nip glass glass large can bottle bottle (small) bottle bottle bottle (large) jug flagon 27 (b) On the average, write in the number your partner would normally drink in a week (including

weekends): Beer No. Wine No. Spirits/

Liqueurs No. Sherry No. Cocktails No.

glass/can glass nip glass glass large can bottle bottle (small) bottle bottle bottle (large) jug flagon no partner 9

Appendix F 28828. On average, how often do you (or your partner) use illicit drugs (e.g., marijuana, ecstasy)?

You

Your partner

never/seldom 1 never/seldom 1 special occasions 2 special occasions 2 once or twice a week 3 once or twice a week 3 3-4 days per week 4 3-4 days per week 4 5-6 days per week 5 5-6 days per week 5 every day 6 every day 6 no partner 9

29. Have you (or your partner) ever suffered from a serious mental health problem (e.g., nervous

breakdown, psychotic disorder, schizophrenia)?

You

Your partner

yes 2 yes 2 no 1 no 1 no partner 9

30. Do you (or your partner) suffer from a physical/medical condition (e.g., multiple sclerosis,

diabetes, a heart condition) that affects your ability to perform the normal activities of daily life?

You

Your partner


31. Do you (or your partner) have a warm, caring relationship with your child?

You

Your partner

never/seldom 4 never/seldom 4 sometimes 3 sometimes 3 often 2 often 2 always 1 always 1 no partner 9

32. How often do you (or your partner) play games with or talk to your child?

You

Your partner


Appendix F 28933. How often do you (or your partner) praise your child when he/she has done something well?

You

Your partner


34. How often do you (or your partner) know where your child is, who your child is with, and what

your child is doing?

You

Your partner


35. How much does your mood or the way you are feeling influence the way you (or your partner)

discipline your child?

You

Your partner


36. [Only answer question 36 if your child lives in a two-parent family.] How often do you and your partner agree on the best way to discipline your child?

never/seldom 4 sometimes 3 often 2 always 1 no partner 9

37. How often do you (or your partner) set and enforce rules or limits on your child’s behaviour?

You

Your partner


Appendix F 29038. When your child has done something wrong, how often do you (or your partner) yell or speak

harshly to your child?

You

Your partner


39. When your child has done something wrong, how often do you (or your partner) physically punish

your child (e.g., smacking, grabbing, or slapping)?

You

Your partner


40. When your child has done something wrong, how often do you (or your partner) spank your child

repeatedly, belt your child, or hit your child with an object other than your hand?

You

Your partner


SECTION THREE: Life Events The following questions concern events that may have happened to you or your child in the last year. 41. Have you (or your partner) become unemployed in the last year?

You

Your partner


42. Have you (or your partner) spent any time in prison in the last year?

You

Your partner


Appendix F 291 43. Have you divorced or separated from a partner in the last year?

yes 2 no 1

44. Has your family moved house in the last year?

yes 2 no 1

45. Has your child lived with someone other than you (e.g., foster home, other care) in the last year?

yes 2 no 1

46. Has your child had a change of parent figure in the last year (e.g., a partner moved out of the

household or a new partner lived with the family)? yes 2 no 1

47. Has an immediate family member died in the last year?

yes 2 no 1

48. In the last year, have any other events occurred which have upset or distressed you or your child?

yes___________________ ___________________

2

no

1

We are interested in finding out what you thought of this questionnaire. Please feel free to provide any comments or feedback concerning the content or structure of this questionnaire. ___________________________________________________________________________________________________

___________________________________________________________________________________________________

___________________________________________________________________________________________________

Thank you for completing this questionnaire.

Please check you have answered each question, then return the questionnaire in the envelope provided.

Appendix F 292

Teacher Number Child Number

FAMILY RISK FACTOR CHECKLIST - TEACHER

Name of school: ______________________________________________________ Teacher’s name: _____________________________________________________ Child’s name: _______________________________________________________

Instructions 1. This questionnaire assesses the extent to which teachers are aware of the family

background of their students. Your individual answers will not be shared with anyone.

2. Please answer this questionnaire in relation to the child in your class named below. 3. Most of the questions consist of two parts. For the first part, please answer the

question by circling a number. For the second part, please tick a box, indicating where you got your information from to answer the first part of the question.

4. Please circle ‘don’t know’ only if you really have no idea of the answer.

Otherwise, circle what you believe is probably the correct answer. 5. If you have circled ‘don’t know’ for the first part of the question, you do not need to

tick a box for the second part of the question. 6. If you got your information from more than one person or source, tick each box

that applies. 7. When you have answered all the questions, feel free to provide comments in the

space provided at the end of the questionnaire. For example, we would like to know if you found the questionnaire easy to read and understand, and if you were comfortable with the length of time it took to complete the answers.

8. When you have completed the questionnaire, please place it in the reply paid

envelope provided and put it in the post. It will then be posted directly to the researcher and will not be seen by anyone else.

Your time and effort are greatly appreciated,

Thank you.

Appendix F 293

Teacher Number Child Number

Family Risk Factor Checklist - Teacher

Section One is a small section about child details. Section Two is about chronic family risk factors. Section Three is about adverse life events that have happened in the last year. Please complete each section to the best of your knowledge for the child named on the cover sheet. SECTION ONE: Child Details Please write your answer in the space provided or circle the correct number. 1. Child’s year level:_____________________ 2. Child’s gender:

female 1 male 2

3. Do you have any concerns about the current wellbeing and adjustment of this child?

yes 2 no 1 don’t know 0 4. Do you think that this child has a higher chance than average of developing a behavioural,

emotional or mental health problem in the future? yes 2 no 1 don’t know 0

5. How long have you known this child?

1 2 3 4 less than 1 month 2-5 months 6-12 months 12 months or longer

SECTION TWO: Chronic Family Risk Factors 6 (a) Which of the following best describes this child’s present family situation? [If you don’t know, go

to question 7.] the child ❑ child’s parent/s ❑two-parent family 2 Where did you get this information from? child’s peers ❑one-parent family 1 other teachers ❑ school record ❑don’t know 0 own observation ❑ other___________________ ❑

Appendix F 2946 (b) If the child is living in a two-parent family, which of the following best describes his/her family

situation? [If the child lives in a one-parent family, go to question 6 (c).] the child ❑original two-parent family 3 Where did you get this child’s parent/s ❑stepfamily (e.g., parent and nonbiological partner) 2 information from? child’s peers ❑other (e.g., foster family) 1 other teachers ❑ school record ❑don’t know 0 own observation ❑ other_________ ❑ 6 (c) If the child is living in a one-parent family, which of the following best describes his/her family

situation? the child ❑sole parent from the child’s birth 3 Where did you get this child’s parent/s ❑sole parent following divorce or separation 2 information from? child’s peers ❑sole parent following death of spouse 1 other teachers ❑ school record ❑don’t know 0 own observation ❑ other_________ ❑7. How many siblings live in the same household as this child? none 0 one 1 the child ❑two 2 child’s parent/s ❑three 3 Where did you get this information from? child’s peers ❑four 4 other teachers ❑five 5 school record ❑six or more 6 own observation ❑ other___________________ ❑don’t know 9 8. Is the child of Aboriginal or Torres Strait Islander origin? the child ❑ child’s parent/s ❑yes 2 Where did you get this information from? child’s peers ❑no 1 other teachers ❑ school record ❑don’t know 0 own observation ❑ other___________________ ❑ 9. Does the child’s family speak a language other than English at home? the child ❑ child’s parent/s ❑yes 2 Where did you get this information from? child’s peers ❑no 1 other teachers ❑ school record ❑don’t know 0 own observation ❑

Appendix F 295 other___________________ ❑10. Do the parent/s have problems finding enough money each week to provide their family’s basic

needs such as food and rent?

the child ❑ child’s parent/s ❑yes 2 Where did you get this information from? child’s peers ❑no 1 other teachers ❑ school record ❑don’t know 0 own observation ❑ other___________________ ❑ 11. Would either of the child’s parents consider him/herself to be unemployed? the child ❑ child’s parent/s ❑yes 2 Where did you get this information from? child’s peers ❑no 1 other teachers ❑ school record ❑don’t know 0 own observation ❑ other___________________ ❑ 12. In general, how would you rate the socioeconomic status of this family (think about their level of

education and income)?

3 2 1 0 low middle high don’t know

13. Does the child’s family frequently move house (e.g., move one or more times per year)? the child ❑ child’s parent/s ❑yes 2 Where did you get this information from? child’s peers ❑no 1 other teachers ❑ school record ❑don’t know 0 own observation ❑ other___________________ ❑ 14. Does the child have multiple changes of parent figures (e.g., mother has a series of partners

[one or more per year] who live with the family)?

the child ❑ child’s parent/s ❑yes 2 Where did you get this information from? child’s peers ❑no 1 other teachers ❑ school record ❑don’t know 0 own observation ❑ other___________________ ❑

Appendix F 296 15. Has the child ever lived with someone other than the current family (e.g., foster home, other care)? the child ❑ child’s parent/s ❑yes 2 Where did you get this information from? child’s peers ❑no 1 other teachers ❑ school record ❑don’t know 0 own observation ❑ other___________________ ❑ 16. Do the child’s parent/s have friends and/or relatives who can be relied on to provide support in

times of need? the child ❑ child’s parent/s ❑yes 1 Where did you get this information from? child’s peers ❑no 2 other teachers ❑ school record ❑don’t know 0 own observation ❑ other___________________ ❑ 17. How frequently does serious verbal conflict (e.g., heated arguing) occur between adults in the

child’s home? the child ❑never/seldom 1 Where did you get this information from? child’s parent/s ❑sometimes 2 child’s peers ❑often 3 other teachers ❑ school record ❑don’t know 0 own observation ❑ other___________________ ❑ 18. How frequently does physical conflict or violence (e.g., slapping, punching) occur between adults

in the child’s home? the child ❑never/seldom 1 Where did you get this information from? child’s parent/s ❑sometimes 2 child’s peers ❑often 3 other teachers ❑ school record ❑don’t know 0 own observation ❑ other___________________ ❑ 19. [Only answer question 19 if you know the child lives in a two-parent family.]

In general, are the child’s parents less happy with their relationship than average? the child ❑ child’s parent/s ❑yes 2 Where did you get this information from? child’s peers ❑no 1 other teachers ❑ school record ❑

Appendix F 297don’t know 0 own observation ❑ other___________________ ❑ 20. Does either parent often feel stressed? the child ❑ child’s parent/s ❑yes 2 Where did you get this information from? child’s peers ❑no 1 other teachers ❑ school record ❑don’t know 0 own observation ❑ other___________________ ❑ 21. Does either parent suffer from depression or anxiety? the child ❑ child’s parent/s ❑yes 2 Where did you get this information from? child’s peers ❑no 1 other teachers ❑ school record ❑don’t know 0 own observation ❑ other___________________ ❑ 22. Has either parent ever been in trouble with the law or committed an offence (e.g., assault,

stealing)? [Do not include minor traffic offences such as speeding or parking tickets.]

the child ❑ child’s parent/s ❑yes 2 Where did you get this information from? child’s peers ❑no 1 other teachers ❑ school record ❑don’t know 0 own observation ❑ other___________________ ❑ 23. Does either parent have a problem with alcohol or illicit drug use? the child ❑ child’s parent/s ❑yes 2 Where did you get this information from? child’s peers ❑no 1 other teachers ❑ school record ❑don’t know 0 own observation ❑ other___________________ ❑ 24. Has either parent ever suffered from a serious mental health problem (e.g., nervous breakdown,

psychotic disorder, schizophrenia)? the child ❑ child’s parent/s ❑yes 2 Where did you get this information from? child’s peers ❑

Appendix F 298no 1 other teachers ❑ school record ❑don’t know 0 own observation ❑ other___________________ ❑25. Does either parent suffer from a physical/medical condition (e.g., multiple sclerosis, diabetes, heart

condition) that affects their ability to perform normal activities of daily life? the child ❑ child’s parent/s ❑yes 2 Where did you get this information from? child’s peers ❑no 1 other teachers ❑ school record ❑don’t know 0 own observation ❑ other___________________ ❑ 26. How often do you have concerns about the parenting practices used for this child? the child ❑never/seldom 1 Where did you get the information that child’s parent/s ❑sometimes 2 caused the concerns? child’s peers ❑often 3 other teachers ❑ school record ❑don’t know 0 own observation ❑ other___________________ ❑ 27. Does either parent have a warm, caring relationship with the child? the child ❑ child’s parent/s ❑yes 1 Where did you get this information from? child’s peers ❑no 2 other teachers ❑ school record ❑don’t know 0 own observation ❑ other___________________ ❑ 28. How often do the parent/s play games with or talk to the child? the child ❑never/seldom 1 Where did you get this information from? child’s parent/s ❑sometimes 2 child’s peers ❑often 3 other teachers ❑ school record ❑don’t know 0 own observation ❑ other___________________ ❑ 29. How often do the parent/s praise the child when he/she has done something well? the child ❑never/seldom 1 Where did you get this information from? child’s parent/s ❑

Appendix F 299sometimes 2 child’s peers ❑often 3 other teachers ❑ school record ❑don’t know 0 own observation ❑ other___________________ ❑ 30. Does either parent seem to know where their child is, who their child is with, and what their

child is doing most of the time? the child ❑ child’s parent/s ❑yes 1 Where did you get this information from? child’s peers ❑no 2 other teachers ❑ school record ❑don’t know 0 own observation ❑ other___________________ ❑ 31. Does the mood of the child’s parent/s often influence the way they discipline the child? the child ❑ child’s parent/s ❑yes 2 Where did you get this information from? child’s peers ❑no 1 other teachers ❑ school record ❑don’t know 0 own observation ❑ other___________________ ❑ 32. [Only answer question 32 if you know the child lives in a two-parent family.]

Do the child’s parents usually agree on the best way to discipline the child?

the child ❑ child’s parent/s ❑yes 1 Where did you get this information from? child’s peers ❑no 2 other teachers ❑ school record ❑don’t know 0 own observation ❑ other___________________ ❑ 33. Do the child’s parent/s often fail to set and enforce rules or limits on the child’s behaviour? the child ❑ child’s parent/s ❑yes 2 Where did you get this information from? child’s peers ❑no 1 other teachers ❑ school record ❑don’t know 0 own observation ❑ other___________________ ❑ 34. When the child has done something wrong, how often do the parent/s yell or speak harshly to the

child? the child ❑

Appendix F 300never/seldom 1 Where did you get this information from? child’s parent/s ❑sometimes 2 child’s peers ❑often 3 other teachers ❑ school record ❑don’t know 0 own observation ❑ other___________________ ❑ 35. When the child has done something wrong, how often do the parent/s physically punish the

child (e.g., smacking, grabbing, or slapping child)? the child ❑never/seldom 1 Where did you get this information from? child’s parent/s ❑sometimes 2 child’s peers ❑often 3 other teachers ❑ school record ❑don’t know 0 own observation ❑ other___________________ ❑ 36. When the child has done something wrong, how often do the parent/s spank the child repeatedly,

belt the child, or hit the child with an object other than their hand? the child ❑never/seldom 1 Where did you get this information from? child’s parent/s ❑sometimes 2 child’s peers ❑often 3 other teachers ❑ school record ❑don’t know 0 own observation ❑ other___________________ ❑ SECTION THREE: Adverse Life Events The following questions concern events that may have happened to the child in the last year. 37. Has either parent become unemployed in the last year? the child ❑ child’s parent/s ❑yes 2 Where did you get this information from? child’s peers ❑no 1 other teachers ❑ school record ❑don’t know 0 own observation ❑ other___________________ ❑ 38. Has either parent spent any time in prison in the last year? the child ❑ child’s parent/s ❑yes 2 Where did you get this information from? child’s peers ❑no 1 other teachers ❑ school record ❑don’t know 0 own observation ❑ other___________________ ❑

Appendix F 30139. Have the child’s parents divorced or separated in the last year? the child ❑ child’s parent/s ❑yes 2 Where did you get this information from? child’s peers ❑no 1 other teachers ❑ school record ❑don’t know 0 own observation ❑ other___________________ ❑40. Has the family moved house in the last year? the child ❑ child’s parent/s ❑yes 2 Where did you get this information from? child’s peers ❑no 1 other teachers ❑ school record ❑don’t know 0 own observation ❑ other___________________ ❑ 41. Has the child lived with someone other than the current family (e.g., foster home, other care) in

the last year? the child ❑ child’s parent/s ❑yes 2 Where did you get this information from? child’s peers ❑no 1 other teachers ❑ school record ❑don’t know 0 own observation ❑ other___________________ ❑ 42. Has the child had a change of parent figure in the last year (e.g., mother had a partner who moved

out of the household or a new partner who lived with the family)? the child ❑ child’s parent/s ❑yes 2 Where did you get this information from? child’s peers ❑no 1 other teachers ❑ school record ❑don’t know 0 own observation ❑ other___________________ ❑ 43. Has an immediate family member died in the last year? the child ❑ child’s parent/s ❑yes 2 Where did you get this information from? child’s peers ❑no 1 other teachers ❑ school record ❑don’t know 0 own observation ❑ other___________________ ❑ 44. As far as you are aware, are there any other factors in the family environment that may make it

difficult for the child to develop cognitively, emotionally, or physically?

Appendix F 302 the child ❑ child’s parent/s ❑yes___________________ ___________________

2 Where did you get this information from? child’s peers ❑

no 1 other teachers ❑ school record ❑don’t know 0 own observation ❑ other_____________ ❑ We are interested in finding out what you thought of this questionnaire. Please feel free to provide any comments or feedback concerning the content or structure of this questionnaire. __________________________________________________________________________________________

__________________________________________________________________________________________

__________________________________________________________________________________________

__________________________________________________________________________________________

__________________________________________________________________________________________

Thank you for completing this questionnaire.

Please check you have answered each question, then return the questionnaire in the envelope provided.

Appendix G 303

APPENDIX G - SCORING PROCEDURE FOR FRFC-P

Appendix G 304

Scoring Procedure for FRFC-P Notes: 1. X variables = each variable, including both parent (a) and partner (b) scores,

dichotomised into 'risk absent’ (0) vs ‘risk present’ (1). 2. Y variables = parent and partner X variables summed (NB: only summed for

items with both a parent and partner score). 3. Z variables = Y vars recoded back into a dichotomous variable, taking both the

parent and partner scores into account. 4. Item 9(c) is not dichotomised [all responses to this item are considered 'risk

present', and risk for single parent families is already counted under item 9(a)]. 5. Items 13(a)2 and 13(b)2 on occupation and items 27(a) and 27(b) on amount of

alcohol consumption have special procedures for dichotomising (see syntax below).

6. The following syntax is based on Wave B, Time 1 variables, so all variables end

in 'b1'. After data have been entered and cleaned: Step One: Dichotomise All Variables to Create ‘X’ Variables RECODE fbc8b1 (1 thru 3=0) (4 thru hi =1) INTO xfbc8b1 . VARIABLE LABELS xfbc8b1 'No of ch'. FORMAT xfbc8b1(f1.0). EXECUTE . RECODE fbc9ab1 (2=0) (1=1) INTO xfbc9ab1 . VARIABLE LABELS xfbc9ab1 'parent - family structure'. FORMAT xfbc9ab1(f1.0). EXECUTE . RECODE fbc9bb1 (3=0) (2=1) (1=1) INTO xfbc9bb1 . VARIABLE LABELS xfbc9bb1 'parent - two-parent structure'. FORMAT xfbc9bb1(f1.0). EXECUTE . RECODE fbc10b1 (1=0) (2=1) INTO xfbc10b1 . VARIABLE LABELS xfbc10b1 'parent - aboriginal origin'.

Appendix G 305

FORMAT xfbc10b1(f1.0). EXECUTE . RECODE fbc11b1 (1=0) (2=1) INTO xfbc11b1 . VARIABLE LABELS xfbc11b1 'parent - language other than english at home'. FORMAT xfbc11b1(f1.0). EXECUTE . RECODE fbc12b1 (1=0) (2=1) INTO xfbc12b1 . VARIABLE LABELS xfbc12b1 'parent - financial problems'. FORMAT xfbc12b1(f1.0). EXECUTE . RECODE fb13a1b1 (1=0) (2=0) (3=0) (4=0) (5=1) INTO xf13a1b1 . VARIABLE LABELS xf13a1b1 'parent - employment'. FORMAT xf13a1b1(f1.0). EXECUTE . RECODE fb13b1b1 (1=0) (2=0) (3=0) (4=0) (5=1) INTO xf13b1b1 . VARIABLE LABELS xf13b1b1 'partner - employment'. FORMAT xf13b1b1(f1.0). EXECUTE . RECODE fbc14ab1 (1=0) (2=0) (3=0) (4=1) (5=1) INTO xfb14ab1 . VARIABLE LABELS xfb14ab1 'parent - education'. FORMAT xfb14ab1(f1.0). EXECUTE . RECODE fbc14bb1 (1=0) (2=0) (3=0) (4=1) (5=1) INTO xfb14bb1 . VARIABLE LABELS xfb14bb1 'partner - education'. FORMAT xfb14bb1(f1.0). EXECUTE . RECODE fbc15b1 (1=0) (2=0) (3=0) (4=0) (5=0) (6=0) (7=0) (8=1) (9=1) (10=1) (11=1) (12=1) (13=1) (14=1) INTO xfbc15b1 . VARIABLE LABELS xfbc15b1 'parent - income'. FORMAT xfbc15b1(f1.0). EXECUTE . RECODE fbc16b1

Appendix G 306

(1=0) (2=0) (3=0) (4=0) (0=0) (5=1) (6=1) INTO xfbc16b1 . VARIABLE LABELS xfbc16b1 'moved last 5 yrs'. FORMAT xfbc16b1(f1.0). EXECUTE . RECODE fbc17b1 (0=0) (1=0) (2=0) (3=0) (4=1) (5=1) (6=1) INTO xfbc17b1 . VARIABLE LABELS xfbc17b1 'parent - changes of parent figure'. FORMAT xfbc17b1(f1.0). EXECUTE . RECODE fbc18b1 (1=0) (2=1) INTO xfbc18b1 . VARIABLE LABELS xfbc18b1 'parent - child lived with someone else'. FORMAT xfbc18b1(f1.0). EXECUTE . RECODE fbc19b1 (1=0) (2=1) INTO xfbc19b1 . VARIABLE LABELS xfbc19b1 'parent - social support'. FORMAT xfbc19b1(f1.0). EXECUTE. RECODE fbc20b1 (1=0) (2=1) (3=1) INTO xfbc20b1 . VARIABLE LABELS xfbc20b1 'parent - verbal conflict'. FORMAT xfbc20b1(f1.0). EXECUTE . RECODE fbc21b1 (1=0) (2=1) (3=1) INTO xfbc21b1 . VARIABLE LABELS xfbc21b1 'parent - physical conflict'. FORMAT xfbc21b1(f1.0). EXECUTE . RECODE fbc22b1 (1=0) (2=0) (3=0) (4=0) (5=1) (6=1) (7=1) INTO xfbc22b1 . VARIABLE LABELS xfbc22b1 'parent - happiness'. FORMAT xfbc22b1(f1.0). EXECUTE . RECODE fbc23ab1 (1=0) (2=0) (3=1) INTO xfb23ab1 . VARIABLE LABELS xfb23ab1 'parent - stress'. FORMAT xfb23ab1(f1.0). EXECUTE .

Appendix G 307

RECODE fbc23bb1 (1=0) (2=0) (3=1) INTO xfb23bb1 . VARIABLE LABELS xfb23bb1 'partner - stress'. FORMAT xfb23bb1(f1.0). EXECUTE . RECODE fbc24ab1 (1=0) (2=1) (3=1) INTO xfb24ab1 . VARIABLE LABELS xfb24ab1 'parent - depressed/anxious'. FORMAT xfb24ab1(f1.0). EXECUTE . RECODE fbc24bb1 (1=0) (2=1) (3=1) INTO xfb24bb1 . VARIABLE LABELS xfb24bb1 'partner - depressed/anxious'. FORMAT xfb24bb1(f1.0). EXECUTE . RECODE fbc25ab1 (1=0) (2=1) (3=1) (4=1) INTO xfb25ab1 . VARIABLE LABELS xfb25ab1 'parent - criminal activity'. FORMAT xfb25ab1(f1.0). EXECUTE . RECODE fbc25bb1 (1=0) (2=1) (3=1) (4=1) INTO xfb25bb1 . VARIABLE LABELS xfb25bb1 'partner - criminal activity'. FORMAT xfb25bb1(f1.0). EXECUTE . RECODE fbc26ab1 (1=0) (2=0) (3=0) (4=0) (5=0) (6=1) INTO xfb26ab1 . VARIABLE LABELS xfb26ab1 'parent - frequency alcohol consumption'. FORMAT xfb26ab1(f1.0). EXECUTE . RECODE fbc26bb1 (1=0) (2=0) (3=0) (4=0) (5=0) (6=1) INTO xfb26bb1 . VARIABLE LABELS xfb26bb1 'partner - frequency alcohol consumption'. FORMAT xfb26bb1(f1.0). EXECUTE . RECODE fbc28ab1 (1=0) (2=1) (3=1) (4=1) (5=1) (6=1) INTO xfb28ab1 . VARIABLE LABELS xfb28ab1 'parent - illicit drugs'. FORMAT xfb28ab1(f1.0). EXECUTE .

Appendix G 308

RECODE fbc28bb1 (1=0) (2=1) (3=1) (4=1) (5=1) (6=1) INTO xfb28bb1 . VARIABLE LABELS xfb28bb1 'partner - illicit drugs'. FORMAT xfb28bb1(f1.0). EXECUTE . RECODE fbc29ab1 (1=0) (2=1) INTO xfb29ab1 . VARIABLE LABELS xfb29ab1 'parent - mental condition'. FORMAT xfb29ab1(f1.0). EXECUTE . RECODE fbc29bb1 (1=0) (2=1) INTO xfb29bb1 . VARIABLE LABELS xfb29bb1 'partner - mental condition'. FORMAT xfb29bb1(f1.0). EXECUTE . RECODE fbc30ab1 (1=0) (2=1) INTO xfb30ab1 . VARIABLE LABELS xfb30ab1 'parent - medical condition'. FORMAT xfb30ab1(f1.0). EXECUTE . RECODE fbc30bb1 (1=0) (2=1) INTO xfb30bb1 . VARIABLE LABELS xfb30bb1 'partner - medical condition'. FORMAT xfb30bb1(f1.0). EXECUTE . RECODE fbc31ab1 (1=0) (2=0) (3=1) (4=1) INTO xfb31ab1. VARIABLE LABELS xfb31ab1 'parent - warmth'. FORMAT xfb31ab1(f1.0). EXECUTE . RECODE fbc31bb1 (1=0) (2=0) (3=1) (4=1) INTO xfb31bb1 . VARIABLE LABELS xfb31bb1 'partner - warmth'. FORMAT xfb31bb1(f1.0). EXECUTE . RECODE fbc32ab1 (1=0) (2=0) (3=1) (4=1) INTO xfb32ab1. VARIABLE LABELS xfb32ab1 'parent - involvement'. FORMAT xfb32ab1(f1.0). EXECUTE .

Appendix G 309

RECODE fbc32bb1 (1=0) (2=0) (3=1) (4=1) INTO xfb32bb1 . VARIABLE LABELS xfb32bb1 'partner - involvement'. FORMAT xfb32bb1(f1.0). EXECUTE . RECODE fbc33ab1 (1=0) (2=0) (3=1) (4=1) INTO xfb33ab1 . VARIABLE LABELS xfb33ab1 'parent - praise'. FORMAT xfb33ab1(f1.0). EXECUTE . RECODE fbc33bb1 (1=0) (2=0) (3=1) (4=1) INTO xfb33bb1 . VARIABLE LABELS xfb33bb1 'partner - praise'. FORMAT xfb33bb1(f1.0). EXECUTE . RECODE fbc34ab1 (1=0) (2=0) (3=1) (4=1) INTO xfb34ab1 . VARIABLE LABELS xfb34ab1 'parent - monitoring'. FORMAT xfb34ab1(f1.0). EXECUTE . RECODE fbc34bb1 (1=0) (2=0) (3=1) (4=1) INTO xfb34bb1 . VARIABLE LABELS xfb34bb1 'partner - monitoring'. FORMAT xfb34bb1(f1.0). EXECUTE . RECODE fbc35ab1 (1=0) (2=0) (3=1) (4=1) INTO xfb35ab1 . VARIABLE LABELS xfb35ab1 'parent - mood influence discipline'. FORMAT xfb35ab1(f1.0). EXECUTE . RECODE fbc35bb1 (1=0) (2=0) (3=1) (4=1) INTO xfb35bb1 . VARIABLE LABELS xfb35bb1 'partner - mood influence discipline'. FORMAT xfb35bb1(f1.0). EXECUTE . RECODE fbc36b1 (1=0) (2=0) (3=1) (4=1) INTO xfbc36b1. VARIABLE LABELS xfbc36b1 'parent - agreement over discipline'. FORMAT xfbc36b1(f1.0). EXECUTE .

Appendix G 310

RECODE fbc37ab1 (1=0) (2=0) (3=1) (4=1) INTO xfb37ab1 . VARIABLE LABELS xfb37ab1 'parent - set rules/limits'. FORMAT xfb37ab1(f1.0). EXECUTE . RECODE fbc37bb1 (1=0) (2=0) (3=1) (4=1) INTO xfb37bb1 . VARIABLE LABELS xfb37bb1 'partner - sets rules/limits'. FORMAT xfb37bb1(f1.0). EXECUTE . RECODE fbc38ab1 (1=0) (2=0) (3=1) INTO xfb38ab1 . VARIABLE LABELS xfb38ab1 'parent - yell at child'. FORMAT xfb38ab1(f1.0). EXECUTE . RECODE fbc38bb1 (1=0) (2=0) (3=1) INTO xfb38bb1 . VARIABLE LABELS xfb38bb1 'partner - yell at child'. FORMAT xfb38bb1(f1.0). EXECUTE . RECODE fbc39ab1 (1=0) (2=0) (3=1) INTO xfb39ab1 . VARIABLE LABELS xfb39ab1 'parent - physical punishment'. FORMAT xfb39ab1(f1.0). EXECUTE . RECODE fbc39bb1 (1=0) (2=0) (3=1) INTO xfb39bb1 . VARIABLE LABELS xfb39bb1 'partner - physical punishment'. FORMAT xfb39bb1(f1.0). EXECUTE . RECODE fbc40ab1 (1=0) (2=1) (3=1) INTO xfb40ab1. VARIABLE LABELS xfb40ab1 'parent - belting'. FORMAT xfb40ab1(f1.0). EXECUTE . RECODE fbc40bb1 (1=0) (2=1) (3=1) INTO xfb40bb1. VARIABLE LABELS xfb40bb1 'partner - belting'. FORMAT xfb40bb1(f1.0). EXECUTE .

Appendix G 311

RECODE fbc41ab1 (1=0) (2=1) INTO xfb41ab1 . VARIABLE LABELS xfb41ab1 'parent - unemployed last yr'. FORMAT xfb41ab1(f1.0). EXECUTE . RECODE fbc41bb1 (1=0) (2=1) INTO xfb41bb1. VARIABLE LABELS xfb41bb1 'partner - unemployed last yr'. FORMAT xfb41bb1(f1.0). EXECUTE . RECODE fbc42ab1 (1=0) (2=1) INTO xfb42ab1. VARIABLE LABELS xfb42ab1 'parent - prison last yr'. FORMAT xfb42ab1(f1.0). EXECUTE . RECODE fbc42bb1 (1=0) (2=1) INTO xfb42bb1. VARIABLE LABELS xfb42bb1 'partner - prison last yr'. FORMAT xfb42bb1(f1.0). EXECUTE . RECODE fbc43b1 (1=0) (2=1) INTO xfbc43b1. VARIABLE LABELS xfbc43b1 'parent - divorced last yr'. FORMAT xfbc43b1(f1.0). EXECUTE . RECODE fbc44b1 (1=0) (2=1) INTO xfbc44b1. VARIABLE LABELS xfbc44b1 'parent - moved house last yr'. FORMAT xfbc44b1(f1.0). EXECUTE . RECODE fbc45b1 (1=0) (2=1) INTO xfbc45b1. VARIABLE LABELS xfbc45b1 'parent - child lived with someone else in last yr'. FORMAT xfbc45b1(f1.0). EXECUTE . RECODE fbc46b1 (1=0) (2=1) INTO xfbc46b1. VARIABLE LABELS xfbc46b1 'parent - changed parent figure last yr'. FORMAT xfbc46b1(f1.0). EXECUTE .

Appendix G 312

RECODE fbc47b1 (1=0) (2=1) INTO xfbc47b1. VARIABLE LABELS xfbc47b1 'parent - death of family member last yr'. FORMAT xfbc47b1(f1.0). EXECUTE . RECODE fbc48b1 (1=0) (2=1) INTO xfbc48b1. VARIABLE LABELS xfbc48b1 'parent - any other prob last yr'. FORMAT xfbc48b1(f1.0). EXECUTE . Step Two & Three: Sum Parent and Partner ‘X’ Variables to Create ‘Y’ Variables, then Recode ‘Y’ Variables Back into Two Levels of Risk to Create ‘Z’ Variables Please note: Final dichotomised variables are X variables if there is no partner score and Z variables if there is a partner score. COMPUTE yfb131b1 = SUM(xf13a1b1, xf13b1b1) . FORMAT yfb131b1 (f1.0). EXECUTE. RECODE yfb131b1 (2=1) (1=1) (0=0) INTO zfb131b1 . VARIABLE LABELS zfb131b1 'unemploymt risk - both pars'. FORMAT zfb131b1(f1.0). EXECUTE. COMPUTE yfbc14b1 = SUM(xfb14ab1, xfb14bb1) . FORMAT yfbc14b1(f1.0). EXECUTE. RECODE yfbc14b1 (2=1) (1=1) (0=0) INTO zfbc14b1 . VARIABLE LABELS zfbc14b1 'education risk - both pars'. FORMAT zfbc14b1(f1.0). EXECUTE. COMPUTE yfbc23b1 = SUM(xfb23ab1, xfb23bb1) . FORMAT yfbc23b1(f1.0). EXECUTE. RECODE yfbc23b1 (2=1) (1=1) (0=0) INTO zfbc23b1 . VARIABLE LABELS zfbc23b1 'stress risk - both pars'. FORMAT zfbc23b1(f1.0). EXECUTE . COMPUTE yfbc24b1 = SUM(xfb24ab1, xfb24bb1). FORMAT yfbc24b1(f1.0).

Appendix G 313

EXECUTE. RECODE yfbc24b1 (2=1) (1=1) (0=0) INTO zfbc24b1. VARIABLE LABELS zfbc24b1 'depression/anx risk - both pars'. FORMAT zfbc24b1(f1.0). EXECUTE. COMPUTE yfbc25b1 = SUM(xfb25ab1, xfb25bb1) . FORMAT yfbc25b1(f1.0). EXECUTE . RECODE yfbc25b1 (2=1) (1=1) (0=0) INTO zfbc25b1 . VARIABLE LABELS zfbc25b1 'criminality risk - both pars'. FORMAT zfbc25b1(f1.0). EXECUTE. COMPUTE yfbc26b1 = SUM(xfb26ab1, xfb26bb1) . FORMAT yfbc26b1(f1.0). EXECUTE. RECODE yfbc26b1 (2=1) (1=1) (0=0) INTO zfbc26b1 . VARIABLE LABELS zfbc26b1 'frequency alcohol consumption risk - both pars'. FORMAT zfbc26b1(f1.0). EXECUTE . COMPUTE yfbc28b1 = SUM(xfb28ab1, xfb28bb1) . FORMAT yfbc28b1(f1.0). EXECUTE . RECODE yfbc28b1 (2=1) (1=1) (0=0) INTO zfbc28b1 . VARIABLE LABELS zfbc28b1 'illicit drug risk - both pars'. FORMAT zfbc28b1(f1.0). EXECUTE . COMPUTE yfbc29b1 = SUM(xfb29ab1, xfb29bb1) . FORMAT yfbc29b1(f1.0). EXECUTE . RECODE yfbc29b1 (2=1) (1=1) (0=0) INTO zfbc29b1 . VARIABLE LABELS zfbc29b1 'mental health risk - both pars'. FORMAT zfbc29b1(f1.0). EXECUTE . COMPUTE yfbc30b1 = SUM(xfb30ab1, xfb30bb1) . FORMAT yfbc30b1(f1.0). EXECUTE . RECODE yfbc30b1 (2=1) (1=1) (0=0) INTO zfbc30b1 . VARIABLE LABELS zfbc30b1 'medical condition risk - both pars'.

Appendix G 314

FORMAT zfbc30b1(f1.0). EXECUTE . COMPUTE yfbc31b1 = SUM(xfb31ab1, xfb31bb1) . FORMAT yfbc31b1(f1.0). EXECUTE . RECODE yfbc31b1 (2=1) (1=0) (0=0) INTO zfbc31b1 . VARIABLE LABELS zfbc31b1 'warmth risk - both pars'. FORMAT zfbc31b1(f1.0). EXECUTE . COMPUTE yfbc32b1 = SUM(xfb32ab1, xfb32bb1) . FORMAT yfbc32b1(f1.0). EXECUTE . RECODE yfbc32b1 (2=1) (1=0) (0=0) INTO zfbc32b1 . VARIABLE LABELS zfbc32b1 'involvement risk - both pars'. FORMAT zfbc32b1(f1.0). EXECUTE . COMPUTE yfbc33b1 = SUM(xfb33ab1, xfb33bb1) . FORMAT yfbc33b1(f1.0). EXECUTE . RECODE yfbc33b1 (2=1) (1=0) (0=0) INTO zfbc33b1 . VARIABLE LABELS zfbc33b1 'praise risk - both pars'. FORMAT zfbc33b1(f1.0). EXECUTE . COMPUTE yfbc34b1 = SUM(xfb34ab1, xfb34bb1) . FORMAT yfbc34b1(f1.0). EXECUTE . RECODE yfbc34b1 (2=1) (1=0) (0=0) INTO zfbc34b1 . VARIABLE LABELS zfbc34b1 'monitoring risk - both pars'. FORMAT zfbc34b1(f1.0). EXECUTE . COMPUTE yfbc35b1 = SUM(xfb35ab1, xfb35bb1) . FORMAT yfbc35b1(f1.0). EXECUTE . RECODE yfbc35b1 (2=1) (1=1) (0=0) INTO zfbc35b1 . VARIABLE LABELS zfbc35b1 'mood influencing discipline risk- both pars'. FORMAT zfbc35b1(f1.0). EXECUTE . COMPUTE yfbc37b1 = SUM(xfb37ab1, xfb37bb1) . FORMAT yfbc37b1(f1.0).

Appendix G 315

EXECUTE . RECODE yfbc37b1 (2=1) (1=1) (0=0) INTO zfbc37b1 . VARIABLE LABELS zfbc37b1 'rule setting risk - both pars'. FORMAT zfbc37b1(f1.0). EXECUTE . COMPUTE yfbc38b1 = SUM(xfb38ab1, xfb38bb1) . FORMAT yfbc38b1(f1.0). EXECUTE . RECODE yfbc38b1 (2=1) (1=1) (0=0) INTO zfbc38b1 . VARIABLE LABELS zfbc38b1 'yelling at child risk - both pars'. FORMAT zfbc38b1(f1.0). EXECUTE . COMPUTE yfbc39b1 = SUM(xfb39ab1, xfb39bb1) . FORMAT yfbc39b1(f1.0). EXECUTE . RECODE yfbc39b1 (2=1) (1=1) (0=0) INTO zfbc39b1 . VARIABLE LABELS zfbc39b1 'physical punishment risk - both pars'. FORMAT zfbc39b1(f1.0). EXECUTE . COMPUTE yfbc40b1 = SUM(xfb40ab1, xfb40bb1) . FORMAT yfbc40b1(f1.0). EXECUTE . RECODE yfbc40b1 (2=1) (1=1) (0=0) INTO zfbc40b1 . VARIABLE LABELS zfbc40b1 'belting risk - both pars'. FORMAT zfbc40b1(f1.0). EXECUTE . COMPUTE yfbc41b1 = SUM(xfb41ab1, xfb41bb1) . FORMAT yfbc41b1(f1.0). EXECUTE. RECODE yfbc41b1 (2=1) (1=1) (0=0) INTO zfbc41b1 . VARIABLE LABELS zfbc41b1 'unemployed last yr risk - both pars'. FORMAT zfbc41b1(f1.0). EXECUTE . COMPUTE yfbc42b1 = SUM(xfb42ab1, xfb42bb1) . FORMAT yfbc42b1(f1.0). EXECUTE . RECODE yfbc42b1 (2=1) (1=1) (0=0) INTO zfbc42b1 . VARIABLE LABELS zfbc42b1 'prison last year risk - both pars'.

Appendix G 316

FORMAT zfbc42b1(f1.0). EXECUTE . *Value labels for final dichotomised (X and Z) variables. value labels xfbc8b1 1 '4 or more children' 0 '1-3 children' /xfbc9ab1 1 'one parent family' 0 'two-parent family' /xfbc9bb1 1 'stepfamily/other' 0 'original two-parent family' /xfbc10b1 1 'yes' 0 'no' /xfbc11b1 1 'yes' 0 'no' /xfbc12b1 1 'yes' 0 'no' /zfb131b1 1 'either parent unemployed' 0 'both parents employed' /zfbc14b1 1 'either parent low education' 0 'both parents high education' /xfbc15b1 1 'low income' 0 'high income' /xfbc16b1 1 'frequent house moves' 0 'infrequent house moves' /xfbc17b1 1 'multiple changes of parent figures' 0 'few changes parent figure' /xfbc18b1 1 'yes' 0 'no' /xfbc19b1 1 'no' 0 'yes' /xfbc20b1 1 'some verbal conflict' 0 'no verbal conflict' /xfbc21b1 1 'some physical conflict' 0 'no physical conflict' /xfbc22b1 1 'unhappy' 0 'happy' /zfbc23b1 1 'either parent often stressed' 0 'neither parent often stressed' /zfbc24b1 1 'either parent some depression/anxiety' 0 'no depression/anxiety' /zfbc25b1 1 'either parent some criminal offences' 0 'no criminal offences' /zfbc26b1 1 'frequent alcohol consumption' 0 'infrequent alcohol consumption' /zfbc28b1 1 'either parent using illicit drugs' 0 'no illicit drug use' /zfbc29b1 1 'either parent suffered serious mh problem' 0 'no serious mh problem' /zfbc30b1 1 'either parent medical condition' 0 'no medical condition' /zfbc31b1 1 'neither parent warm relationship' 0 'at least one parent warm relationship' /zfbc32b1 1 'neither parent frequently involved' 0 'at least one parent frequently involved' /zfbc33b1 1 'neither parent praises often' 0 'at least one parent praises often' /zfbc34b1 1 'neither parent monitors closely' 0 'at least one parent monitors closely' /zfbc35b1 1 'either parent mood frequently influences discipline' 0 'neither parent mood frequently influences discipline' /xfbc36b1 1 'frequently disagree' 0 'frequently agree' /zfbc37b1 1 'either parent fails to set and enforce rules' 0 'both parents set and enforce rules' /zfbc38b1 1 'either parent often yells' 0 'neither parent often yells' /zfbc39b1 1 'either parent often physically punish' 0 'neither parent physically punishes often' /zfbc40b1 1 'either parent belts' 0 'no belting' /zfbc41b1 1 'either parent became unemployed in last year' 0 'neither parent became unemployed in last year' /zfbc42b1 1 'either parent in prison in last year' 0 'neither parent in prison in last yr ‘ /xfbc43b1 1 'yes' 0 'no' /xfbc44b1 1 'yes' 0 'no' /xfbc45b1 1 'yes' 0 'no' /xfbc46b1 1 'yes' 0 'no' /xfbc47b1 1 'yes' 0 'no' /xfbc48b1 1 'yes' 0 'no'. *Checking the recoded variables; a Y variable should be calculated even for single parent families who have no risk scores for partner variables.

Appendix G 317

list variables = chid fbc9ab1 fbc31ab1 fbc31bb1 xfb31ab1 xfb31bb1 yfbc31b1 zfbc31b1. list variables = chid fbc9ab1 fbc23ab1 fbc23bb1 xfb23ab1 xfb23bb1 yfbc23b1 zfbc23b1. Step Four: Dichotomise Item 13(a)2 and 13(b)2 recode fb13a2b1 (77 = 77) (100 thru 199 = 1) (200 thru 299 = 2) (300 thru 399 = 3) (400 thru 499 = 4) (500 thru 599 = 5) (600 thru 699 = 6) (700 thru 799 = 7) (800 thru 899 = 8) (900 thru 999 = 9) into f13a2mb1. variable labels f13a2mb1 'occupation-major groups'. value labels f13a2mb1 1 'Managers and administrators' 2 'Professionals' 3 'Associate professionals' 4 'Tradespersons and related workers' 5 'Advanced clerical and service workers' 6 'Intermediate clerical sales and service workers' 7 'Intermediate production and transport workers' 8 'Elementary clerical sales and service workers' 9 'Labourers and related workers' 77 'homeduties, student, or unemployed'. format f13a2mb1 (f2.0). execute. recode fb13b2b1 (77 = 77) (100 thru 199 = 1) (200 thru 299 = 2) (300 thru 399 = 3) (400 thru 499 = 4) (500 thru 599 = 5) (600 thru 699 = 6) (700 thru 799 = 7) (800 thru 899 = 8) (900 thru 999 = 9) into f13b2mb1. variable labels f13b2mb1 'partner occupation-major groups'. value labels f13b2mb1 1 'Managers and administrators' 2 'Professionals' 3 'Associate professionals' 4 'Tradespersons and related workers' 5 'Advanced clerical and service workers' 6 'Intermediate clerical sales and service workers' 7 'Intermediate production and transport workers' 8 'Elementary clerical sales and service workers' 9 'Labourers and related workers' 77 'homeduties, student, or unemployed'. format f13b2mb1 (f2.0). execute. list variables = chid fb13a2b1 f13a2mb1 fb13b2b1 f13b2mb1. *The following recodes of Item 13a21 and 13b21 were conducted so that the final Z variable would be coded even for families where neither parent was working. This was done so that the Q 13 occupation variable could be included in subsequent analyses without contributing to a great loss of cases (due to listwise deletion). RECODE f13a2mb1 (lo thru 6=0) (7 thru 9 =1) (77 = 77) INTO x13a2mb1 . VARIABLE LABELS x13a2mb1 'parent-occupation'. FORMAT x13a2mb1(f2.0). EXECUTE . RECODE f13b2mb1 (lo thru 6=0) (7 thru 9 =1) (77 = 77) INTO x13b2mb1. VARIABLE LABELS x13b2mb1 'partner-occupation'. FORMAT x13b2mb1(f2.0). EXECUTE .

Appendix G 318

DO IF (x13a2mb1 ne 77 & x13b2mb1 ne 77). COMPUTE yfb132b1 = SUM(x13a2mb1, x13b2mb1). ELSE IF (x13a2mb1 = 77 & x13b2mb1 = 77). COMPUTE yfb132b1 = 1. END IF. EXECUTE . RECODE yfb132b1 (2=1) (1=1) (0=0) INTO zfb132b1. VARIABLE LABELS zfb132b1 'occupation risk - both pars'. VALUE LABELS zfb132b1 0 'both pars high ASCO occup or 1 high and 1 not working' 1 'at least 1 par low ASCO occup or neither par working'. FORMAT zfb132b1(f1.0). EXECUTE. list variables = chid f13a2mb1 f13b2mb1 x13a2mb1 x13b2mb1 yfb132b1 zfb132b1. *NB: If the above 'DO IF' procedure does not work, then use the following syntax instead to create final dichotomous variables for item 13a2 and 13b2. *NB: '77' must NOT be defined as missing for the following compute statement. COMPUTE yfb132b1 = (x13a2mb1 + x13b2mb1). FORMAT yfb132b1(f3.0). EXECUTE. RECODE yfb132b1 (1=1) (2=1) (78 = 1) (154 = 1) (77=0) (0=0) INTO zfb132b1. VARIABLE LABELS zfb132b1 'occupation risk - both pars'. VALUE LABELS zfb132b1 0 'both pars high ASCO occup or 1 high and 1 not working' 1 'at least 1 par low ASCO occup or neither par working'. FORMAT zfb132b1(f1.0). EXECUTE. list variables = chid f13a2mb1 f13b2mb1 x13a2mb1 x13b2mb1 yfb132b1 zfb132b1. Step Five: Dichotomise Item 27(a) and 27(b) ***Computing units of alcohol. *NB: This was done after all Time 1 data had been matched, so all variables end only in '1' - not 'b1'. list variables = chid fbc9a1 fb27a11 fb27a21 fb27a31 fb27a41 fb27a51 fb27a61 fb27a71 fb27a81 fb27a91 f27a101 f27a111 f27a121 f27a131. list variables = chid fbc9a1 fb27b11 fb27b21 fb27b31 fb27b41 fb27b51 fb27b61 fb27b71 fb27b81 fb27b91 f27b101 f27b111 f27b121 f27b131. **Parent. *Beer. COMPUTE uf27a11 = fb27a11 * 200 * 5 / 1000 . VARIABLE LABELS uf27a11 'parent-glasses of beer to units per wk' . FORMAT uf27a11(f4.1). EXECUTE .

Appendix G 319

COMPUTE uf27a21 = fb27a21 * 375 * 5 / 1000 . VARIABLE LABELS uf27a21 'parent-lge cans of beer to units per wk' . FORMAT uf27a21(f4.1). EXECUTE . COMPUTE uf27a31 = fb27a31 * 750 * 5 / 1000 . VARIABLE LABELS uf27a31 'parent-bottles of beer to units per wk' . FORMAT uf27a31(f4.1). EXECUTE . COMPUTE uf27a41 = fb27a41 * 1140 * 5 / 1000 . VARIABLE LABELS uf27a41 'parent-jugs of beer to units per wk' . FORMAT uf27a41(f4.1). EXECUTE . COMPUTE uf27a51 = fb27a51 * 2000 * 5 / 1000 . VARIABLE LABELS uf27a51 'parent-flagons of beer to units per wk' . FORMAT uf27a51(f4.1). EXECUTE . *Wine. COMPUTE uf27a61 = fb27a61 * 200 * 11 / 1000 . VARIABLE LABELS uf27a61 'parent-glasses of wine to units per wk' . FORMAT uf27a61(f4.1). EXECUTE . COMPUTE uf27a71 = fb27a71 * 750 * 11 / 1000 . VARIABLE LABELS uf27a71 'parent-bottles of wine to units per wk' . FORMAT uf27a71(f4.1). EXECUTE . *Spirits. COMPUTE uf27a81 = fb27a81 * 30 * 37 / 1000 . VARIABLE LABELS uf27a81 'parent-nips of spirits to units per wk' . FORMAT uf27a81(f4.1). EXECUTE . COMPUTE uf27a91 = fb27a91 * 375 * 37 / 1000 . VARIABLE LABELS uf27a91 'parent-sm bottles of spirits to units per wk' . FORMAT uf27a91(f4.1). EXECUTE . COMPUTE u27a101 = f27a101 * 750 * 37 / 1000 . VARIABLE LABELS u27a101 'parent-lge bottles of spirits to units per wk' . FORMAT u27a101(f4.1). EXECUTE . *Sherry. COMPUTE u27a111 = f27a111 * 140 * 17 / 1000 . VARIABLE LABELS u27a111 'parent-glasses of sherry to units per wk' . FORMAT u27a111(f4.1). EXECUTE . COMPUTE u27a121 = f27a121 * 750 * 17 / 1000 . VARIABLE LABELS u27a121 'parent-bottles of sherry to units per wk' .

Appendix G 320

FORMAT u27a121(f4.1). EXECUTE . *Cocktails - N.B. Assuming at least two nips per cocktail. COMPUTE u27a131 = f27a131 * 60 * 55 / 1000 . VARIABLE LABELS u27a131 'parent-glasses of cocktails to units per wk' . FORMAT u27a131(f4.1). EXECUTE . **Partner. COMPUTE uf27b11 = fb27b11 * 200 * 5 / 1000 . VARIABLE LABELS uf27b11 'partner-glasses of beer to units per wk' . FORMAT uf27b11(f4.1). EXECUTE . COMPUTE uf27b21 = fb27b21 * 375 * 5 / 1000 . VARIABLE LABELS uf27b21 'partner-lge cans of beer to units per wk' . FORMAT uf27b21(f4.1). EXECUTE . COMPUTE uf27b31 = fb27b31 * 750 * 5 / 1000 . VARIABLE LABELS uf27b31 'partner-bottles of beer to units per wk' . FORMAT uf27b31(f4.1). EXECUTE . COMPUTE uf27b41 = fb27b41 * 1140 * 5 / 1000 . VARIABLE LABELS uf27b41 'partner-jugs of beer to units per wk' . FORMAT uf27b41(f4.1). EXECUTE . COMPUTE uf27b51 = fb27b51 * 2000 * 5 / 1000 . VARIABLE LABELS uf27b51 'partner-flagons of beer to units per wk' . FORMAT uf27b51(f4.1). EXECUTE . *Wine. COMPUTE uf27b61 = fb27b61 * 200 * 11 / 1000 . VARIABLE LABELS uf27b61 'partner-glasses of wine to units per wk' . FORMAT uf27b61(f4.1). EXECUTE . COMPUTE uf27b71 = fb27b71 * 750 * 11 / 1000 . VARIABLE LABELS uf27b71 'partner-bottles of wine to units per wk' . FORMAT uf27b71(f4.1). EXECUTE . *Spirits. COMPUTE uf27b81 = fb27b81 * 30 * 37 / 1000 . VARIABLE LABELS uf27b81 'partner-nips of spirits to units per wk' . FORMAT uf27b81(f4.1). EXECUTE . COMPUTE uf27b91 = fb27b91 * 375 * 37 / 1000 . VARIABLE LABELS uf27b91 'partner-sm bottles of spirits to units per wk' .

Appendix G 321

FORMAT uf27b91(f4.1). EXECUTE . COMPUTE u27b101 = f27b101 * 750 * 37 / 1000 . VARIABLE LABELS u27b101 'partner-lge bottles of spirits to units per wk' . FORMAT u27b101(f4.1). EXECUTE . *Sherry. COMPUTE u27b111 = f27b111 * 140 * 17 / 1000 . VARIABLE LABELS u27b111 'partner-glasses of sherry to units per wk' . FORMAT u27b111(f4.1). EXECUTE . COMPUTE u27b121 = f27b121 * 750 * 17 / 1000 . VARIABLE LABELS u27b121 'partner-bottles of sherry to units per wk' . FORMAT u27b121(f4.1). EXECUTE . *Cocktails - N.B. Assuming at least two nips per cocktail. COMPUTE u27b131 = f27b131 * 60 * 55 / 1000 . VARIABLE LABELS u27b131 'partner-glasses of cocktails to units per wk' . FORMAT u27b131(f4.1). EXECUTE . list variables = chid fbc9a1 fb27a11 uf27a11 fb27a21 uf27a21 fb27a31 uf27a31 fb27b11 uf27b11 fb27b21 uf27b21 fb27b31 uf27b31 . list variables = chid fb27a61 uf27a61 fb27b61 uf27b61. *Computing total units of alcohol per week. COMPUTE uwk27a1 = SUM(uf27a11 to u27a131) . VARIABLE LABELS uwk27a1 'parent-total units alcohol per wk'. FORMAT uwk27a1(f5.1). EXECUTE . COMPUTE uwk27b1 = SUM(uf27b11 to u27b131) . VARIABLE LABELS uwk27b1 'partner-total units alcohol per wk'. FORMAT uwk27b1(f5.1). EXECUTE . list variables = chid uf27a11 uf27a21 uf27a31 uf27a41 uf27a51 uf27a61 uf27a71 uf27a81 uf27a91 u27a101 u27a111 u27a121 u27a131 uwk27a1. list variables = chid uf27b11 uf27b21 uf27b31 uf27b41 uf27b51 uf27b61 uf27b71 uf27b81 uf27b91 u27b101 u27b111 u27b121 u27b131 uwk27b1. **Converting to risk absent or risk present for alcohol Qs (N.B. high risk drinking = 4 or more units alcohol per day for women (28 units or more per week) and 5 or more units per day (35 units or more per week) for men). *N.B If fbc31 = 1, then 27a is female and 27b is male, but if fbc31 = 2, then 27a is male and 27b is female. *Parent DO IF (fbc31 = 1) . RECODE uwk27a1 (lo thru 28 = 0) (28 thru hi = 1) INTO xuf27a1. DO IF (fbc31 = 2) .

Appendix G 322

RECODE uwk27a1 (lo thru 35 = 0) (35 thru hi = 1) INTO xuf27a1. END IF . VARIABLE LABELS xuf27a1 'parent-alcohol probs'. VALUE LABELS xuf27a1 0 'no' 1 'yes'. FORMAT xuf27a1(f1.0). EXECUTE . *Partner DO IF (fbc31 = 1) . RECODE uwk27b1 (lo thru 35 = 0) (35 thru hi = 1) INTO xuf27b1. DO IF (fbc31 = 2) . RECODE uwk27b1 (lo thru 28 = 0) (28 thru hi = 1) INTO xuf27b1. END IF . VARIABLE LABELS xuf27b1 'partner-alcohol probs'. VALUE LABELS xuf27b1 0 'no' 1 'yes'. FORMAT xuf27b1(f1.0). EXECUTE . list variables = chid fbc31 uwk27a1 xuf27a1 uwk27b1 xuf27b1. *Combining parent and partner score and recoding back into two levels of risk. COMPUTE yufb271 = SUM(xuf27a1, xuf27b1). FORMAT yufb271(f1.0). EXECUTE. RECODE yufb271 (0=0) (1=1) (2=1) INTO zufb271. VARIABLE LABELS zufb271 'alcohol risk-both pars'. VALUE LABELS zufb271 1 'either parent alcohol problem' 0 'no alcohol problem'. FORMAT zufb271(f1.0). EXECUTE. list variables = chid xuf27a1 xuf27b1 yufb271 zufb271. Step Six: Recode Items 31-34 for Single Parent Families *For single parent families, the combined Z risk score should show risk present for items 31-34 if the single parent scores risk on these items (even though for two-parent families both parents need to score risk present for the final Z score to be scored risk present). DO IF (fbc9ab1 = 1) . RECODE yfbc31b1 (1=1) (0=0) INTO zfbc31b1. END IF . VARIABLE LABELS zfbc31b1 'warmth risk - both pars'. EXECUTE . DO IF (fbc9ab1 = 1) . RECODE yfbc32b1 (1=1) (0=0) INTO zfbc32b1. END IF . VARIABLE LABELS zfbc32b1 'involvement risk - both pars'.

Appendix G 323

EXECUTE . DO IF (fbc9ab1 = 1) . RECODE yfbc33b1 (1=1) (0=0) INTO zfbc33b1. END IF . VARIABLE LABELS zfbc33b1 'praise risk - both pars'. EXECUTE . DO IF (fbc9ab1 = 1) . RECODE yfbc34b1 (1=1) (0=0) INTO zfbc34b1. END IF . VARIABLE LABELS zfbc34b1 'monitoring risk - both pars'. EXECUTE . list variables = chid fbc9ab1 fbc31ab1 fbc31bb1 xfb31ab1 xfb31bb1 yfbc31b1 fbc31b1. Step Seven: Compute Total Risk Score *NB: This was done after all Time 1 data had been matched, so all variables end only in '1' - not 'b1'. COMPUTE prisk1 = SUM(xfbc81, xfbc9a1, xfbc9b1, xfbc101 to xfbc121, zfb1311, zfb1321, zfbc141, xfbc151 to xfbc221, zfbc231, zfbc241, zfbc251, zfbc261, zufb271, zfbc281, zfbc291, zfbc301, zfbc311, zfbc321, zfbc331, zfbc341, zfbc351, xfbc361, zfbc371, zfbc381, zfbc391, zfbc401, zfbc411, zfbc421, xfbc431 to xfbc481) . VARIABLE LABELS prisk1 'total parent risk score' . FORMAT prisk1 (f2.0). EXECUTE . list variables = chid xfbc81 xfbc9a1 xfbc9b1 xfbc101 to xfbc121 zfb1311 zfb1321 zfbc141 xfbc151 to xfbc221 zfbc231 zfbc241 zfbc251 zfbc261 zufb271 zfbc281 zfbc291 zfbc301 zfbc311 zfbc321 zfbc331 zfbc341 zfbc351 xfbc361 zfbc371 zfbc381 zfbc391 zfbc401 zfbc411 zfbc421 xfbc431 to xfbc481 prisk1. Step Eight: Recode Total Risk Score into a Categorical Variable to Produce Low, Medium, and High Risk Groups RECODE prisk1 (lo thru 6 = 1) (7 thru 12 = 2) (13 thru hi = 3) INTO zsrisk1. VARIABLE LABELS zsrisk1 'level of risk(4)'. VALUE LABELS zsrisk1 1 'low risk = 0-6 risk factors' 2 'medium risk = 7-12 risk factors' 3 'high risk = 13 or more risk factors'. FORMAT zsrisk1(f1.0). EXECUTE. list variables = chid prisk1 zsrisk1. FREQUENCIES VARIABLES= prisk1 zsrisk1 /HISTOGRAM NORMAL.

Appendix G 324

Step Nine: Compute Domain Risk Scores *NB: This was done after all Time 1 data had been matched, so all variables end only in '1' - not 'b1'. COMPUTE mood21 = sum(xfbc201, xfbc221, zfbc231, zfbc241, zfbc351). VARIABLE LABELS mood21 'parental verbal conflict & mood probs'. FORMAT mood21(f1.0). EXECUTE . list variables = chid xfbc201 xfbc221 zfbc231 zfbc241 zfbc351 mood21. COMPUTE parent1 = sum(zfbc311, zfbc321, zfbc331, xfbc361, zfbc371, zfbc381, zfbc391, zfbc401) . VARIABLE LABELS parent1 'parenting practices (PAR)' . FORMAT parent1(f1.0). EXECUTE . COMPUTE antiso21 = sum(xfbc211, zfbc251, zfbc281, zfbc261, zufb271, zfbc291, zfbc421) . VARIABLE LABELS antiso21 'parental antisocial & psychotic beh (APB)'. FORMAT antiso21(f1.0). EXECUTE . list variables = chid xfbc211 zfbc251 zfbc281 zfbc261 zufb271 zfbc291 zfbc421 antiso21. COMPUTE ses1 = sum(xfbc81, xfbc9a1, xfbc9b1, xfbc101, xfbc111, xfbc121, zfb1311, zfb1321, zfbc141, xfbc151, xfbc191, zfbc301, zfbc411) . VARIABLE LABELS ses1 'family structure & ses (SES)'. FORMAT ses1(f1.0). EXECUTE . list variables = chid xfbc81 xfbc9a1 xfbc9b1 xfbc101 xfbc111 xfbc121 zfb1311 zfb1321 zfbc141 xfbc151 xfbc191 zfbc301 zfbc411 ses1. COMPUTE life1 = sum(xfbc161, xfbc171, xfbc181, xfbc431, xfbc441, xfbc451, xfbc461, xfbc471) . VARIABLE LABELS life1 'adverse life events & instability (ALI)'. FORMAT life1(f1.0). EXECUTE . FREQUENCIES VARIABLES=mood21 parent1 antiso21 ses1 life1 /PERCENTILES = 33 67 /STATISTICS=STDDEV MINIMUM MAXIMUM MEAN MEDIAN SKEWNESS SESKEW KURTOSIS SEKURT /HISTOGRAM /ORDER ANALYSIS . Step Ten: Recode Domain Risk Scores into Categorical Variables Recode mood21 (0 = 1) (1, 2 = 2) (3 thru hi = 3) into moodcat1. Variable labels moodcat1 'categorical parental verbal conflict & mood probs'.

Appendix G 325

Value labels moodcat1 1 'low' 2 'medium' 3 'high'. Format moodcat1 (f1.0). Execute. list variables = chid mood21 moodcat1. Recode parent1 (0=1) (1, 2 = 2) (3 thru hi = 3) into parcat1. Variable labels parcat1 'categorical parenting practices'. Value labels parcat1 1 'low' 2 'medium' 3 'high'. Format parcat1 (f1.0). Execute. list variables = chid parent1 parcat1. Recode antiso21 (0=1) (1, 2 = 2) (3 thru hi = 3) into anticat1. Variable labels anticat1 'categorical parental antisocial & psychotic beh'. Value labels anticat1 1 'low' 2 'medium' 3 'high'. Format anticat1 (f1.0). Execute. list variables = chid antiso21 anticat1. Recode ses1 (0, 1, 2 =1) (3, 4 = 2) (5 thru hi = 3) into sescat1. Variable labels sescat1 'categorical family structure & ses'. Value labels sescat1 1 'low risk' 2 'medium risk' 3 'high risk'. Format sescat1 (f1.0). Execute. list variables = chid ses1 sescat1. Recode life1 (0=1) (1, 2 = 2) (3 thru hi = 3) into lifecat1. Variable labels lifecat1 'categorical adverse life events & instability'. Value labels lifecat1 1 'low' 2 'medium' 3 'high'. Format lifecat1 (f1.0). Execute. list variables = chid life1 lifecat1. *Looking at new variables. FREQUENCIES VARIABLES= moodcat1 parcat1 anticat1 sescat1 lifecat1 /STATISTICS=STDDEV VARIANCE MINIMUM MAXIMUM MEAN MEDIAN MODE SKEWNESS SESKEW KURTOSIS SEKURT /HISTOGRAM NORMAL /ORDER= ANALYSIS .

Appendix H 326

APPENDIX H - SCHOOL RECRUITMENT PACKAGE

Appendix H 327

THE PROMOTING

ADJUSTMENT IN SCHOOLS PROJECT

(PROMAS)

Appendix H 328

PROMAS GOALS Overview The PROMAS Project is a study examining the types of problems that children and families experience. It aims to help schools to provide services to children and families in need. Schools are an important part of modern life. Most children spend large amounts of time at school. Schools also have ongoing contact with parents and the broader community. This means that teachers, principals and others may be able to identify children or families who are having problems, and provide assistance in times of need. In particular, school personnel may be well placed to promote positive adjustment in students, and to help children and families to access appropriate help for problems that are developing. This research is concerned with assessing the adjustment and wellbeing of a random sample of Queensland preschool and primary school children. It will identify the types of changes and problems that occur in families, that may affect child adjustment. Finally, the research seeks your opinions regarding the types of services that schools could provide to support children and families in your school community. Goals of the Research This project has four goals: 1. To assess the wellbeing and adjustment of approximately 1000 Queensland

preschool and primary school children; 2. To identify the characteristics and problems experienced by the families of these

children; 3. To help teachers at participating schools to improve their ability to identify and

respond to children who are at risk of developing mental health problems; and 4. To help participating schools to develop effective mental health intervention and

prevention strategies for helping children and families in need.

Appendix H 329

TIMELINE OF ACTIVITIES

Short-term Plan Phase One data collection will commence in May, 1999 and continue until the end of August, 1999. The same procedure will commence again for the second and third data collection points in May 2000 and May 2001. Week

Parents

Teachers

Start (MAY)

Initial mailout to 1500 parents (60 per school)

1

2 Reminder/Thankyou letters sent out

3

First wave of assessment packages given out

4 Replacement surveys sent out

5

6 Additional Questions sent out to 50% of parents who returned completed questionnaires

Second wave of assessment packages given out

7 Final thankyou letters for returning the first assessment package sent out

8 Reminder/Thankyou letters sent out for Additional Questions

Reminder/Thankyou letters sent out

9

10 Replacement surveys for Additional Questions sent out

Replacement surveys sent out

11

12 (AUG)

Final thankyou letters for returning assessment packages sent out

Appendix H 330

Long-term Plan

• First data collection (baseline measurement) - Surveys to parents - Surveys to teachers

• Data analysis of first data collection

• Second data collection (baseline measurement) • Data analysis of second data collection

• Third data collection (baseline measurement) • Data analysis of third data collection

1999

2000

2001

YEAR

Semester 1: Teacher-focused IVN* Semester 2: Post-assessment for teacher IVN • Pre-assessment for MH* promotion IVN • Delivery of MH promotion IVN

Semester 1: No IVN or measurement Semester 2: Fourth data collection period

Semester 1: Continue MH promotion IVN Semester 2: Maintenance phase of IVN • Post-assessment for MH promotion IVN

Semester 1: No IVN or measurement Semester 2: Fifth data collection period

Semester 1: No IVN or measurement Semester 2: 12 month follow-up for MH IVN

Semester 1: No IVN or measurement Semester 2: Sixth data collection period

ALL SCHOOLS

INTERVENTION SCHOOLS † CONTROL SCHOOLS

Random allocation to intervention or control

2002

2003

2004

* Note: IVN = Intervention MH = Mental Health † Note: Phase 2 subject to funding

Appendix H 331

MEASUREMENT OVERVIEW PHASE ONE Questionnaire data will be collected from the teachers and parents of 1500 children across 25 schools at three time points - in first semester 1999, 2000, and 2001. Children will be randomly selected. That is, we will not be selecting only children with adjustment problems. Assessment Package for Parents Parents will complete three questionnaires, similar to the teacher questionnaires: • Family Risk Factors Checklist (FRFC) - about their child’s family background. • Child Behaviour Checklist (CBCL) - about their child’s behavioural or emotional

problems. • Intervention Option Survey (IOS) - about their opinions on the usefulness of different

school-based mental health interventions. • Some parents will also receive an extra series of questions (Additional Questions),

which examine some areas of family relationships or adult adjustment in greater detail. These Additional Questions are being sent, at random, to five in every ten participants.

Assessment packages will be distributed to teachers after gaining consent from parents. Assessment Package for Teachers The study will require teachers to complete a one page form (listing their personal details) and three questionnaires: • Family Risk Factors Checklist (FRFC) - about their knowledge of the child’s family

background. • Child Behaviour Checklist (CBCL) - about their observations of the child’s

behavioural or emotional problems. • Intervention Option Survey (IOS) - about teachers’ opinions on the usefulness of

different school-based mental health interventions. Teachers will receive separate packages for each child in their class participating in the project. PHASE TWO As well as continuing the yearly data collection from teachers and parents, additional measures will be used to evaluate the effectiveness of the school-based interventions. Key outcomes will be: (1) child mental health problems, (2) mental health risk factors, (3) school-wide mental health promotion activities, and (4) consumer satisfaction.

Appendix H 332

PROMAS RESEARCH TEAM

Center for Health Promotion Research

and Development University of Texas

Health Science Center TEXAS

Centre for Public Health Research

Queensland University of Technology

QUEENSLAND

Centre for Adolescent Health

University of Melbourne VICTORIA

Professor Guy Parcel

Director of the Center for Health Promotion Research and Development

Dr Jan Nicholson Research Fellow and Psychologist Centre for Public Health Research

Professor George Patton Director of the Centre for

Adolescent Health

Dr Susan Tortolero

Epidemiologist

Professor Brian Oldenburg Head of School

School of Public Health

Ms Sara Glover Research Coordinator of The Gatehouse Project

Ms Sarah Dwyer PhD student and Psychologist

Centre for Public Health Research

Ms Diana Battistutta

Biostatistician Faculty of Health

PROMAS Advisory Committee

Representatives from Education Queensland, Queensland Health, school principals, teachers, guidance officers, mental health professionals, and parents

Appendix H 333

Centre for Public Health Research Queensland University of Technology Dr Jan Nicholson is an NH&MRC post-doctoral fellow in the Centre for Public Health Research. Her research interests include identifying family risk factors for children’s mental health and the development and evaluation of family-focussed interventions. She has conducted qualitative research (funded by the Australian Rotary Health Research Foundation) to examine current mental health problems and intervention strategies in SE Queensland primary schools. Her publications include child mental health epidemiology, behavioural family interventions and mental health in stepfamilies.

Professor Brian Oldenburg is Head of School of Public Health at QUT. He has a long and distinguished career in public health, with an international reputation for work in a variety of fields including trials of health promotion interventions in workplace and community settings. He has extensive research experience with current interests concerning the evaluation of ecologically appropriate health promotion interventions, and strategies for the dissemination of innovations. He has published widely on a diverse range of public health topics.

Ms Sarah Dwyer is a registered Psychologist with considerable experience conducting individual and group parenting programs through the Parenting and Family Support Centre. Client groups include parents of children with conduct problems, attention deficit disorder, anxiety problems, and stepfamily couples. She was editorial assistant for “Healthy Families, Healthy Nation”, a report commissioned by the state government on strategies for improving family mental health in Australia.

Ms Diana Battistutta is the biostatistician on the project and is based in the Faculty of Health, QUT. She has worked in health statistics since the early 1980’s on a variety of medical and epidemiological studies, including clinical and community intervention trials. By the very nature of her work, she has published on a wide range of topics, including the health of Vietnam veterans, women’s health, and the health of young people. Centre for Adolescent Health The University of Melbourne Professor George Patton is Director of the Centre for Adolescent Health and Professor in Adolescent Psychiatry at The University of Melbourne. He has a strong background in the epidemiology and treatment of common mental health problems in adolescents, including depression, deliberate self-harm, eating disorders and substance abuse. He is a member on the Ministerial Working Party on Youth Suicide in Victoria and a member of the Health Promoting Schools group auspiced by the Victorian Health Promotion Foundation. Dr Patton is currently principle investigator on the Gatehouse Project, a school-based mental health prevention program involving in excess of 3,500 students in 27 Victorian High Schools.

Ms Sara Glover is the Research Coordinator of the Gatehouse Project. She has a teaching background and considerable experience in curriculum development including writing the National Health and Physical Education Statement and Profile for Australian Schools. She has undertaken a number of consultancies and projects for the Victorian Board of Studies, Victorian Curriculum and Assessment Board, and other education bodies. Sara also has extensive experience in designing, implementing, and evaluating professional development programs at state and national levels. On Gatehouse, she has been responsible for developing the curriculum materials, liaising with schools and overseeing the management of the Project. Centre for Health Promotion Research and Development University of Texas Health Science Center at Houston, USA Professor Guy Parcel is the J.P. McGovern Professor in Health Promotion, and Director of the Center for Health Promotion Research and Development in the University of Texas at Houston. He has a considerable research history conducting health promotion interventions in school settings with a focus on diet, exercise, safe sex practices and drug use. He has also conducted family-based interventions for health promotion and the management of children’s chronic illnesses. Professor Parcel has visited Australia on a regular basis to promote joint research and teaching activities, and to facilitate institutional exchanges of faculty and students.

Dr Susan Tortolero is a paediatric public health epidemiologist, with considerable expertise in child mental health epidemiology, intervention design, and an understanding of the school as a site for health promotion. Her current research focuses on risk taking behaviours, substance use, and suicide among adolescents.

Appendix H 334

CONFIDENTIALITY AND CONSENT

All information provided to the project, including names and details of participating children, parents, and teachers, will remain confidential at all times. Participation in the project is voluntary and schools are free to withdraw from the study at any time without comment or penalty. However, only schools who are prepared to make a five year commitment to the project should agree to participate. If you are unsure about the aims of the research, or whether to participate, please contact Sarah Dwyer on 3864 5561 or Dr Jan Nicholson on 3864 3389. They will be able to answer any questions you may have. You may also contact the Secretary of the University Research Ethics Committee on 3864 2902 if you wish to raise any concerns about the conduct of this research.

Appendix H 335

INCENTIVES TO PARTICIPATE Schools • Each teacher will receive a $1 scratch-it ticket with their assessment package. • All schools will receive $50 for participating in the study. • All schools participating in the study will be entered into a draw to win a modern,

quality computer (donated by Source Technology). • By their participation, each school will contribute to the scientific understanding

of mental health risk factors and the effectiveness of school-based mental health interventions.

• Schools will receive information regarding the risk factor profile of the school

community, enabling better planning of preventive and intervention programs for schools.

• Schools will gain access to a new screening instrument to assist in accurately

identifying children at risk of developing mental health problems. • Schools will be assisted to enhance the means by which they respond to children

with behavioural and emotional problems.* • Schools will be helped to establish programs to prevent children’s mental health

problems.* • Schools will be assisted to develop and maintain effective links with key

community agencies and individuals.* Parents • Each parent will receive a $1 scratch-it ticket with their assessment package. • Each parent who returns completed questionnaires will be entered into a draw to

win a $200 Myer Voucher. • Parents may be assisted to contact appropriate services, thereby improving their

access to services. • Parents may receive earlier intervention for child or family problems, resulting in

reduced distress and fewer problems in the long-term.* *Note: Intervention schools only. Control schools will receive feedback on the prevalence of

different mental health risk factors. Successful aspects of the interventions will be introduced into the control schools in 2004.

Appendix H 336

SCHOOL REQUIREMENTS

All Schools 1. Sign a contract indicating intent to participate for the duration of the study,

May 1999 - December 2004. 2. Nominate a staff person to serve as a liaison for the project. 3. Send information about the project (supplied by QUT) home to parents on three occasions (one

week apart), including: (1) a flyer given to all preschool to grade 3 children to take home to parents, (2) a letter mailed to all parents directly from the school (cost paid by QUT), and (3) a paragraph about the project included in the school newsletter.

4. Provide the research team with a list of children enrolled from preschool to grade three, and their

gender. 5. Provide the research team with the parents’ names and addresses of the 60 children randomly

selected from preschool to grade 3 (method of provision to be negotiated with individual schools). 6. Provide the research team with the phone numbers of nonresponders if requested. 7. Agree to be randomly assigned after baseline data is collected to one of two experimental

conditions, control (measurement only) or intervention (measurement plus mental health intervention).

8. Allow data collection from parents and teachers for each year of the study.

Intervention Schools Only* 1. Participate in the teacher-focussed intervention and the mental health promotion interventions over

a period of two years, Semester One 2002 - Semester Two 2003. 2. Allow assessment and observation of the school-wide mental health promotion activities. 3. Provide feedback on the interventions. *Note: Interventions subject to funding

Appendix H 337

PROMAS PARTICIPATION CONTRACT

All Schools ________ This school will participate for the duration of the PROMAS Project, May 1999 - December 2004. ________ We agree to nominate a staff member to serve as a liaison for the project. ________ We agree to send information about the project (supplied by QUT) home to the parents of all preschool to grade 3 children on three occasions (one week apart). ________ We agree to provide the research team with a list of children enrolled from preschool to grade 3, and their gender. ________ We agree to provide the research team with the parents’ names and addresses of the 60 children randomly selected from preschool to grade 3. ________ We agree to be randomly assigned, after baseline data collection to the control group (measurement only) or intervention group (measurement plus mental health interventions). ________ We agree to allow data collection from parents and teachers for each year of the study.

Schools assigned to the intervention condition ________ The school agrees to participate in the teacher-focussed intervention and the mental health promotion interventions over a period of two years, Semester One 2002 - Semester Two 2003. ________ We agree to allow assessment and observation of the school-wide mental health promotion activities. ________ We agree to provide feedback on the project. As my initials above and signature below indicate, I have read and agree to the terms of this contract. As principal of _______________________________, I have the authority to sign this document. I understand the requirements of the study and this school is willing to participate in the PROMAS Project. I have filed a copy of this contract. _______________________________ _____________________________ (signature) (date) _______________________________ _____________________________ (print name) (PROMAS representative) Staff person that will serve as the PROMAS liaison:_____________________________

Please fax the completed Participation Contract to Sarah Dwyer (3864 3369) to indicate your desire to participate. The next 15 schools who reply will be included in the project.

Appendix H 338

FOR MORE INFORMATION

Dr Jan Nicholson Chief Investigator


Queensland University of Technology Victoria Park Rd

Kelvin Grove QLD 4059

Ph: (07) 3864 3389 Fax: (07) 3864 3369

E-Mail: [email protected]

Sarah Dwyer Project Investigator


Queensland University of Technology Victoria Park Rd

Kelvin Grove QLD 4059

Ph: (07) 3864 5561 Fax: (07) 3864 3369

E-mail: [email protected]

Appendix I 339

APPENDIX I - SCHOOL RECRUITMENT PRESENTATION

Appendix I 340

Verbal Recruitment Presentation for Teachers Prior to the presentations, secretaries were invited to attend the recruitment

meeting. This was to keep administrative staff informed of the small impact that the

project was likely to have on them. High quality overhead transparencies were used

for the presentations. The content of the recruitment presentations was as follows: (✤

indicates a new overhead transparency has been projected onto the screen).

“Thanks very much for letting Chris and myself come out to talk to you about

PROMAS today. PROMAS stands for ‘Promoting Adjustment in Schools’, so the

project is concerned with children’s mental health and preventing the development of

behavioural and emotional problems. What we’d like to do today is give you enough

information for you to decide if this is the sort of project that you would like to get

involved in. What I’ll do is give you some background to the project, describe the

two phases of the project to you, their aims, exactly what would be involved if the

school does decide to participate, and some of the benefits in participating in

PROMAS.

✤ Just to let you know who we are - both Chris and myself are based in the

School of Public Health, Queensland University of Technology, and this is where

most of the research is being conducted. The director of the project, Dr Jan Nicholson

is also based in the School of Public Health at QUT. Because it is such a large

project, we are working closely with several other research groups, who are having

ongoing input and feedback into the project. The Centre for Adolescent Health in

Melbourne is currently running a project called ‘Gatehouse’ which aims to prevent

adolescent suicide and depression. Another group we are collaborating with is the

Centre for Health Promotion Research and Development in Texas, USA, and they are

conducting the largest school-based health promotion study ever undertaken,

involving nearly 100 primary schools. This Centre is a very good role model for us

and is providing ongoing expert input into our project, to ensure that we are using the

most up-to-date strategies and techniques and so that we can ensure everything runs

as smoothly as possible for the schools participating in PROMAS. Another group that

is important to us is our Advisory Committee, which is made up of representatives

from Education Queensland, Queensland Health, Guidance Officers, Principals, and

Appendix I 341

teachers. Several times a year we meet with this group to get their input and feedback

on the methods we are using for each stage of the project.

✤ Just to let you know a bit more about the project, here are some of the

principles that the project is based on. We know that schools potentially play a key

role when it comes to promoting and protecting children’s mental health. Most

children go to schools and they spend a fair proportion of each day at school which

means there are real opportunities there to help prevent the development of

behavioural or emotional problems or to deal with problems that do arise as

effectively as possible. The second principle is that in order for us to know what

strategies will be most useful in the school environment to help support these

children, requires careful consultation with the schools involved. For example, we

need to find out what your priorities are, what the most common behavioural or

emotional problems are at your school, which strategies you think might be useful for

dealing with children with different sorts of problems. You are the people working

within the schools and are likely to know what sorts of strategies will and won’t work

within your school. For this reason, PROMAS is very much a staged project where

the first phase is all about finding out this information, and the second phase is then

using this information to introduce strategies that are individually tailored to meet the

needs of your school.

✤ Just to let you know where we came from, this research began with a group

of schools in the Gold Coast District in 1996. What we did was to run several focus

groups with teachers, Guidance Officers and Principals, and three key concerns

emerged from this earlier research. Firstly, teachers were concerned that the numbers

of children with some sort of adjustment problem are increasing. This is backed up

by statistics. We know that there are now more children with problems such as

depression, anxiety, or conduct disorder. Secondly, teachers were telling us that it is

the children from the highly disrupted family backgrounds that also have problems

learning and coping in the classroom - so not only are these children having problems

at home, but often they also present a real challenge in the classroom. Finally,

teachers expressed some concern at the time that they may be missing some of the

children who are at risk of developing a problem. They were fairly confident that

they could pick out the children who were disruptive and loud in the classroom, but

worried that they may be missing some of the quieter children, some of the children at

risk of developing either depression or anxiety types of problems - due to either a lack

Appendix I 342

of time (all their time being taken up with the noisier children), or the fact that many

children at risk of depression or anxiety do not express their problems in an obvious

way.

So from this earlier research, the aims of the current PROMAS Project have

evolved. ✤ These are the aims for the overall project - I’ll breakdown the aims for the

two separate phases of the project shortly. The first aim is to assess the schools’

capacity for identifying children at risk of developing behavioural or emotional

problems. This is important, because if we want to be able to prevent the

development of these problems, then we need to be able to identify the children who

are at risk of developing these problems, preferably before the problems develop. So

we want to find out how much teachers know about children’s family backgrounds,

and if they are able to say which children are at risk even before the children manifest

behavioural problems. The second and third aims revolve around both prevention and

treatment. We want to be able to maximise opportunities within the school to prevent

the development of children’s adjustment problems, but we also want to strengthen

ways in which schools currently respond to children who have already developed

problems.

In terms of what we have achieved so far, last year we piloted the three

measures we are using with one Brisbane State School and, subsequently, further

revised and refined these measures. We also recruited 10 schools to the project who

have completed their first data collection and are still participating in Phase 1 of the

project. And the reason we are here today is that, this year, we are recruiting a further

15 or so schools to begin participation in PROMAS this year. We ended up sending

out recruitment packages to all schools which had enrolment sizes between 200-700

children and which also had attached preschools.

Now, I’ll give you an overview of the two phases of the project and then move

on to what exactly would be involved if you decide to participate. ✤ These are the

aims specific to Phase 1. Phase 1 is concerned with assessing the schools’ capacity to

identify ‘at risk’ children and with creating a profile of the school. We want to

determine your priorities - what are the most common behavioural and emotional

problems you encounter within the school and what are the most common family

background risk factors for the development of these problems. In Phase 1 we will

also look at what policies, systems, structures, or strategies you already have in place

for managing and supporting troubled children and families. This is so we can find

Appendix I 343

out what your strengths are, so that any interventions we introduce in Phase 2 are

compatible with what you are already doing and build on your current practices. But

also importantly, to identify any weaknesses or gaps that we could look at addressing

when we get to Phase 2. We are not just looking at what is available within schools,

but going into the wider community as well to conduct an audit of what may be

available more broadly to help support schools in managing children in need. We

know that often if you have a child in crisis, you need help straight away, and yet if

you refer the child to a community service, often there are lengthy waiting lists.

PROMAS is concerned with improving school-community links and looking at

innovative ways to get around the problem of lengthy waiting lists.

✤ To show you how this all fits together, here is a timeline for Phase 1. This

year, in 1999 we will collect three types of information from parents and teachers: 1)

information on children’s current behaviour both at home and at school, 2)

information on children’s family backgrounds and how much teachers know about

children’s family backgrounds, and 3) information on which interventions you think

will be feasible within the school setting and be useful for helping children with

different sorts of problems. In 2000, there will be a one year follow-up where we will

collect the same information again. In addition, we will conduct the school audit to

look at the policies or systems the school currently has in place. In 2001, there will be

a two year follow-up and it is at this time that we will conduct the community audit.

At the end of Phase 1 (2001), schools will be randomly allocated to one of two

conditions - an intervention group, and a wait list group. If you are allocated to the

intervention group, you will receive interventions at the beginning of Phase 2 in 2002.

If you are allocated to the wait list group, it means you have to wait a little longer

until the end of Phase 2 (2004) to receive the interventions, but all schools will

receive interventions in the end. In fact, the advantage of being allocated to the wait

list group, is that the interventions will have had longer to have been trialled and we

are more likely to know the successful elements of the interventions by then.

✤ In terms of the types of interventions we introduce, well that depends very

much upon what we find out in Phase 1. So we will know what the most common

mental health problems are, which interventions you think might be useful, and which

interventions have been evaluated and found to be effective. Chris, do you want to

say a word or two about what you are working on at the moment? (Chris then

explains he is doing a literature review of school-based programs to determine which

Appendix I 344

strategies have been evaluated and have strong evidence of being effective.) We will

be in a position to say well these are the problems you are faced with and here are a

list of interventions that other schools have found effective for dealing with those

problems.

What we do know about the interventions is that they will span whole-of-

school policies - so we are not targeting any particular grade. The interventions may

involve curriculum, so for example, if low self-esteem was identified as a problem,

we could introduce curriculum components that focus on improving children’s self-

esteem. We also know that staff skills and knowledge will be important - how

confident do you feel if a child comes to you with a problem and do you know where

you can go to get further help? And finally, as I mentioned before, the school’s

interactions with families and the broader community will also be important.

✤ Now let me explain exactly what would be involved if the school decides to

participate. The first thing that we would do is randomly select 60 children from

preschool to grade 3. We would send home these three questionnaires to those

children’s parents and whichever parents return their questionnaires with consent, we

would then ask that child’s classroom teacher to complete similar questionnaires.

One thing that is important here is that we don’t just send home the questionnaires to

parents without first giving them some warning and several chances to opt out of the

research if they don’t want to participate. So we send home flyers, information in the

school newsletter, and a letter specifically to the parents of the children who have

been selected explaining the project in more detail and asking them to contact the

research team or the school if they do not want the school releasing their addresses to

the research team.

The time involved for teachers depends upon how many classes you have per

grade and how many parents return their questionnaires. How many classes do you

have per grade here? (let’s say for example, the school says they have 2 classes per

grade). We are selecting 60 children in total, so that is 15 per grade from preschool to

grade 3. If you have 2 classes per grade, we would select 7-8 children per class. Last

year we got a 50-60% response rate from parents, so 50-60% of 7-8 is about 3-5

children for which you would be asked to complete questionnaires. The

questionnaires take about 45 minutes per child to complete so you are looking at 3-5

multiplied by 45 minutes - and we know that with everything else you are expected to

do, that is quite a lot of time. We’ve tried to think of everything we could do to make

Appendix I 345

it as easy as possible for teachers to complete questionnaires, and one thing we have

come up with is to have someone from the PROMAS team present in the school while

teachers are filling in the questionnaires - to be available to answer questions and help

teachers fill them in.

Common questions and answers around this point Please note: if these questions are not asked, we made sure we gave the

information that would answer them anyway.

1) Q - Do the children have to be randomly selected or can we pick out the

ones we think are at risk? A - The children have to be randomly selected for a couple

of reasons. Because we want to be able to create a profile for the school, we need to

have a sample of children who are representative of the general population of children

at this school. The best way to do this would be to send home questionnaires to every

child’s parent. The problem with this is it would have been way too much work for

teachers! The second reason we have to randomly select is that we have to be careful

not to ‘stigmatise’ families or children. One of the first questions parents ask when

they receive this fairly personal questionnaire, is why me? We need to be able to

reassure them, that their child has been selected on a purely random basis.

2) Q - Are people really going to be honest on such a personal questionnaire?

A - Because of the sensitive nature of the questionnaires, confidentiality is very

important, so that anything the parents tell us remains confidential and anything

teachers tell us also remains confidential. A further mechanism we have for

protecting confidentiality is that as soon as we receive questionnaires back from either

parents or teachers, we detach the cover sheets which contain the identifying

information and these are then stored separately from the questionnaires - so that

anyone looking at the completed questionnaire cannot identify who has fill it in.

Having said that, we were amazed by some of the responses we had last year. We had

parents reporting the full range of risk factors - saying that, yes, they were using harsh

parenting practices, yes, they were engaging in criminal activities, or yes, they were

taking drugs.

3) Q - Aren’t the ones at highest risk the ones least likely to return

questionnaires? A - Yes, we know from other survey-based research that, invariably,

the highest 1-5% of at risk families are unlikely to return questionnaires. We are

Appendix I 346

doing a number of things to increase response rates, such as sending out reminder

letters and replacement surveys. We offer parents the incentive that if they return

questionnaires, they will be entered into a draw to win a $200 Myer Voucher.

However, the main thing that we are doing to get around the problem of high risk

families not returning questionnaires is phoning as many of the nonresponders as we

can. Last year when we did this, we found that 90% or more were then happy to

complete the Family Risk Factors Checklist with us over the phone - so we are getting

information on even the really high risk families. And as I said before, we certainly

had some very high risk families, reporting the presence of many risk factors,

returning questionnaires last year.

Another person who will be very important to us is the Liaison Officer. This

will be someone with an interest in this area from each school who will help to ensure

good communication between the PROMAS team and the school.

Appendix J 347

APPENDIX J - INFORMATION SHEETS

Information Sheet for Parents

Information Sheet for Teachers

Appendix J 348

INFORMATION FOR PARENTS

Project Title The Promoting Adjustment in Schools Project (PROMAS) What the Research is About The PROMAS Project is a study examining the types of problems children and families experience. It aims to help schools to provide services to children and families in need. Schools are an important part of modern life. Most children spend large amounts of time at school. Schools also have ongoing contact with parents and the broader community. This means that teachers, principals and others may be able to identify children or families who are having problems, and provide assistance in times of need. This research is concerned with assessing your child’s personal adjustment and wellbeing. It is also concerned with identifying the types of changes and problems that may be affecting your family. Finally, the research is interested in your opinions regarding the types of services that schools could provide to support your child and your family. Purpose of the Research This project has four aims: 1. To assess the wellbeing and adjustment of approximately 1500 Queensland preschool

and primary school children; 2. To identify the characteristics and problems experienced by the families of these


respond to children and families who need help; and 4. To help participating schools to develop effective strategies for helping children and

families in need. Who is Doing the Research? This study is part of a large public health project headed by Dr Jan Nicholson at the School of Public Health at the Queensland University of Technology. This research is being conducted in association with experts from the University of Melbourne, and the University of Texas in Houston. A number of researchers will be working on the study, including Ms Sarah Dwyer, a psychologist from QUT. How Was Your Family Selected? The parents and teachers of about 1500 children from 25 Queensland state primary schools will be asked to take part in the study. From each school, the names of 60 students in preschool to grade three will be selected at random from the school roles. The parents and teachers of these children will be asked to complete some questionnaires about the child’s behaviour, the child’s family background, and their opinions regarding the role of schools in helping families and children. You are being asked to participate, because your child’s name was one of those chosen at random from the school role.

Appendix J 349What is Involved For You? This study will require you to complete three questionnaires: • one about your family (Family Background Checklist); • one about your child’s behaviour (Child Behaviour Checklist); and • one about your opinions on the role of schools in helping children and families

(Intervention Option Survey). Each questionnaire will take 10-15 minutes to complete. Some people will be posted an extra series of questions (Additional Questions) a few weeks after you receive this package. These examine some areas of family relationships or adult adjustment in greater detail. These additional questions are being sent, at random, to four in every ten participants. Duration of Involvement We are interested in how families develop and change over time, and how this is related to children’s wellbeing and the role of schools. Therefore, we would like to study your family three times: • now – please complete all enclosed questionnaires now. • one year from now – in 12 months time, we will send you the same questionnaires

to complete again. • two years from now – in 24 months time, we will send you the questionnaires to

complete again. Teachers’ Questionnaires If you agree to be involved in this research, and if you agree to your child’s teacher being involved, we will send your child’s current classroom teacher a set of questionnaires to complete. These questionnaires are similar to the three that you have completed. They ask questions about your child’s behaviour, family background, and the role of schools.

Costs and Benefits that May Result from the Research This research will provide important information that will help schools to develop programs that meet the needs of children and families in their communities. It aims to help schools to detect children and families who need help at an earlier stage than may be happening now. Providing earlier help to children and their families can help to prevent problems and improve children’s long-term adjustment. There will be no direct costs arising from your participation in this research. The questionnaires should take about 10-15 minutes each to complete. Confidentiality of Your Responses The personal identity of yourself and your family will remain confidential at all times. The information that you provide will NOT be passed on to your child’s teacher and, similarly, any information your child’s teacher provides will be posted directly to the researcher and will remain confidential. To ensure confidentiality, all questionnaires are identified by a coded number. Identifying information such as your consent forms and the instructions pages will be stored separately from your questionnaires, in securely locked, restricted access filing cabinets. No identifying information about the participants will be used in any paper that may result from this research.

Appendix J 350Consent Included in this package are two consent forms. One of them is for your own consent to participate for two years, and therefore asks for your address and phone number so that we can recontact you in 12 months and 24 months time. The second consent form is for your permission to give the similar questionnaires to your child’s teacher to complete. Participation in this project is entirely voluntary and you are free to withdraw from the study at any time without comment or penalty. You may also withdraw your permission for the teacher to complete these questionnaires about your child at any time without comment or penalty. Questions or Concerns If you are unsure about the aims of the research, or whether to participate, please contact Sarah Dwyer on 3864 5561 or Dr Jan Nicholson on 3864 3389. They will be able to answer any questions you may have. You may also contact the Secretary of the University Research Ethics Committee on 3864 2902 if you wish to raise any concerns about the conduct of this research. If you have concerns about your child’s adjustment, please contact the research team. We may be able to provide advice regarding appropriate services in your community that may benefit your family. Contact: Sarah Dwyer 3864 5561. What Next? In order to gain an understanding of the needs of a wide range of children and their families, we would like as many parents as possible to complete the questionnaires, so your participation is very much appreciated. When you have completed the questionnaires and consent forms, please put them in the reply paid envelope (no stamp required) and mail this to us. As a token of our appreciation for your participation in the research, each family who returns the completed questionnaires and consent forms will have the opportunity to win a $200 Myer Voucher. We have also included a $1 scratch-it ticket with your questionnaires as a small thank you.

If you do not wish to participate, please return the blank questionnaires and crossed out consent forms. This will ensure that we do not send you reminders and replacement questionnaires. Thank you.

Further Information or Questions: Sarah Dwyer phone: 3864 5561 Mailing Address: Ms Sarah Dwyer Promoting Adjustment in Schools Project (PROMAS) School of Public Health Queensland University of Technology Victoria Park Rd, Kelvin Grove, QLD 4059.

Appendix J 351

INFORMATION FOR TEACHERS

Project Title The Promoting Adjustment in Schools Project (PROMAS) What the Research is About The PROMAS Project is a study examining the types of problems that children and families experience. It aims to help schools to provide services to children and families in need. Schools are an important part of modern life. Most children spend large amounts of time at school. Schools also have ongoing contact with parents and the broader community. This means that teachers, principals and others may be able to identify children or families who are having problems, and provide assistance in times of need. In particular, school personnel may be well placed to promote positive adjustment in students, and to help children and families to access appropriate help for problems that are developing. This research is concerned with assessing the adjustment and wellbeing of a random sample of Queensland preschool and primary school children. It is also concerned with identifying the types of changes and problems that occur in families, that may affect child adjustment. Finally, the research seeks your opinions regarding the types of services that schools could provide to support children and families in your school community.

Purpose of the Research This project has four aims: 1. To assess the wellbeing and adjustment of approximately 1500 Queensland preschool

and primary school children; 2. To identify the characteristics and problems experienced by the families of these


respond to children who are at risk of developing mental health problems; and 4. To help participating schools to develop effective mental health intervention and

prevention strategies for helping children and families in need. Who is Doing the Research? This study is part of a large public health project headed by Dr Jan Nicholson at the School of Public Health at the Queensland University of Technology. This research is being conducted in association with experts from the University of Melbourne, and the University of Texas in Houston. A number of researchers will be working on the study, including Ms Sarah Dwyer, a psychologist from QUT.

Appendix J 352How Was Your School Selected? All state primary schools from 11 districts in South East Queensland which had enrolment sizes between 200-700 children were invited to participate in the project. Your school was one of the first 25 to agree to participate in the study. From each school, 30 girls and 30 boys from preschool to grade three will be randomly selected, and consent for participation will be sought from each child’s parents. How Were You Selected? Parents of participating children have provided consent: (1) to complete a series of questionnaires about their child and family; and (2) for the child’s classroom teacher to complete a similar set of questionnaires about the child and family background. The fact that you have been contacted by the research team, indicates that the parents of the child named on the attached questionnaires have provided their consent for you to complete a series of questionnaires about the child. A copy of the signed consent form is enclosed.

What is Involved? This study will require you to complete a one page form (listing your personal details) and three questionnaires: • one about the child’s family background (Family Risk Factors Checklist); • one about the child’s behaviour (Teacher Report Form); and • one about your opinions on the role of schools in helping children and families

(Intervention Option Survey). Each questionnaire will take about 15 minutes to complete. If you have more than one child in your class who has been selected for the study, you will receive separate packages and will be asked to complete these forms for each child. In total, the questionnaires should take about 45 minutes per child to complete. Costs and Benefits that May Result from the Research This research will provide important information that will help schools to develop programs that meet the mental health needs of children and families in their communities. It aims to help schools to detect children and families who need help at an earlier stage than may be happening now. Providing earlier help to children and their families can help to prevent problems and improve children’s long-term adjustment. There will be no direct costs arising from your participation in this research. The questionnaires should take about 45 minutes per child to complete.

Confidentiality of Your Responses Your personal identity will remain confidential at all times. The information that you provide will NOT be passed on to the child’s parents and, similarly, any information the child’s parents provide to the study will remain confidential. To ensure confidentiality, all questionnaires are identified by a coded number. Identifying information such as your consent forms and the instructions pages will be stored separately from your questionnaires, in securely locked, restricted access filing cabinets. No identifying information about the participants will be used in any publication that may result from this research.

Appendix J 353Consent Participation in this project is entirely voluntary and you are free to withdraw from the study at any time without comment or penalty. Questions or Concerns If you are unsure about the aims of the research, or whether to participate, please contact Sarah Dwyer on 3864 5561 or Dr Jan Nicholson on 3864 3389. They will be able to answer any questions you may have. You may also contact the Secretary of the University Research Ethics Committee on 3864 2902 if you wish to raise any concerns about the conduct of this research. If you have any concerns about the child or family who are the focus of the research, please contact the research team. We may be able to provide advice regarding appropriate services in your community that may benefit this family. Contact: Sarah Dwyer 3864 5561. What Next? In order to gain an understanding of the needs of a wide range of children and their families, we would like as many teachers as possible to complete the questionnaires, so your participation is very much appreciated. When you have completed the questionnaires, please put them in the reply paid envelope (no stamp required) and mail this to us. As a token of our appreciation for your participation in the research, $50 will be given to your school. We have also included a $1 scratch-it ticket with your questionnaires as a small thank you.

If you do not wish to participate, please return the blank questionnaires (clearly marked that you do not wish to participate). This will ensure that we do not send you reminders and replacement questionnaires. Thank you.

Further Information or Questions: Sarah Dwyer phone: (07) 3864 5561 Mailing Address: Ms Sarah Dwyer Promoting Adjustment in Schools Project (PROMAS) School of Public Health Queensland University of Technology Victoria Park Rd, Kelvin Grove QLD 4059.

Appendix K 354

APPENDIX K - CONSENT FORMS

Consent Form for Parent Participation

Consent Form for Teacher Completion of Questionnaires

about my Child

Appendix K 355

The Promoting Adjustment in Schools Project (PROMAS)

Consent Form for Parent Participation

I hereby give my consent to participate in the PROMAS Project. I have received detailed written materials outlining the nature of the research and have been given the option to phone the researchers if I have any questions or concerns. I acknowledge that: 1. Any information I give to researchers will be treated in the strictest confidence. Any

information I provide will NOT be passed on to my child’s teacher. 2. My participation in the study is voluntary. I am able to withdraw at any time, without

incurring any costs or penalties. 3. I will be asked to complete three questionnaires now and on two more occasions with one

year between each assessment. 4. Information about families who participate in the study will be used for the preparation of

research reports and articles. The personal identity of myself and my family will remain confidential at all times.

Name……………………………………….. Signature……………………………………. Date………………………. Follow-up Details Over the next two years, the PROMAS Project would like to follow-up on your child’s behaviour and family circumstances. To facilitate this process, we would like your address and telephone number to help us find you. This information will NOT be passed on to your child’s school. Your Address………………………………………………………………….. ……………………………………………… Postcode…………………… Telephone………………………………….(H) …………………………….(W) Please provide the names and addresses of two relatives or other persons who are acquainted with you, that are unlikely to shift and who are always likely to know your future whereabouts. You should contact these people and check with them that it is okay before completing this section. We will contact these people to check your location if, and only if, we have difficulty finding you at 12 and 24 months.

Appendix K 356

1. Relative/friend

Name……………………………………………………………………………. Address………………………………………………………………………….. ……………………………………………. Postcode………………………… Telephone………………………….…(H) ………………………………(W)

2. Relative/friend Name…………………………………………………………………………………. Address……………………………………………………………………………….. ……………………………………………… Postcode………………………….. Telephone……………………………….(H) ………………………………(W)

Appendix K 357

The Promoting Adjustment in Schools Project (PROMAS) Consent Form for Teacher Completion of Questionnaires about my Child

I hereby give consent for my child’s teacher to complete questionnaires about my child’s family background. I have received detailed written materials outlining the nature of the research. I acknowledge that: 1. Any information that my child’s teacher gives to researchers will be completely

confidential. 2. My permission for the teacher to complete questionnaires about my child is

voluntary. I am able to withdraw this consent at any time, without incurring any costs or penalties.

3. My child will be followed up for two years. My child’s current teacher will be asked

to complete questionnaires about my child’s family background. Whoever is teaching my child at the time of the two follow-ups will be asked to complete the follow-up questionnaires.

My child’s name……………………………………… My name……………………………………………… Signature……………………………………………… Date……………………….

Appendix L 358

APPENDIX L - TEACHER DETAILS SHEET

Appendix L 359

Teacher Number

Teacher Details Sheet These questions provide us with demographic information about the teachers who have answered the questionnaires. Please write your answer in the space provided or circle the correct number. 1. What is your age? ____________________________________________________ 2. What is your gender?

female 1 male 2

3. What are your highest qualifications?

certificate 6 diploma 5 3 year degree 4 4 year degree (hons or grad. dip.) 3 masters degree 2 doctorate 1

4. For how many years have you been teaching? _____________________________ 5. What is the title of your current teaching appointment? _____________________

Appendix M 361

APPENDIX M - PROMAS NEWSLETTER

halla


Appendix N 365

APPENDIX N - CHARACTERISTICS OF PARTICIPANTS

WITH FULL TIME 1 AND 2 TEACHER DATA VERSUS

PARTICIPANTS MISSING TEACHING DATA

Appendix N 366

Table N.1

Demographic Characteristics and Proportions of Children at Low, Medium, or High Risk as

Measured by the FRFC-T for Participants with Full Time 1 and Time 2 Teacher Data Versus

Participants Missing Teacher Data

Full Data (N=455)a

Missing Data (N=570)a

Sig. (p)b


Child age (at Time 1) 6.9 (1.2) 6.8 (1.2) .264

Parent age (at Time 1) 35.9 (5.6) 35.2 (5.8) .053

Number of children living with family 2.6 (1.1) 2.5 (1.1) .287

% (N) % (N)

Gender of parent completing FRFC-P (female)

89.4 (406) 88.7 (504) .763

Gender of child (female) 54.1 (246) 48.6 (277) .090


Aboriginal or Torres Strait Islander origin 2.2 (10) 5.7 (32) .007

Language other than English spoken at home

7.8 (35) 13.6 (77) .003

Either parent unemployed 6.6 (30) 10.1 (57) .055

Either parent in low prestige occupationc (or neither parent working)

40.0 (173) 47.8 (259) .016

Parent completing FRFC-P did not finish high school

52.9 (238) 53.8 (304) .800

Income less than AUD$20,800 per year 19.0 (83) 27.4 (149) .002

Short FRFC-T total risk score

Low (0-2 risk factors)

Medium (3-6 risk factors)

High (7 or more risk factors)

74.7 (340)

22.4 (102)

2.9 (13)

71.4 (215)

21.3 (64)

7.3 (22)

.021

a Total N; percentages are based on the available N, which fluctuates slightly for each item. b Unpaired t-test used for continuous variables; Chi-squared test used for categorical variables. c Low prestige occupation = the three lowest categories of the 9-point Australian Standard Classification of Occupations (ASCO) coding system (Australian Bureau of Statistics & McLennan, 1997).

Appendix O 367

APPENDIX O - COORDINATES USED TO CONSTRUCT

RECEIVER OPERATING CHARACTERISTIC (ROC)

CURVES IN PAPER 3

Appendix O 368

Table O.1

Coordinates Used to Construct ROC Curves for the Prediction of Internalising Only

Problems: Parent-report Screening Methods

t Score for CBCL Internalising Score FRFC-P Total Risk Score

Cut-offa Sensitivity 1-Specificity Cut-offa Sensitivity 1-Specificity

32.0 1.000 1.000 -1.0 1.000 1.000

33.5 .986 .925 0.5 .971 .983

36.5 .971 .881 1.5 .914 .925

39.5 .971 .835 2.5 .829 .815

41.5 .971 .759 3.5 .657 .687

44.5 .971 .635 4.5 .514 .559

47.0 .957 .513 5.5 .429 .428

48.5 .957 .466 6.5 .300 .325

50.0 .943 .435 7.5 .271 .247

51.5 .886 .365 8.5 .200 .181

52.5 .871 .341 9.5 .186 .138

53.5 .829 .319 10.5 .143 .092

54.5 .800 .283 11.5 .114 .060

56.0 .686 .224 12.5 .043 .036

57.5 .571 .175 13.5 .029 .022

58.5 .571 .148 14.5 .014 .016

59.5 .500 .111 15.5 .000 .009

60.5 .443 .089 16.5 .000 .004

61.5 .429 .082 18.0 .000 .000

62.5 .400 .078

63.5 .343 .057

64.5 .271 .050

65.5 .257 .049

66.5 .243 .046

67.5 .243 .040

68.5 .214 .036

69.5 .157 .032

70.5 .157 .027

71.5 .114 .020

72.5 .086 .020

Appendix O 369

Table O.1 cont.

t Score for CBCL Internalising Score FRFC-P Total Risk Score


73.5 .043 .014

74.5 .043 .011

75.5 .029 .010

76.5 .029 .009

77.5 .014 .004

79.0 .000 .004

81.0 .000 .003

83.0 .000 .001

85.0 .000 .000

a The smallest cut-off value is the minimum observed test value minus 1, and the largest cut-off value is the maximum observed test value plus 1. All other cut-off values are the averages of two consecutive ordered observed test values.

Appendix O 370

Table O.2

Coordinates Used to Construct ROC Curves for the Prediction of Externalising Only

Problems: Parent-report Screening Methods

t Score for CBCL Externalising Score FRFC-P Total Risk Score


29.0 1.000 1.000 -1.0 1.000 1.000

31.0 1.000 .976 0.5 1.000 .980

33.5 1.000 .934 1.5 .965 .921

36.0 1.000 .917 2.5 .947 .805

37.5 1.000 .890 3.5 .912 .666

39.0 1.000 .865 4.5 .807 .535

40.5 1.000 .827 5.5 .719 .405

41.5 1.000 .804 6.5 .632 .298

42.5 1.000 .760 7.5 .526 .227

43.5 1.000 .722 8.5 .386 .166

45.0 1.000 .639 9.5 .298 .130

46.5 .982 .571 10.5 .211 .087

47.5 .982 .547 11.5 .175 .056

48.5 .982 .519 12.5 .088 .032

49.5 .982 .488 13.5 .070 .018

51.0 .982 .427 14.5 .018 .016

52.5 .930 .370 15.5 .000 .008

53.5 .877 .305 16.5 .000 .004

54.5 .807 .271 18.0 .000 .000

55.5 .772 .245

56.5 .754 .210

57.5 .702 .189

58.5 .684 .165

59.5 .649 .137

61.0 .614 .123

62.5 .526 .100

63.5 .491 .082

64.5 .404 .071

65.5 .351 .061

66.5 .351 .054

Appendix O 371

Table O.2 cont.

t Score for CBCL Externalising Score FRFC-P Total Risk Score


67.5 .351 .051

68.5 .281 .034

69.5 .246 .031

70.5 .211 .028

71.5 .140 .021

72.5 .105 .018

73.5 .018 .014

74.5 .000 .013

75.5 .000 .010

76.5 .000 .007

78.5 .000 .006

80.5 .000 .004

82.0 .000 .001

84.0 .000 .000


Appendix O 372

Table O.3

Coordinates Used to Construct ROC Curves for the Prediction of Total Behaviour Problems:

Parent-report Screening Methods

t Score for CBCL Total Problem Score FRFC-P Total Risk Score


23.0 1.000 1.000 -1.0 1.000 1.000

25.0 1.000 .991 0.5 .992 .980

27.5 1.000 .976 1.5 .977 .914

29.5 1.000 .966 2.5 .930 .793

31.0 1.000 .961 3.5 .867 .647

32.5 1.000 .956 4.5 .766 .513

33.5 1.000 .950 5.5 .680 .378

34.5 1.000 .934 6.5 .547 .277

35.5 1.000 .922 7.5 .484 .202

36.5 1.000 .917 8.5 .414 .136

37.5 1.000 .895 9.5 .344 .102

38.5 1.000 .868 10.5 .250 .066

39.5 1.000 .842 11.5 .203 .038

40.5 1.000 .812 12.5 .117 .020

41.5 1.000 .757 13.5 .078 .011

42.5 1.000 .727 14.5 .055 .008

43.5 1.000 .693 15.5 .016 .006

44.5 1.000 .650 16.5 .016 .002

45.5 1.000 .605 18.0 .000 .000

46.5 1.000 .550

47.5 1.000 .522

48.5 .984 .473

49.5 .984 .415

50.5 .984 .384

51.5 .969 .331

52.5 .969 .290

53.5 .969 .254

54.5 .945 .216

55.5 .891 .179

56.5 .836 .138

Appendix O 373

Table O.3 cont.

t Score for CBCL Total Problem Score FRFC-P Total Risk Score


57.5 .750 .121

58.5 .719 .094

59.5 .711 .082

60.5 .672 .064

61.5 .641 .061

62.5 .617 .052

63.5 .594 .044

64.5 .563 .031

65.5 .492 .019

66.5 .406 .014

67.5 .352 .008

68.5 .305 .008

69.5 .266 .008

70.5 .242 .002

71.5 .180 .002

72.5 .148 .002

73.5 .102 .002

75.0 .078 .002

77.0 .063 .000

78.5 .047 .000

79.5 .031 .000

80.5 .023 .000

82.0 .016 .000

84.5 .008 .000

87.0 .000 .000


Appendix O 374

Table O.4

Coordinates Used to Construct ROC Curves for the Prediction of Internalising Only

Problems: Teacher-report Screening Methods

t Score for TRF Internalising Score Short FRFC-T Total Risk Score


33.0 1.000 1.000 -1.0 1.000 1.000

34.5 .914 .962 0.5 .743 .638

35.5 .914 .919 1.5 .571 .390

36.5 .914 .824 2.5 .257 .252

39.0 .800 .693 3.5 .171 .152

42.0 .800 .655 4.5 .171 .090

43.5 .743 .524 5.5 .086 .062

44.5 .743 .521 6.5 .086 .024

45.5 .714 .502 7.5 .057 .014

46.5 .629 .431 8.5 .029 .005

47.5 .629 .414 10.5 .000 .002

48.5 .629 .390 13.0 .000 .000

50.0 .543 .352

51.5 .514 .281

52.5 .486 .279

53.5 .457 .243

54.5 .457 .207

55.5 .457 .198

56.5 .400 .164

57.5 .371 .138

58.5 .371 .126

59.5 .371 .112

60.5 .343 .090

61.5 .286 .079

62.5 .200 .064

63.5 .200 .055

64.5 .143 .040

65.5 .143 .029

66.5 .114 .024

67.5 .086 .017

Appendix O 375

Table O.4 cont.

t Score for TRF Internalising Score Short FRFC-T Total Risk Score


68.5 .086 .014

69.5 .086 .012

71.0 .057 .007

73.5 .057 .005

75.5 .029 .002

81.5 .029 .000

88.0 .000 .000


Appendix O 376

Table O.5

Coordinates Used to Construct ROC Curves for the Prediction of Externalising Only

Problems: Teacher-report Screening Methods

t Score for TRF Externalising Score Short FRFC-T Total Risk Score


34.0 1.000 1.000 -1.0 1.000 1.000

36.5 1.000 .960 0.5 .714 .640

38.5 1.000 .898 1.5 .629 .386

39.5 .971 .783 2.5 .486 .233

41.0 .971 .771 3.5 .400 .133

42.5 .914 .533 4.5 .257 .083

44.0 .886 .505 5.5 .171 .055

45.5 .886 .498 6.5 .114 .021

46.5 .886 .443 7.5 .086 .012

47.5 .857 .438 8.5 .000 .007

48.5 .829 .400 10.5 .000 .002

49.5 .800 .350 13.0 .000 .000

50.5 .800 .326

51.5 .743 .319

52.5 .714 .255

53.5 .686 .233

54.5 .629 .198

55.5 .600 .169

56.5 .600 .152

57.5 .514 .129

58.5 .486 .110

59.5 .486 .095

60.5 .457 .071

61.5 .457 .062

62.5 .457 .052

63.5 .429 .045

64.5 .429 .038

65.5 .343 .033

66.5 .257 .026

68.0 .200 .019

Appendix O 377

Table O.5 cont.

t Score for TRF Externalising Score Short FRFC-T Total Risk Score


70.0 .171 .019

72.5 .143 .017

74.5 .143 .014

76.5 .114 .010

78.5 .057 .010

80.0 .029 .010

81.5 .029 .007

82.5 .000 .007

83.5 .000 .002

85.0 .000 .000


Appendix O 378

Table O.6

Coordinates Used to Construct ROC Curves for the Prediction of Total Behaviour Problems:

Teacher-report Screening Methods

t Score for TRF Total Problem Score Short FRFC-T Total Risk Score


27.0 1.000 1.000 -1.0 1.000 1.000

29.5 1.000 .975 0.5 .792 .627

31.5 1.000 .913 1.5 .660 .371

32.5 1.000 .846 2.5 .472 .224

34.0 1.000 .836 3.5 .358 .127

35.5 .981 .799 4.5 .245 .077

36.5 .981 .789 5.5 .189 .047

37.5 .925 .711 6.5 .151 .012

38.5 .925 .709 7.5 .094 .007

39.5 .906 .687 8.5 .038 .002

40.5 .887 .679 10.5 .019 .000

41.5 .887 .624 13.0 .000 .000

42.5 .868 .582

43.5 .849 .545

44.5 .849 .512

45.5 .830 .470

46.5 .792 .438

47.5 .792 .396

48.5 .755 .336

49.5 .736 .306

50.5 .717 .281

51.5 .717 .261

52.5 .660 .219

53.5 .660 .189

54.5 .642 .162

55.5 .623 .137

56.5 .528 .124

57.5 .509 .104

58.5 .472 .090

59.5 .472 .070

Appendix O 379

Table O.6 cont.

t Score for TRF Total Problem Score Short FRFC-T Total Risk Score


60.5 .453 .065

61.5 .434 .055

62.5 .377 .050

63.5 .321 .037

64.5 .245 .030

65.5 .245 .025

66.5 .226 .015

68.0 .170 .012

69.5 .151 .010

70.5 .132 .010

72.0 .113 .010

73.5 .094 .010

74.5 .094 .007

76.0 .038 .007

77.5 .038 .005

79.5 .000 .005

81.5 .000 .002

83.0 .000 .000


Appendix P 380

APPENDIX P - TEACHER JUDGEMENT OF CHILDREN’S

FUTURE RISK OF DEVELOPING MENTAL HEALTH

PROBLEMS

Appendix P 381

Teacher Judgement of Children’s Future Risk of Developing

Mental Health Problems

The brief investigation described in this Appendix was taken out of Paper 2

due to space limitations. It explores the issue of whether teachers use their knowledge

of children’s exposure to family risk factors to answer the single nomination question.

Aim

To determine whether teachers use their knowledge of children’s exposure to

family risk factors to make judgements concerning children’s future risk of

developing a mental health problem.

Measure: Teacher Judgement of Children’s Future Risk Teachers were asked to make a global judgement concerning each child’s risk

status by answering a single nomination question: ‘Do you think that this child has a

higher chance than average of developing a behavioural, emotional, or mental health

problem in the future?’. Possible responses were: ‘yes’, ‘no’, or ‘don’t know’.

Results To examine whether teachers used their knowledge of children’s risk factor

exposure to make judgements about children’s future risk of negative mental health

outcomes, logistic regression analyses were performed. The potential clustering

effect (due to teachers completing questionnaires on more than one child) was

controlled for in these analyses. Using Time 1 data only, teachers’ perceptions of

family risk factor exposure (short FRFC-T total risk score: low, medium, high) and

teachers’ observations of child behaviour (TRF/C-TRF total behaviour problems:

clinical range, nonclinical range) were entered separately into logistic regression

equations to determine whether they predicted teachers’ judgement of which children

may be at risk for future mental health problems (single nomination question: ‘yes’ vs

‘no’).

Table P.1 shows the odds of teachers making a judgement that a child has a

higher chance than average of developing a mental health problem in the future (yes

Appendix P 382

response to the single judgement question), based on their ratings of the child’s level

of exposure to family risk factors on the short FRFC-T and their observation of the

child’s behaviour on the TRF. Based on the unadjusted ORs, teachers were over four

and a half times (OR = 4.61, CI = 3.07 - 6.92) more likely to identify a child as being

‘at future risk’ when they rated the child as being at medium overall family risk

exposure and over 55 times (OR = 55.36, CI = 16.23 - 188.85) more likely to identify

a child as being ‘at future risk’ when they perceived the child as being exposed to

high overall family risk, relative to children whom teachers rated as being at low

overall family risk exposure. In comparison, the odds of a teacher making a

judgement of being ‘at future risk’ were over 84 times (OR = 84.50, CI = 33.09 -

215.79) higher for children they rated as being in the clinical range for total behaviour

problems, relative to children rated as being in the nonclinical range.

Next, the short FRFC-T total risk score (low, medium, high) was entered into

the logistic regression equation at the same time as the continuous TRF total problem

score to determine whether teachers’ knowledge of family risk factor exposure

predicted a ‘yes’ response to the single judgement question, even after variation due

to their observations of child behaviour was taken into account. Similarly, the

dichotomous TRF total problem score (clinical range, nonclinical range) and

continuous total FRFC-T risk score were both included in a second multivariable

logistic regression to examine the unique influence of teachers’ observations of child

behaviour on their judgement of a child’s future risk. Children for whom teachers

answered ‘don’t know’ (N = 51) or were missing a response (N = 7) to the single

nomination question, were excluded from these analyses.

The adjusted odds ratios were lower but still highly significant. Even after

variation due to teachers’ observation of the child’s behaviour was taken into account,

the odds of a teacher identifying a child to be ‘at future risk’ were over twice as high

(OR = 2.30, CI = 1.33 - 3.98) for children rated as being exposed to medium family

risk and over 40 times (OR = 41.53, CI = 8.29 - 208.05) higher for children rated as

being exposed to high family risk, relative to children teachers perceived as being

exposed to low overall family risk. Not surprisingly, teachers’ observation of

children’s behaviour was still strongly predictive of teachers’ judgement of future

risk, even after adjusting for teachers’ perceptions of the child’s risk exposure (OR =

58.10, CI = 22.10 - 152.77).

Appendix P 383

Table P.1

Relative Odds of Teachers Responding ‘Yes’ (vs ‘No’)a to the Single Nomination

Question Concerning a Child’s Future Risk of Developing a Mental Health Problem

by Levels of Teacher-perceived Exposure to Family Risk Factors and Teachers’

Observation of the Child’s Behaviour at School

95% CI for Adjusted Odds

Ratio

Unadjusted Odds Ratio

Adjusted Odds Ratiob

Lower Upper

Sig (p)

Short FRFC-T Total Risk Scorec:

Low (0-2 risk factors; referent)

Medium (3-6 risk factors)

High (7 or more risk factors)

1.00

4.61

55.36

1.00

2.30

41.53

1.33

8.29

3.98

208.05

.003

<.001

TRF Total Behaviour Problemsd:

Not in clinical range (referent)

In clinical range

1.00

84.50

1.00

58.10

22.10

152.77

<.001

a Children for whom teachers answered ‘don’t know’ (N = 51) or were missing a response (N = 7) to the single nomination question were excluded from the analysis.

b Teachers’ ratings of children’s risk exposure on the short FRFC-T (low, medium, high) were adjusted by teachers’ observations of children’s behaviour on the TRF (continuous total behaviour problem score) and teachers’ observations of children’s behaviour on the TRF (in clinical range, not in clinical range) were adjusted by teachers’ ratings of children’s risk exposure on the short FRFC-T (continuous total risk score). c Unadjusted analyses: total N = 698; N that teachers judged to be at future risk = 172. Adjusted analyses: total N = 677; N that teachers judged to be at future risk = 165. d Both unadjusted and adjusted analyses: total N = 677; N that teachers judged to be at future risk = 165.

Appendix P 384

Discussion It was of interest to determine whether teachers used their knowledge of

children’s exposure to ALI- and SES-type family risk factors to predict whether a

child was ‘at future risk’ of developing a mental health problem. The odds of a

teacher identifying a child as being ‘at future risk’ were over 40 times higher for

children whom they rated as being at high overall risk on the short FRFC-T, relative

to children whom they rated as being at low overall risk, even after their observation

of the child’s behaviour had been taken into account. Teacher observation of the

child’s current behaviour was an even stronger predictor of teachers’ judgement of

risk. The odds of a teacher identifying a child as being ‘at future risk’ were over 58

times higher for children who were in the clinical range compared with children in the

nonclinical range, after adjusting by teacher ratings of children’s risk exposure. It

should be noted that the use of a single data source, the teacher, would have inflated

the relationship between teachers’ ratings of exposure to family risk factors,

observation of children’s behaviour, and teachers’ judgements of children’s future

risk status. The reported ORs would have been lower if the informant who rated the

child’s behaviour or exposure to family risk factors was different from the informant

who answered the simple nomination question. Nonetheless, the overall trends

reported here are likely to be robust.

Despite the greater predictive value of teachers’ observation of behaviour over

their knowledge of risk factor exposure, the use of child behaviour to nominate ‘at

risk’ children can only be used to identify children for indicated interventions, since,

by definition, these children have current behaviour problems. For example, Dadds,

Spence, Holland, Barrett and Laurens (1997) used teacher nominations of anxious

children as one of their screening gates for identifying children for an indicated

preventive intervention for anxiety disorders. These results provide clear evidence

that teachers do use their knowledge of children’s exposure to ALI- and SES-type

family risk factors to identify children who they perceive to be at future risk. Thus,

asking teachers to answer a simple question regarding their opinion of a child’s future

risk may also provide a promising avenue for efficient screening of children into

selective interventions.

Appendix P 385

Conclusions It has been demonstrated that teachers use their knowledge of children’s

family backgrounds when making judgements concerning children’s future risk of

developing mental health problems.

Appendix Q 386

APPENDIX Q - SUGGESTIONS FOR FUTURE

IMPROVEMENTS TO THE FRFC-P

Appendix Q 387

Table Q.1

Suggestions for Future Improvements to the Family Risk Factor Checklist - Parent

(FRFC-P)

Item Improvement

Improvements to Item Wording

10 Think about adding in ‘or Maori origin’.

11 Change wording from ‘does your family speak a language other than English’ to ‘Are you from a non-english speaking background?’

14 Add the word ‘completed’ at the end of the question so that it reads ‘highest level of education completed’.

15 Specify that the question is about ‘gross’ income; perhaps add the words ‘before tax’ to the end of the question.

16 Change ‘number of house moves’ to ‘number of houses lived in’ (it is easier to count).

17 Change ‘number of changes of parent figures’ to ‘number of parent figures’ (count any parent figure who has lived in the same household as your child - this is easier to work out than counting changes every time a parent figure moves into or out of the house).

21 Think about taking out the word ‘violence’ (it may be too strong a word for this context, potentially increasing the social desirability of responses)

22 Add in the words ‘with your partner’ to the end of the question (could be misunderstood to refer to relationship with child).

25 Assess the time period for the risk factor more accurately, i.e., it is not as large a risk factor to have been in trouble with the police as a teenager (before having children) as being in trouble with the police after the birth of own children, or in more recent years.

29 Take out the example ‘psychotic disorder’ (not readily understood); perhaps replace it with simpler language such as ‘seen or heard things not really there’.

30 Perhaps include back problems and cancer as other examples of relatively common chronic physical illnesses.

31 Add in the words ‘How often’ to the beginning of the question so that it has the same item wording as the other parenting items.

33 Think about changing the item to reflect praise following the child complying with instructions, as opposed to praise following the child doing something well, e.g., ‘How often do you (or your partner) praise your child when he/she follows your instructions?’.

34 This item explained zero variance therefore needs to be reworded.

35 Change ‘how much’ to ‘how often’.

Appendix Q 388

Table Q.1 cont.

Improvements to Item Wording cont.

39 Think about taking out the words ‘grabbing or slapping’ or incorporating them into a separate question (most parents consider smacking acceptable but not grabbing or slapping).

40 Perhaps take out the words ‘hit your child with an object other than your hand’ because this encompasses parents who use wooden spoons on their children (this item is meant to assess severe physical punishment and many wooden spoon incidents do not fall into the severe category).

42 Add in being arrested, e.g. ‘Did you or your partner get arrested or spend any time in prison in the last year?’.

47 Change ‘immediate’ family member to ‘close’ family member.

Add in another item for single parents on the nature of their relationship with their ex-partner (if they have one), e.g, conflict over child rearing.

Think about adding in extra items on adverse life events, e.g., a question about a close family member being badly hurt or sick in the previous year.

Improvements to Response Categories

14 Add a TAFE option.

15 Expand higher income categories to match ABS categories (they are currently collapsed).

21 Separate ‘never’ and ‘seldom’ into two different categories (for such a serious item, there is a big difference between never and seldom).

27 (a)

& (b)

Update response categories to better reflect Australian quantities of alcohol, e.g., put in ‘stubbie’. It may also help to specify the quantity in mL in brackets next to each category.

32 Take out the response option of ‘always’ (it is not possible to ‘always’ play games with a child).

Think about quantifying the response sets, e.g., for item 25 on criminal offences, possible response options could be: 0 = no offences; 1 = 1-2 offences; 2 = 3-4 offences, etc. At the least, give more specific examples of what is meant by ‘never’, ‘seldom’, ‘sometimes’, ‘often’, ‘always’.

Think about changing all response sets to a likert-type scale in order to standardise responses across the different items (instead of mixing dichotomous response sets with ordinal sets). This approach would better assess the severity of the risk factors that are currently assessed with a dichotomous response set.

To decrease response bias (acquiescence set), balance the items so that half require a positive response and half require a negative response.

Appendix Q 389

Table Q.1 cont.

Improvements to Questionnaire Formatting

14 (a) & (b)

Put both the parent and partner response categories on the same line, like all other items with a partner option.

22 Take out the instructions ‘only answer question 22 if your child lives in a two-parent family’ (too many two-parent families left this item blank because they assume the question is not relevant to them).

36 Similar to above, take out the instructions that ask parents to answer the item only if their child lives in a two parent family.

Sarah Blyth Dwyer - QUT · Sarah Blyth Dwyer BA(Hons)Psych This thesis is submitted for the degree...

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