Johnson Simon Biomedical Technology Infertility

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DOI: 10.1177/0891243211434615 2012 26: 261 originally published online 21 February 2012Gender & Society

Katherine M. Johnson and Richard M. Simonfor Technological Salience

Women's Attitudes Toward Biomedical Technology for Infertility : The Case

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WOMENS ATTITUDES TOWARD BIOMEDICAL TECHNOLOGY FOR

INFERTILITY:

The Case for Technological SalienceKatherine M. Johnsonthe Pennsylvania state Universityrichard M. siMonrice University at houston

Research has consistently revealed gender differences in attitudes toward science and technology. One explanation is that women are more personally affected by particular technologies (e.g., biomedical interventions), so they consider them differently. However, not all women universally experience biomedical technologies. We use the concept of technological salience to address how differences in subjective implications of a technology might explain differences in womens attitudes toward biotechnology. In a sample of U.S. women from the National Survey of Fertility Barriers, we examine how women with and without a biomedical barrier to fertility evaluate biotechnology for infertility, which, we argue, reflects differences in technological salience. For women with a biomedical barrier, various experiences, beliefs, and values impacted their attitudes; yet, most of these did not affect attitudes if women had not experienced a fertility barrier. Results suggest that technological salience contextualizes womens attitudes toward these biotechnologies and may also have broader implications for other biotechnologies.

Keywords: health/medical; knowledge/science; reproduction

INTRODUCTION

Research has consistently revealed gender differences in public attitudes toward science and technology. Women are typically more skeptical than men across a wide range of issues, including reproductive interventions (Napolitano and Ogunseitan 1999). One explanation is that women are

AUTHORS NOTE: We would like to thank Pat rafail and the anonymous reviewers for their comments on earlier versions of this manuscript.GENDER & SOCIETY, Vol. 26 No. 2, April 2012 261-289DOI: 10.1177/0891243211434615 2012 by The Author(s)

261 at INSTITUTO DE INVEST SOCIALES on March 20, 2012gas.sagepub.comDownloaded from

262 GENDER & SOCIETY / April 2012

more likely to be personally affected by particular technologies (Bryant and Pini 2006; Nelkin 1981). Yet most studies fail to link attitudes to experiences (except see Simon 2011). Feminist theory has provided some basis for understanding the gendertechnology relationship, although feminists have disagreed as to whether technology liberates or oppresses women (Lublin 1998; Wacjman 1991). Recent theorizing, however, is more ambivalentemphasizing that differences among women contextualize their relationships to technology: Experiences are not universal (Thompson 2002). Yet such insights have not been integrated into studies of attitudes toward technology: Most still treat gender categories as homogenous (e.g., Hayes and Tariq 2000).

In this article, we contribute to a theoretical framework of gender and attitudes toward technology that is based on experiences, focusing on biotechnology for infertility. Drawing on insights from feminist and other scholarship on technology, as well as social-psychological literature on attitudes, we address how differences in womens fertility status affect how they may differentially use personal experiences, beliefs, and values to evaluate this technology. For fertility status, we distinguish between women who have and have not experienced a biomedical barrier to fertility. This is a broader definition of fertility problems that includes medically defined infertility (12 months of unprotected [hetero]sex without conception), miscarriage, and other problems conceiving or carrying a pregnancy to term (e.g., chronic illness). We argue that having or not having a biomedical barrier to fertility is a major category of difference shaping womens attitudes about these technologies: We expect that women who have experienced a barrier will use different criteria when assessing the technology because it has greater subjective implications for them. We develop these ideas more generally with the concept of technological salience and test this on a sample of U.S. women from the National Survey of Fertility Barriers.

BACKGROUND: GENDER AND TECHNOLOGY

Feminist scholarship has increasingly theorized the gendertechnology relationship, and studies of public attitudes toward science have addressed gender differences in appraisals of science and technology. Yet these do not often come together in the same research. In theorizing technology, feminist scholarship has historically been deeply divided over viewing it as inherently oppressive or liberating for women (Lublin 1998). For instance, some view reproductive technologies as a form of patriarchal


Johnson and Simon / WOmENS ATTITUDES TOWARD BIOmEDICAL TECHNOLOGY 263

medical control over womens bodies (e.g., ORiordan and Haran 2009), while others have emphasized how technology can give women more control over reproduction (Wacjman 1991). More recent turns in feminist theorizing, however, have become more ambivalent, focusing on how womens relationships to technology may vary across different social groups and even for one woman across her life course (Lublin 1998; Thompson 2002). For example, Thompson (2005) observed how some women simultaneously experienced infertility treatment as objectifying and liberatinga phenomenon she described as agency through objectification.

Other, often nonfeminist, research has focused on differences in mens and womens attitudes toward science and technology. Women are typically more pessimistic than men across a range of issues, including the environment (McCright 2010), nuclear power (Freudenburg and Davidson 2007), and biotechnologies (Napolitano and Ogunseitan 1999; Simon 2010). Some point to gender socialization: Women are socialized to be less interested in and less informed about science and technology (Tenenbaum and Leaper 2003). This deficit model explanation posits that when people know more about science and technology, they will have more favorable attitudes (Allum et al. 2008). A recent study, however, found that womens increasing knowledge of technology actually contributed to their greater pessimism; for men this relationship was in the opposite direction (Simon 2010). In other words, men and women may evaluate science and technology through divergent processes.

One explanation is that women approach science and technology through a unique process, reflecting gender-specific consequences. Because womens bodies are more often involved in childbearing, they are more likely to be exposed to the consequences of science and technology (Bryant and Pini 2006; Mallory 2006). For example, Many of the applications [of biotechnologies] towards human health issues will likely affect fetuses, mothers, and young children more than [other] members of society (Napolitano and Ogunseitan 1999, 202). This suggests that certain technologies may have greater personal implications for women than for men.

Two key insights from feminist science and technology studies are relevant here. First, women are overrepresented as users rather than producers of technology, which reinforces gender inequality: Women are often defined as passive beneficiaries, whereas men are the bearers of technological knowledge (Bray 2007). Second, womens bodies are viewed differently than mens in relation to technology. As Martin (2001,



197) observed, science and medicine use models [of womens bodies] implying failed production, waste, decay, and breakdown. Therefore, when technology becomes personally relevant, it may also define womens bodies as problematic.

Yet these various insights and explanations still ignore how womens experiences with and relationships to technology differ: Not all women relate to reproductive technologies in the same way simply because they are women. Although womens bodies may be defined as problematic (Martin 2001), for some women this becomes inherently more personal. In the next section, we develop technological salience as an explanatory concept describing how womens evaluations of technology may differ vis--vis their personal history and experiences.

CONCEPTUAL FRAmEWORK

Technological Salience and Attitude Formation

We draw on social-psychological insights about attitude formation to begin filling in some conceptual gaps. Research on attitudes suggests that personal relevance of an attitude-object (i.e., the topic about which an attitude is being formed) may affect how an individual processes information and makes a social judgment (Liberman and Chaiken 1996; Sorrentino et al. 1988). When an attitude-object has high personal relevance, individuals process information about it in a more thoughtful, holistic way; when an object is of low relevance, individuals tend to rely on simple rules or heuristics to create judgments (Sorrentino et al. 1988). Personal relevance therefore may activate a different set of information to create ones attitude. Regarding attitudes toward technology, this implies that when the technology is more personally relevant (e.g., biotechnology for womens health), an individual will form judgments in different ways than when it is of low relevance (e.g., space technology).

Yet, it is not simply that a technology is perceived as more or less personally relevant; when a technology has further subjective implications for someone as a potential user, we expect they might be more thorough in their evaluations, drawing on various types of information in creating judgments. We describe this as technological saliencethe subjective implications of a specific technology for ones lived experience. When a technology becomes more salient, it likely sensitizes individuals to consider the implications of a technology from a wider range of perspectives (e.g., scientific, religious, or ethical), thinking about how the technology can affect their lives.



Biotechnology for infertility arguably becomes salient when a woman has experienced a biomedical barrier to fertility. This makes the technology more personally relevant because she might actually use it. We would expect, then, that women who have ever had a biomedical barrier to fertility would use different criteria in forming their attitudes compared to other women. Strickler made a similar observation, addressing how clinicians versus infertility patients evaluated treatments in fundamentally different ways: Clinicians viewed infertility in terms of diseases, which lend themselves to medical cures (1992, 113), while patients considered psychological and emotional consequences of treatments. Here, differences in personal histories with and relationships to this technology gave rise to different criteria by which it was evaluated. Women for whom reproductive technologies had greater personal implications drew on a more extensive set of subjective evaluative criteria than the clinicians who administered but did not directly experience them.

It is important to note here that the salience of infertility technologies is itself deeply gendered, shaped by gendered assumptions within medicine as an institution: Historically there has been a greater emphasis on infertility as a womens issue and much less focus on male infertility (Sandelowski 1993). Women become patients not only by being infertile but also by belonging to an infertile couple: Treatments for male infertility are overwhelmingly applied to womens bodies (Greil 2002). Therefore, technological salience is not gender-neutral: Medical conditions and technological solutions are socially produced and gender norms greatly influence whose bodies are subject to treatment (Bray 2007).

Experience, Beliefs, and Values

What are the different evaluative criteria that technological salience might activate? Research on attitudes points to personal experience with the attitude-object (both direct and indirect) as an important factor in attitude formation (Fazio, Eiser, and Shook 2004). Additionally, attitudes do not exist in isolation, but are typically built on other belief and value orientations (Stern et al. 1995). For our study, we suggest that three main constructs tap into these: reproductive experience and values, technological experience, and contextual beliefs and values.

Reproductive experience and values. Womens reproductive histories and values are likely to be highly relevant in shaping their attitudes about reproductive technologies. Women differently contemplate pregnancy;



have/have not experienced pregnancy; and place different levels of importance on motherhood. Because there is a general consciousness that fertility technologies are becoming more available and more widely used (Broekmans et al. 2007; Jade Martin 2010), we might expect that any women who are currently pregnant, have been pregnant, or are contemplating pregnancy may be more aware of these technologies. However, reproductive experiences and values may be more impactful for women with a biomedical barrier to fertility. If women have been or are planning to become pregnant and also have a biomedical barrier, then infertility technologies become more relevant compared to women who can conceive without intervention. We consider reproductive experiences and values as distinct from technological experiences (see following) because women may not have experience with infertility and related technologies, but their personal situations make reproductive technology in general more relevant to their lives.

Technological experience. Having personal experience with a particular technology or knowing others who have can be important forms of knowledge shaping attitudes about medical technology (Gabe and Calnan 1989). Women who have had a biomedical barrier to fertility can draw on personal experience if they have sought any medical help. Women can also draw on indirect knowledge from social networks. In addition, women may have more confidence in the technology if they know a friend or family member who has successfully conceived from it; on the other hand, they may evaluate it more negatively if they know someone who has had unsuccessful treatment. When technological salience is high, we expect that both direct and indirect technological experiences will predict attitudes because women might be more likely to draw on that information. In contrast, when technological salience is low, women might be less likely to use (indirect) experiences because the experiences and the technology have a weaker connection. Rather, we would expect them to rely more on dominant cultural interpretations of the technology (i.e., simple rules for forming attitudes; Sorrentino et al. 1988).

Contextual beliefs and values. Reproductive processes are heavily influenced by broader belief systems and values that individuals hold. These might be important predictors of attitudes when technological salience is high because we expect that women would be more likely to consider how the technology might be assimilated into their lives. Assisted reproduction raises many ethical concerns, which may make women cautious, especially if scientists are perceived as playing God (Kalfoglou



et al. 2005). These are often rooted in religious beliefs, which are frequently at odds with science and technology, including reproductive interventions (Roudsari, Allan, and Smith 2007). Because most religions oppose some form of reproductive intervention, religiosity may be more important in shaping womens attitudes. Womens personal locus of control may also factor in: Women who have a greater sense of personal control over their health and wellness may be less likely to place confidence in (external) medical authorities (Kornelson 2005; Nelson 1983). Finally, reproduction and family are highly gendered institutions that call on broader belief systems about men and women. Given the male-dominated character of science, technology, and medicine (Hayes 2001), we expect that women who espouse more traditional gender attitudes may also be more inclined to accept traditionally construed patriarchal medical authority.

Social Location

A final element to consider is how social structural location affects attitudes (Kiecolt 1988). While we do not necessarily suspect that the importance of social location varies by level of technological salience, it is still essential to consider this for attitudes toward fertility technologies. The concept of stratified reproduction relates womens social location to experiences of reproduction (Colen 1986; Rapp 2001). Depending on social class, race, age, and marital status, among other factors, women are differentially empowered and encouraged to have/not have children, which likely affects their attitudes about reproductive technologies. Both imagined and actual U.S. fertility patients tend to be middle-class, white, heterosexual, married women. Infertility services are concentrated in a private medical market, so resources such as (private) insurance and income are key to access (Bell 2009; King and Meyer 1997). White women are much more likely to seek infertility help than Black and Hispanic women (Greil et al., 2011; Stephen and Chandra 2000); this is often interpreted via the history of distrust between Black and Hispanic groups and the U.S. medical system (Dovidio et al. 2008). Asian women are more similar to white women in their attitudes; however, recent research (Greil et al., 2011) indicates they may have greater ethical concerns about treatments. Age is both biologically and culturally significant because of the natural decline in womens fecundity (Broekmans et al. 2007; Friese, Becker, and Nachtigall 2006). Infertility technologies may resonate more with older women. However, they may also be more doubtful about the technologys efficacy compared to younger women,



who might take fertility technologies for granted. Finally, infertility has historically been defined and treated as a medical problem within a married couple (Sandelowski 1993)typically excluding single and lesbian women; however, more recent technology for egg freezing targets single, career-driven women to protect their future fertility (Jade Martin 2010). Therefore, while being married or in a heterosexual relationship may prime women to be more aware of these technologies, single women are also increasingly targeted for particular procedures.

To summarize, prior research has found gender differences in attitudes toward science and technology but has tended to assume within-gender experiences are similar (e.g., gender socialization produces womens pessimism or viewing all womens bodies as implicated in childbearing). Drawing on feminist insights that women do not universally experience and relate to technology and on social-psychological literature on attitudes, we argue that technological salience may be a key, overlooked concept in theorizing womens attitudes about technology. When a technology becomes salient, we expect this will activate a wide range of individual experiences, beliefs, and values as criteria to evaluate the technology: These same factors might be less likely to affect attitudes when technological salience is low.

Reproductive interventions are an ideal subject with which to test the technological salience hypothesis because they are potentially applicable to all women, yet only some women require these interventions. Biotechnologies for infertility have more direct implications for women with a biomedical barrier to fertility than for other women. We expect that reproductive experiences and values, direct and indirect experiences with the technology, and other contextual beliefs and values will be associated with attitudes toward biotechnology for women who have had a biomedical barrier to fertility but not for other women.

mETHOD

Data

Data came from wave one of the National Survey of Fertility Barriers (NSFB)a random-digit-dialing telephone survey that addresses social and psychological aspects of childbearing and fertility problems among a nationally representative sample of U.S. women and a subset of partners (see Johnson and White 2009 for methodology report). Interviews were completed with 4,712 women between September 2004 and December



2006. There was a 53% percent response rate to the initial screener and 37.2% percent response rate to the final survey. Although this is relatively low, a comparison of key demographic and reproductive measures with two other national surveys with high response rates (National Survey of Family Growth and Current Population Survey) showed similar distributions of race/ethnicity, age, marital status, SES, and several fertility variables, including ever pregnant, ever infertile, and ever sought infertility treatment. To be interviewed, women had to be ages 25 to 45 and respond to introductory questions about wanting additional children, having ever been pregnant, and having ever experienced fertility problems. Women who had already had one child, did not want any more children, and had never experienced fertility problems were undersampled (20 percent randomly selected for full interview). Census tracts with 40% percent or more racial/ethnic minorities were oversampled to facilitate analyses by race/ethnicity.

For this study, we divided the sample into two groups for comparison: women who have ever had a biomedical barrier to fertility and women who have never had a barrier. This was constructed from self-reports in response to several questions. There were three main barrier pathways: (1) reporting a pregnancy (intended or unintended) that took more than one year to conceive, while not using birth control; (2) reporting one or more miscarriages; and (3) reporting another type of medical problem, such as sterilization prior to having desired children. Because of the diversity of womens experiences, we controlled for type of barrier in models for those women (described below). Because objective or medically defined measures of infertility do not necessarily address subjective experiences, we also included a subjective measure of fertility problems (described below). Lesbian women can also be defined as having a fertility barrier and are likely to use assisted reproductive technologies when desiring biological children (Agigian 2004); however, because only 45 respondents self-identified as lesbian, controlling for this was not feasible.

After listwise deletion, the samples contained 2,887 (89.6% percent of the original sample) and 1,367 (87% percent of the original sample) women for those with and without a biomedical barrier to fertility, respectively. We compared characteristics for which there were complete data for both the original and analytic samples. There were similar distributions for age, race, parental status, most reproductive characteristics, and most technological experience characteristics. This suggested that the analytic sample did not dramatically differ from the original NSFB sample. In analyses, we weighted to adjust for nonresponse and sampling design.



measures

Outcome. The main outcome was womens confidence in biotechnology for infertility. This was constructed from two ordinal items: (1) Medical science can be a big help to women having trouble getting pregnant and (2) Women who have trouble getting pregnant would benefit from consulting a doctor (polychoric correlation = .43 for women with a biomedical barrier and .57 for women without). Items had four response categories (strongly agree to strongly disagree). These were initially part of a three-item scale with a planned missing design. Respondents were randomly asked two-thirds of the items to reduce respondent burden without compromising concept validity (Johnson and White 2009). Items were missing completely at random, so they do not bias the results (Allison 2002). We used singly imputed versions of items (from the NSFB public data) to construct our measure. We used the two-item scale because the third item was substantively different: It measured the extent to which respondents believed women could have children in their late thirties and beyond, with the help of modern medicine. Because the topic of advanced maternal age is associated with numerous ethical and medical concerns (e.g., increased risk of birth defects and complications), we excluded this in favor of addressing more general attitudes about medical intervention for infertility.

Social location control variables. While our analyses focus on differences in experiences, values, and beliefs, these are likely to be correlated with social location. We controlled for age in years. We indicated marital status comparing married (= 1) to all other statuses (= 0). We used a dichotomous measure of race (white = 1; nonwhite = 0). Because women could report multiple racial categories, anyone who reported at least one nonwhite category was coded as nonwhite. In preliminary analyses, we analyzed four categories (Black, Hispanic, Asian, and white) but found no significant differences, so we present the simplified measure here.

We used five variables for socioeconomic status: health insurance (private = 1; else = 0), state-mandated insurance (living in state with mandated infertility coverage = 1; else = 0), family income (12 categories,


not a personal characteristic, it potentially mediates between personal insurance and access.

Reproductive experiences and values. We included five measures to address womens reproductive experiences and values. We gauged parental status (= 1 if biological or social parent; = 0 if neither). Social parenting included adoption, stepchildren, and formal/informal fostering. An exclusive measure of biological parenthood showed no substantive difference in preliminary analyses. We indicated whether women were currently pregnant or trying to get pregnant (= 1) compared to women who were not trying or ambivalent (= 0). We indicated whether a woman had ever been pregnant (= 1) or not (= 0). We indicated whether women would like a(nother) baby in the future (= 1 if probably or definitely yes; = 0 if probably or definitely no). We also included the Importance of Parenthood Scale, which was composed of the following: (1) Having children is important to feeling complete as a woman; (2) I always thought I would be a parent; (3) Life . . . more fulfilling with children; and (4) It is important for me to have children. Items had four response categories (strongly disagree to strongly agree). A high score reflected a greater value of parenthood ( = .72 for women with a biomedical barrier, .78 for women without).

For women with a biomedical barrier, we included additional measures of their experiences in an extended model. We coded a respondent as self-identifying as having fertility problems (= 1) if they answered yes or maybe to the following: (1) Do you think you have/have had/might have trouble getting pregnant? (2) Do you think you have/have had a fertility problem? We indicated whether problems occurred with a first pregnancy (= 1) versus subsequent pregnancies (= 0). We controlled for type of fertility barrier: 12-month infertility and trying to conceive (infertile with intent), 12-month infertility but not trying to conceive (infertile no intent), miscarriage only, other medical issues (e.g., surgery, illness, sterilization of self/partner, and desire to have a child). We separated infertility into the intent/no intent groups because these women often have very different reactions (Greil and McQuillan 2004).

Technological experience. Several variables measured womens technological experiences. We indicated whether they knew any friends or family who had used medical help to conceive: (1) Have family or friends pursued medical help to get pregnant? (2) Did any have a baby as a result of [medical] help? We recoded these into three dichotomous variables: (1)



did not know anyone; (2) know someone, they were unsuccessful; (3) know someone, they had a baby.

In an extended model only for women with a biomedical barrier, we included a measure assessing the level of medical help seeking: no help seeking (= 0), considered only (= 1), talked to a doctor (= 2), had evaluation/diagnosis (= 3), and had treatment for the problem (e.g., artificial insemination) (= 4). If women had treatment, we further indicated whether it was successful, meaning it resulted in a current pregnancy or live birth (= 1); or unsuccessful, meaning no pregnancy, miscarriage, or stillbirth (= 0).

Contextual beliefs and values. The final set of variables included various beliefs and values relevant to family and reproduction. We indicated womens ethical concern about infertility treatments via a three-item scale asking about artificial insemination, donor insemination, and in vitro fertilization. Three response categories ranged from no ethical problem to serious ethical problems ( = .72 for women with a biomedical barrier, .77 for women without). We included a six-item scale for medical locus of control. This assessed a womans perception of self-control over her health and well-being with statements such as If I am sick, my own behavior determines how soon I get well again. Each item had four response categories ranging from strongly disagree to strongly agree ( = .72 for women with a biomedical barrier, .68 for women without). We included a four-item scale measuring religiosity. This assessed frequency of religious behaviors, and how close they felt to God ( = .52 for women with a biomedical barrier, .57 for women without). Each item had different response categories; these were standardized before scaling. Finally, we included a measure of gender beliefs that assessed attitudes about men and women having separate spheres of responsibility for work and family life. The item had four response categories, ranging from strongly agree to strongly disagree: a high score indicated more traditional attitudes.

Analytic Strategy

Our main hypothesis is that because biotechnology for infertility has more personal implications for women who have had a biomedical barrier to fertility, they will use different criteria for appraising it than other women. To test this, we split our sample by biomedical barrier status because we suspect not just that the coefficients between these two groups will be unequal (i.e., interaction effects) but that entirely different factors may be



at work in terms of what is shaping their attitudes about infertility technologies. We estimated both linear and ordinal logistic regression models because our outcome of interest was ordinal with seven categories. Results were substantively similar, so we present linear results for ease of interpretation.

RESULTS

Table 1 displays descriptive statistics by biomedical barrier status. Bivariate associations show significant differences across each construct for these two groups of women. Notably, women who have never had a biomedical barrier have significantly more confidence in biotechnology for infertility on average than women who have ever had a biomedical barrier. This supports suggestions in prior research that women may be more skeptical of science and technology when their bodies are the focus of intervention (Bryant and Pini 2006; Nelkin 1981). These groups are similar across only three characteristics: percentage who know someone that sought medical help for pregnancy and did not have a baby; percentage who know someone that sought medical help and had a baby; and average medical locus of control score. The series of differences here further justify splitting our analytic sample. These differences also suggest that biomedical barrier status is potentially associated with different life course trajectories: Women with a biomedical barrier are more likely to be mothers, more likely to have experienced pregnancy or are trying to become pregnant, rate motherhood as more important, are more religious, and have more traditional attitudes. They also rate lower across all of the SES variables on average. It is possible that these characteristics place women in a position to test their fertility earlier on and discover problems.

Table 2 shows linear regression results only for women with a biomedical barrier. Each successive model adds variables measuring the contributions of each construct in our framework. Model 1 controls for social location. Race, education, and income are all positively associated with womens confidence in biotechnology in model 1, but in model 5 only the effects for race and education hold. White women express greater confidence in this biotechnology than non-white women, and more educated women express greater confidence than less educated women. These are both in the expected direction, based on prior research finding a link between higher education levels and greater acceptance of science and technology (e.g., Hayes 2001; Simon 2010), and research suggesting that non-white women may be less trusting of the medical system or have


274

TAB

LE 1

: Des

crip

tive

Stat

istic

s by

BM

B St

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a

BMB

(n =

28

87)

No

BMB

(n =

13

67)

M

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SDR

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Mea

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%SD

Ran

gepb

Conf

iden

ce .

. . bi

otec

hnol

ogy

(scale

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90.

902-

87.

00.

903-

8*

Socia

l loca

tion

Ag

e (ye

ars

)36

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9025

-45

34.5

5.95

25-4

5***

M

arrie

d64

.663

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W

hite

58.3

66.2

***

Ed

ucat

ion

(years

)13

.32.

810-

2214

.33.

150-

22***

Pr

ivate

insu

rance

63.8

78.2

***

In

com

e (ca

tegori

es)

7.6

3.14

1-12

8.6

2.83

1-12

***

Em

ploy

ed

full-

time

51.9

60.8

***

Rep

rodu

ctive

exp

erie

nces

and

valu

es

Pare

nt

89.6

75.2

***

Tr

ying

8.2

4.5

**

Ev

er

preg

nant

91.9

76.2

***

W

ants

a(no

ther)

baby

39.4

40.6

***

Im

porta

nce

of p

aren

thoo

d (sc

ale)

3.3

0.56

1-4

3.2

0.63

1-4

**

Se

lf-id

entif

icatio

n . .

. fe

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y pr

oble

ms

20.4

Fi

rst p

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ancy

pro

blem

40.9

Ba

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: in

ferti

le, w

ith in

tent

35.9

Ba

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: in

ferti

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: m

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5

(Con

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275

Tech

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Kn

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ed

help

(no b

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13.5

17.8

Kn

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help

(bab

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.333

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Hel

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g (le

vel)

1.0

1.48

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Su

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t10

.9

Unsu

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trea

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t5.

2Co

ntex

tual

bel

iefs

and

valu

es

Ethi

cal c

once

rn (s

cale)

9.3

2.58

6-18

8.8

2.55

6-18

***

R

elig

iosit

y (st

anda

rdize

d sc

ale)

0.2

2.69

()9.3

-4.3

().54

2.92

()9.3

-4.3

***

Lo

cus

of c

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l (sca

le)17

.92.

587-

2417

.72.

2210

-24

Se

para

te s

pher

es (h

igh sc

ore =

tradi

tiona

l)2.

50.

921-

42.

30.

901-

4**

a. W

eig

hted

by

NSFB

sur

vey

desig

n an

d po

pula

tion

weig

hts.

b. D

iffere

nce

of m

eans

test

s pe

rform

ed

for

ord

inal

and

con

tinuous

varia

bles

, c2

te

sts

for

cate

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al.

*p

< .

05, *

*p

Johnson Simon Biomedical Technology Infertility

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Transcript of Johnson Simon Biomedical Technology Infertility