The Effects of Socioeconomic Status,Perceived Discrimination and Mastery

on Health Status in a Youth Cohort

Richard K. Caputo, PhD

ABSTRACT. This study examined the influence of socioeconomic sta-tus, perceived discrimination, and sense of mastery over one’s life on thehealth status of a sub-sample of a US cohort of youth (N = 969). Whencontrolling for a variety of social characteristics and personal attributes,only sense of mastery over one’s life, measured by the Pearlin MasteryScale, affected physical and mental health statuses. Perceived discrimi-nation affected only mental health status, while SES over the life courseaffected only physical health. Findings affirmed the efforts of profes-sions like social work that stress self-determination and empowermentenabling individuals to enhance their own social functioning and im-prove conditions in their communities and in society at large. They alsosuggested that in regard to mental health advocacy efforts to decreasehealth disparities can find social justice related grounds based on gender.[Article copies available for a fee from The Haworth Document Delivery Service:1-800-HAWORTH. E-mail address: <docdelivery@haworthpress.com> Website:<http://www.HaworthPress.com> © 2003 by The Haworth Press, Inc. All rightsreserved.]

KEYWORDS. Discrimination, health disparities, mastery of one’s life,social determinants of health, social justice

Richard K. Caputo is affiliated with the Yeshiva University, Wurzweiler School ofSocial Work, Belfer Hall, 2495 Amsterdam Avenue New York, NY 10033-3299(E-mail: caputo@ymail.yu.edu).

Social Work in Health Care, Vol. 37(2) 2003http://www.haworthpress.com/store/product.asp?sku=J010

2003 by The Haworth Press, Inc. All rights reserved.10.1300/J010v37n02_02 17

This study examines the influence of socioeconomic status (SES),perceived discrimination, and sense of mastery over one’s life on thehealth status of a US cohort youth. The author takes a life course per-spective of SES to smooth out episodic fluctuations of annual income,thereby providing a more accurate gage of economic well-being thanSES based on snapshots of annual income characteristic of cross-sectional studies. Perceived discrimination invariably signifiesstressful circumstances whose effects on health may go well beyondimmediate situations and have long-term adverse consequences. Asense of mastery over one’s life is an important psychological attributethat buffers stress and hence contributes to physical and mental health.The study focuses on maturing youth in part because few others do soexclusively. Among those that do many equate health status with mor-tality (e.g., Geronimus, Bound, Waidmann, Colen, & Steffick, 2001),while others studies often rely on aggregated rather than individual-orfamily-specific data (e.g., Furstenberg & Condran, 1988). For the mostpart, this study excludes youth who were reported as non-respondentsdue to death between 1980 and 1998 because (1) the causes of deathwere not available and (2) accidents and homicides, which generally oc-cur in circumstances that fall beyond the scope of health-related issuesper se, are among the leading proximate causes of death among adoles-cents (Singh and Yu, 1996).

Specifically, this study seeks to answer the following questions:

1. How do SES, perceived discrimination, and sense of mastery overone’s life affect health beyond that of heredity, adolescent familystructure, lifestyle, marital history, psychological attributes, race/eth-nicity/sex, and other factors?

2. Does discrimination have long-term effects on an individual’sphysical and/or mental health?

3. To what extent are structural factors or situational characteristicsvis-à-vis personal attributes more or less likely to influence physi-cal and/or mental health?

Answers to these questions will provide policy makers, social workers,and others interested in improving the health status of vulnerablegroups and/or reducing health disparities between advantaged and dis-advantaged groups of people with ways of assessing the merits of argu-ments for systemic or case-based remedial actions.

18 SOCIAL WORK IN HEALTH CARE

LITERATURE REVIEW

There is a vast literature in regard to the influence of socioeconomicand other factors on health status. Comprehensive summaries can befound in Daniels, Kennedy, and Kawachi (1999), Marchand, Wikler,and Landesman (1998), Williams and Collins (1995), and Kreiger,Rowley, Herman, Avery, and Phillips (1993). Four central findings inthe empirical literature guide the present study. First, although specificproximate causal factors on health outcomes are disputed (Marmot &Wilkinson, 2001), disparate impact of demographic and socioeconomicfactors is fairly well established (Caputo, in press; Cattell, 2001; Ken-nedy, Kawachi, Glass, & Prothrow-Stith, 1998). Nonetheless, as Danielset al. (1999, p. 218) note, the observed income/health gradients do not re-sult from fixed or determinate laws of economic development, but are in-fluenced by policy choices.

The second general finding is that the income/health gradients oper-ate across the socioeconomic spectrum within societies, with the steep-ness of the gradient affected by the degree of inequality. Third, relativeSES is as important as absolute levels of income in determining healthstatus-at least once societies have passed a certain threshold, which theUnited States easily does. And fourth, there are identifiable determi-nants of health, in addition to socioeconomic status, amenable to policychoices that can be guided by considerations of justice (Daniels, 1981).

In addition to SES, race and ethnicity also remain potent predictors ofhealth status, with blacks and Hispanics having higher rates of many ill-nesses compared to whites (Castro, 1993; National Center for HealthStatistics, 1994; Williams & Rucker, 2000). Further, adjusting for SESsubstantially reduces but does not eliminate racial disparities in health(Cooper, 1993). That is, within each level of SES, blacks generally haveworse health status than whites (Rogers, Hummer, Nam, & Peters,1996; Schoendorf, Hogue, & Kleinman, 1992).

A small but growing body of evidence suggests that the experience ofracial, gender, and, by extension, age, sexual orientation, and otherforms of discrimination are adversely related to a broad range of healthoutcomes (Kreiger et al., 1993; Williams, 2000; Williams & Collins,1995). Related research focuses on the “isms,” e.g., racism, sexism, butthese contributions emphasize social relations, defined mainly by thestructure of the economy, as highly determinative of health experiencesamong blacks and, by extension, women and other groups of people,while often ignoring case-specific incidents of discrimination (e.g.,Cooper, 1993). Williams and Williams-Morris (2000) provide a notable

Richard K. Caputo 19

exception, linking institutional racism, exemplified by the associationof higher concentrations of black Americans in segregated residentialareas and lower SES, with physical and mental health problems. Theyalso summarize several studies linking subjective experiences of dis-crimination to psychological distress by, for examples, black Ameri-cans (Ren, Amick, & Williams, 1999), gay men in New York City(Meyer, 1995), and immigrants in southern Florida and San Diego(Rumbaut, 2000).

Several studies report the influence of early life socioeconomic andhealth conditions on long-term consequences for an adult’s health sta-tus, although noticeably absent is the influence of family structure (e.g.,Elo & Preston, 1992). Yet other studies point to the independent influ-ence of neighborhood or community effects on health status (e.g., Cattell,2001; Geronimus, Bound, Waidmann, Colen, & Steffick, 2001).

The study reported here differs from most other studies that have ex-amined the relationship between socioeconomic factors and health inseveral ways. First, unlike, for example, Hebert, Hurley, Olendzki,Teas, Ma, and Hampl, 1998, who used aggregated data, this study reliesprimarily on individual-specific variables, while incorporating one ag-gregated measure (the unemployment rate) about the communities inwhich the subjects lived at the time of survey. In doing so, it nonethelessviews SES as a function of the structural characteristic of the economyand the nuclear-centered family form (e.g., married, unmarried) as astructural characteristic of society (Lynch et al., 1998). The use of indi-vidual-specific data and the focus on perceived discrimination, how-ever, is seen as a theoretical advance over many previous studies bylinking micro-level data with larger structural characteristics of theeconomy and the nuclear-centered family form. Theoretical scholars inthe social sciences (e.g., Dawson, 1994; Turner & Boyns, 2001) stressthe need for more empirical work that connects micro and macro levelsof reality. By taking advantage of the individual-specific data, thisstudy contributes to our theoretical understanding of role of perceiveddiscrimination in health status. Second, the study examines a US na-tional sample of male and female youth, whereas many studies that relyon aggregated data focus on race or class to the exclusion of gender(e.g., Grant, Lyttle, & Weiss, 2000) and many that employ individ-ual-specific variables either focus primarily on men (e.g., Smith,Neaton, Wentworth et al., 1996) or rely on localized samples (e.g.,Salgado de Snyder, 1987).

The third way this study differs from others is by using, as Williamsand Collins (1995) suggest, longitudinal panel data, rather than cross

20 SOCIAL WORK IN HEALTH CARE

sectional data, which form the basis of many related studies (e.g., Ren,Amick, & Williams, 1999; Williams, 2000) that rely on single-year in-dicators. In doing so, this study takes advantage of information obtainedover the life course and constructs cumulative variables that are primar-ily structural (family form, unemployment rate in area of residence) innature. In their longitudinal studies of working men in Scotland, Hart,Smith, and Blane (1998) use only one cumulative variable, a measure ofsocial class, and report that socioeconomic factors acting over the life-time affect health and risk of premature death. Although education levelmay serve as a proxy for social class, this study, while controlling foreducation, uses SES over the life course to smooth out episodic swingsof annual income characteristic of adolescents and young adults as theymove into the labor force and form their own families as is the case withthe study sample.

The fourth way this study differs from others is that it incorporatesseveral lifestyle behaviors associated with substance use and early sex-ual activity. In their pooled cross-sectional study of factors affectinghealth, Rogers et al. (1996) employed only one behavioral measure,namely smoking. Fifth, the present study identifies and controls for ex-posure to both distal (i.e., early life exposures like parents’ family sta-bility) and proximate measures (e.g., use of drugs) thought to affecthealth. Spencer (2001) has called for more life course studies that exam-ine the role of early life exposures as well as multiple exposures of othersocial determinants of health. Fifth and finally, this study differs frommany others by examining the influence of psychosocial factors onhealth status (not the opposite) while controlling for SES and other cor-relates of health. Following Bosma, Marmot, Hemingway et al. (1997)and Marmot and Wilkinson (2001), the study seeks to discern the predic-tive power of psychosocial factors on health over and beyond that of socio-economic gradients. Theoretically, the study focuses on the psychosocialfactor of people’s sense of mastery over their lives. Pearlin, Lieberman,Menaghan, and Mullen (1981) have shown that the self-concept of masterydirectly buffers the ill effects of stressful life events, compared to the indi-rect effects of coping and social supports. As previously noted, the pres-ent study seeks to discern the relative influence of discrimination as astressor and the self-concept of mastery as a buffer on health status whencontrolling for a variety of other sociodemographic, personal, and struc-tural factors that a cohort of youth experienced as they entered the labormarket and formed their own families.

Richard K. Caputo 21

METHODS

Data and Subjects

Data for the study were obtained from the 1979 cohort of the Na-tional Longitudinal Survey of Youth (NLSY79), a nationally represen-tative sample of 12,686 noninstitutionalized youth in the US aged 14 to21 as of December 31, 1978. For the 1998 survey, the most recent avail-able at the time of this study, 8,399 respondents were interviewed, a66.2% unweighted retention rate (79.0% weighted). Respondents in1998 differed on several sociodemographic measures from those in1979, with the major difference in annual family income ($16,726 vs.$10,195). In 1979 they were also slightly younger (17.6 vs. 17.9 yearsold), less educated (10.3 vs. 10.5 years of schooling), from larger fami-lies (4.70 vs. 4.26 members), with proportionately more blacks (14.3%vs. 13.6%, weighted) and proportionately more women (51.4% vs.49.2%, weighted). Results and recommendations were made with thesedifferences in mind. Documentation about the national sample wasfound in the NLS Handbook 1999 (Center for Human Resource Re-search, 1999a) and the NLSY79 User’s Guide 1999 (Center for HumanResource Research, 1999b).

The NLSY79 is highly suited for this study because in 1998 respon-dents 40 years old and over (n = 1,328) were administered an extendedhealth module to provide insight into chronic health problems thatmight affect their labor market activity in the future. The health moduleincluded a variety of measures related to respondents’ physical andmental health, and to a lesser extent the physical health of their biologi-cal parents, as well as in regard to their lifestyles.

The study sample (n = 969) is drawn from those in the populationsample who were 40 years and over in 1998. It includes only thoseabout whom all relevant information, except as noted, was available.Mean values based on the entire population sample by race/ethnic-ity/sex were assigned to two measures, family income used to as a com-ponent of SES and age of first sexual intercourse, whose values werereported as missing in the study sample.

Measures

Table 1 identifies and defines the measures used in this study. Thedependent measures are Mental Health Status and Physical Health Sta-tus. They represent summary scores of the SF-12 Scale, a 12-question

22 SOCIAL WORK IN HEALTH CARE

health survey designed by John Ware of the New England Medical Cen-ter Hospital. The SF-12 measures respondents’ mental and physicalhealth irrespective of their proclivity to use formal health services. Itcomprises two components, the MCS-12, the Mental Component Sum-mary, and the PCS-12, the Physical Component Summary. In large na-tional surveys of the entire US population, both the MCS-12 and thePCS-12 have a mean of 50 and a standard deviation of 10. NLSY79 re-spondents with a score above 50 have better health than the typical per-son in the general US population (age is not held constant). NLSY79respondents with scores below 50 have worse health than the typical USperson (Zagorsky, 2001). Additional information about the SF-12 scalecan be found in the Center for Human Resource Research (1999b) andfrom the Medical Outcomes Trust (20 Park Plaza, Suite 1014, Boston,MA 02116-4313 or http://www.outcomes-trust.org/).

The main independent measures of concern are SES, perceived dis-crimination, and one’s sense of mastery over one’s life. SES reflects re-spondents’ average annual income-to-poverty ratios (IPRs) between1979 and 1998. IPR is a function of respondents’ reported family in-come and the annually adjusted US poverty thresholds that take into ac-count family size. Respondents who reported $0.00 family income areassigned an annual nominal income of $1.00.

Perceived discrimination (ANYDISC) is a dichotomous measurethat captures respondents’ answers to questions about their experienceswith hiring-related discrimination as they sought good jobs. This mea-sure, comprising specific incidents of perceived discrimination, is mul-tidimensional in the sense that incidents of discrimination were basedon race, nationality, sex, or age, but it was nonetheless confined to onesituation, namely experiences seeking good jobs, rather than to severaldifferent situations like at school or work, or getting housing, or fromthe police (Krieger, 1990). Other forms of generalized or structural dis-crimination are accounted for among the background, cumulative, andother measures of social relationships defined below.

The third independent variable of concern (MASTERY) captures asense of mastery or control over one’s life. There is evidence thatpsychosocial factors like sense of powerlessness and perceptions ofmastery affect one’s health (Kessler, House, Anspach, & Williams,1995). The Pearlin Mastery Scale employed here is a commonly usedmeasure that has been found to have a direct buffering relationship tostressful life events, compared to indirect buffers such as the interven-tions of coping and social supports (Pearlin, Lieberman, Menaghan, &Mullan, 1981). A summary of the items that constitute the scale, its va-

Richard K. Caputo 23

TABLE 1. Definitions of Study Variables

Variables Definition

Dependent measures–Health status

Mental health status 1 = MCS-12 score < 50, i.e., worse than typical US person, 0 = MCS-12 score > 50, i.e., better than typical US person

Physical health status 1 = PCS-12 score < 50, i.e., worse than typical US person, 0 = PCS-12 score > 50, i.e., better than typical US person

Independent measures of main concern

ANYDISC 1 = working-age respondents (i.e., 16 and over) who reported that they believed specific types of discrimination(race, nationality, sex, or age) had caused them problems in getting a good job in 1979 and/or 1982, 0 = Other, i.e.,no reports of such discrimination

MASTERY Pearlin Mastery Scale scores obtained in 1992

SES Respondents' average annual income-to-poverty ratio between 1979 and 1998

Other independent measures

Heredity

Respondent's father's health status 1 = father either died from health problems or reported to have/had health problems, 0 = father has/had no healthproblems

Respondent's mother's health status 1 = mother either died from health problems or reported to have/had health problems, 0 = mother has/had no healthproblems

Background

Age of respondent Respondent's age in 1998

HGC Respondents' highest grade completed by 1998

Respondent's family structure at age 14

Two-biological-parent family 1 = at age 14 respondent lived with biological mother & father, 0 = other, Reference

Two-non-biological-parent family 1 = at age 14 respondent lived with two parents, one or both of whom are not the respondent's biological parent,0 = other

Single-parent family 1 = at age 14 respondent lived with a single parent, 0 = other

Poverty status in 1979 1 = family income fell at or below the US poverty threshold, 0 = above the threshold

24

Variables DefinitionRace/ethnicity/sexWhite Male 1 = yes, 0 = no, ReferenceBlack Male 1 = yes, 0 = noHispanic Male 1 = yes, 0 = noWhite Female 1 = yes, 0 = noBlack Female 1 = yes, 0 = noHispanic Female 1 = yes, 0 = noRegion lived in 1979South 1 = yes, 0 = no, ReferenceNortheast 1 = yes, 0 = noNorth central 1 = yes, 0 = noWest 1 = yes, 0 = noUrban environment at age 14 1 = at age 14 respondent lived in a city or town, 0 = other (country, farm, or ranch)Psychological/AttitudinalLocus of control Rotter internal-external locus of control scale scores obtained in 1979Self-esteem Rosenberg self-esteem scale scores obtained in 1980 and 1987LifestyleSexual Intercourse (SI) Age respondents reported as having had their first sexual intercourseSubstancesAlcohol 1 = afraid that they might become an alcoholic, 0 = no such fearCigarettes 1 = a daily smoker who smoked at least 100 cigarettes in his/her lifetime, 0 = otherCocaine 1 = used cocaine in their lifetime, 0 = never used cocaineCrack cocaine 1 = used crack cocaine in their lifetime, 0 = never used crack cocaineMarijuana 1 = used marijuana in their lifetime, 0 = never used marijuanaOther/cumulative/structuralAverage CC The average number of years respondents lived in center citiesAverage OLF The average number of years respondents reported that they were out of the labor forceAverage UR The average annual unemployment rate in respondent's area of residence at the time of surveyNever examined by a physician 1 = respondents who reported that they were never examined by a physician, 0 = otherNever insured 1 = respondents who reported that they never had health insurance, 0 = otherYears married The number of survey years respondents reported their marital status as married25

lidity and reliability, and scoring can be found in Center for Human Re-source Research (1999b). The Pearlin Mastery Scale was administeredin 1992, with higher scores signifying a greater sense of mastery.

In addition to SES, perceived discrimination, and sense of masteryover one’s life, five other major categories of independent variables re-flect those found in the literature to be significant determinants of healthstatus: heredity, background, psychological/attitudinal, lifestyle, andother, which includes cumulative, structural, and health care utilizationmeasures. Many of these measures are either self-explanatory or com-monly used, although elaboration of several specific measures is neces-sary. The two hereditary measures, mother’s and father’s health status,signify whether or not respondents’ biological parents either died fromhealth problems or were reported to have/had health problems. Thesetwo measures are meant to reflect potential genetic predispositions tomental and physical illnesses. Among the background measures of par-ticular interest for purposes of this study are the dummy variables con-structed for race/ethnicity/sex, with white males (WM) serving as themissing category in the multivariate statistical procedures described be-low.

Other background measures are used primarily as controls. These in-clude whether or not respondents lived in an urban environment at age14, lived in the South in 1979, lived in a poor family in 1979, as well asage of respondents, highest grade completed by respondents, and fam-ily structure of respondents at age 14. The use of urban environment atage 14 is consistent with Geronimus et al. (2001) who show that resi-dents of urban poor areas in the US fare worse than their race-andsex-specific national average and worse than residents of rural poor ar-eas matched on race and gender. The use of the South as a dummy mea-sure is consistent with Lochner, Pamuk, Makuc et al. (2001) who showthat individuals living in high income-inequality states in the US facedhigher levels of health-related and other risks (e.g., higher crime rates)compared to individuals living in states with the lowest income inequal-ity. Because there has been so much public and scholarly debate overthe past several decades about the importance of family structure onchildren’s and adolescents’ well-being and because it has been ne-glected in the health-related literature, this study employs three dummyvariables to assess the influence of respondents’ having lived in sin-gle-parent or two-parent non-biological families compared to two-par-ent biological or intact families on health.

The lifestyle measures capture a range of substances that respondentsreported to have used in varying amounts over the course of their lives.

26 SOCIAL WORK IN HEALTH CARE

These substances included smoking cigarettes and consuming alcohol,as well as using marijuana, cocaine, and/or crack cocaine. Given thelink between early sexual activity and sexually transmitted diseases,lifestyle measures also capture the age respondents reported to have hadsexual intercourse for the first time (SI).

The other psychological/attitudinal measures include the Rotter Inter-nal-External Locus of Control Scale (Rotter, 1966) and the RosenbergSelf-Esteem Scale (Rosenberg, 1965). Like the Pearlin Mastery Scalethese two psychological/attitudinal scales are commonly used measures,although they are noticeably absent from many health-related epidemio-logical studies. In a related study, Caputo (in press) reports a positive re-lationship between mastery and both physical and mental health statutes,with self-esteem related only to mental health locus of control related toneither physical nor mental health status. Hence, while mastery istreated here as the more robust predictor of health status, self-esteemand locus of control are used more as controls. Summaries of the itemsthat constitute the Rotter Internal-External Locus of Control Scale andthe Rosenberg Self-Esteem Scale, their validity and reliability, as wellas scoring can be found in Center for Human Resource Research (1999b).The Rotter Locus of Control, administered in 1979, measures a respon-dent’s belief that things happen to oneself due primarily to fate (externalcontrol) or to one’s efforts (internal control), while the Rosenberg Self-Es-teem Scale, administered in 1980 and 1987, measures the self-evaluationof self-esteem that an individual makes and customarily maintains.

The final category of factors thought to influence health status com-prises four cumulative continuous measures and two dichotomous mea-sures. The cumulative continuous measures are: the average number ofyears respondents lived in center cities (Average CC), the average num-ber of years respondents reported that they were out of the labor force(Average OLF), the average annual unemployment rate in respondent’sarea of residence at the time of survey (Average UR), and the number ofsurvey years respondents reported their marital status as married (YearsMarried). The dichotomous measures, Never Insured and No PhysicalExam, are meant to capture heath care utilization.

Procedures

Chi-square, T-test, and ANOVA statistics were used to provide de-scriptive summaries of the major study variables. For dichotomousvariables, the Cochran-Mantel-Haenszel statistic was used with theChi-square to determine if respondents with a given characteristic or at-

Richard K. Caputo 27

tribute, such as having below-average PCS-12 scores, were more likelyto vary by hereditary, background, socioeconomic, and other characteris-tics, such as living in a biological two-parent family, than were below-av-erage PCS-12 scoring respondents in the absence of such characteristics(Cody & Smith, 1997).

Logistic regression analysis was used to determine (1) if SES, per-ceived discrimination, and one’s sense of mastery over one’s life add tothe predictive power of heredity, background, psychological/attitudinallifestyle, and other/cumulative/structural measures thought to influencethe likelihood of below average health status in survey year 1998 and(2) how the addition of SES, perceived discrimination, and one’s senseof mastery over one’s life affect the influence of these other variables onhow likely a respondents’ health status falls below the health status ofthe typical US person. Odds ratios were shown to indicate the likelihoodthat each measure has of predicting health status independently of otherfactors. Hence, if discrimination and perceived mastery reflect a re-spondent’s actual social position or actual opportunities or rewards,their influence over and beyond these other factors are accounted for tothe extent they are captured by the measures used in this study. Separateanalyses were used for physical health status and mental health status,measured by the dichotomous PCS-12 and MCS-12 scores respectively.For both regression analyses, correlates were grouped into two models. Inthe initial analysis, the first or Main Effects Model comprised all measures,except SES, perceived discrimination (ANYDISC), and one’s sense ofmastery over one’s life (PEARLIN). The second or Expanded Model in-cluded measures in the Main Effects Model and added SES, ANYDISC,and PEARLIN.

The residual score statistic, QRS (Breslow & Day, 1980; Stokes, Da-vis, & Koch, 1995), was used to determine what if any effects SES, per-ceived discrimination (ANYDISC), and one’s sense of mastery overone’s life (PEARLIN) had on the overall effect of the Main EffectsModel as well as on individual measures of the Main Effects Model.The Main Effects Model fit adequately when the QRS statistic failed tomeet statistical significance (p > .05). The Hosmer and LemeshowGoodness-of-Fit Test was used to assess how well the data fit the Ex-panded Model, a good fit signified by higher p-values. Both the QRS sta-tistic and the Hosmer and Lemeshow Goodness-of-Fit statistic were setup to reject the null hypothesis that the data fit the specified model.Hence, for the QRS statistic, a p-value < .05 signified that the ExpandedModel was the better of the two models. The Hosmer and LemeshowGoodness-of-Fit test was used as an additional support for the Ex-

28 SOCIAL WORK IN HEALTH CARE

panded Model’s adequacy for the data. In this case, we did not want toreject the null hypothesis that the data fit the specified model, so p-value> .05 is expected (See Cody & Smith, 1997, p. 243; Stokes, Davis, &Koch, 1995, pp. 192-193).

Limitations

This study relies on a sub-sample of a nationally representative sam-ple of a youth cohort. Hence its generalizability to the adult US popula-tion as a whole as well as to the cohort as a whole is compromised. Thestudy uses statistical controls to make inferences about the effects ofperceived discrimination and the self-concept of mastery on health sta-tus. Statistical controls are no substitute for experimental proceduresthat are necessary to make direct causal inferences; hence it is more ap-propriate to think of the independent measures as predictors health sta-tus to be tested rather than as determinants per se. This study does notcontrol for a variety of health-related factors that occurred prior to themeasurements of perceived discrimination and the self-concept of mas-tery. Such factors may influence self-concept of mastery and perceiveddiscrimination, in addition to health status as measured in the 1998 sur-vey. Despite these limitations, findings of the study show importantpredictive correlations between health status, perceived discriminationand the self-concept of mastery. They thereby contribute to the body ofknowledge regarding factors affecting health and provide suggestionsto enhance the efforts of social workers and other health-related practi-tioners to improve the physical and mental well-being of their clients.

RESULTS

Race/Ethnicity/Sex Differences by Select Characteristics

ANOVA results revealed race/ethnicity/sex differences by mentalhealth or MCS-12 scores (F = 5.77, p < .001) and SES measured as aver-age annual income-to-poverty ratios (F = 40.44, p < .001). Duncan posthoc results indicated that black, white and Hispanic females, and His-panic males had the lowest MCS-12 scores (51.6, 52.1, 52.6, 53.5 re-spectively when used as an ordinal level measure). White males andblack males had statistically significant higher scores (54.4 and 55.3 re-spectively) than each category of women. White males and white fe-males had significantly higher average annual income-to-poverty ratios

Richard K. Caputo 29

(3.9 and 3.6 respectively) than black males (2.9), Hispanic males (2.7),and Hispanic females (2.5), all of which had higher average annual in-come-to-poverty ratios than black females (2.2). No race/ethnicity/sexdifferences were found for physical health when the PCS-12 scoreswere used as an ordinal level measure. All race/ethnicity/sex groups hadPCS-12 scores above the typical US person, ranging from 52.0 for blackfemales to 53.3 for black males.

Likelihood of Below Average Health Status by SampleCharacteristics–Nominal Level Bivariate Relationships

Among the 969 youth in the study, 237 (24.5%) had MCS-12 (i.e.,mental health) scores below the typical US person, 167 (17.2%) hadPCS-12 (i.e., physical health) scores below the typical US person, and70 (7.22%) had both MCS-12 and PSC-12 scores below the typical USperson. As Table 2 shows, perceived discrimination, heredity, back-ground, race/ethnicity/sex, 1979 region of residence, and two lifestylemeasures (smoking and cocaine) were associated with the likelihood ofeither below average physical or mental health. These data are pre-sented for descriptive purposes only and no further elaboration is pre-sented in light of multivariate results below. It should be noted,however, that black males were 1.8 (1/.55) times less likely to have be-low average PCS-12 scores when compared against all other race/eth-nicity/sex groups. A separate chi-square analysis revealed that blackmales were 3 times more likely to have died between 1980 and 1998when compared against all other race/ethnicity/sex groups (5.6% vs.1.9%, p < .001), indicated by a response of “death” to questions regard-ing the reason for non-interview status in every survey year. Hispanicmales were also more likely to have died between 1980 and 1998 whencompared against all other race/ethnicity/sex groups, but at a muchlower magnitude, 1.6 times (3.6% vs. 2.3%, p < .05). No differences byrace/ethnicity/sex group were found in regard to year of death.

Characteristics of Youth Below and Above the Typical US Person’sHealth Status–Ordinal and Interval Level Bivariate Relationships

As Table 3 shows, SES, sense of mastery, psychological/attitudinalattributes, and cumulative characteristics distinguished those youthwho fell below from those at or above the typical US person in regard tophysical and mental health status. These data are also presented for de-scriptive purposes only. With the exception of the inverse relationship

30 SOCIAL WORK IN HEALTH CARE

Richard K. Caputo 31

TABLE 2. Nominal Level Characteristics by Below Average Health Status

Below Average Health Status

Physical Health Mental Health

%Present

%Absent

Odds %Present

%Absent

Odds

Independent Measure of Concern

ANYDISC 18.82 15.82 1.23 27.57 21.68 1.38*

Heredity

Respondent's father's health status 19.78 13.88 1.53* 25.59 22.97 1.15

Respondent's mother's health status 22.27 12.55 2.00*** 27.62 21.51 1.39*

Background

Respondent's family structure at age 14

Two-biological-parent family 17.06 17.77 0.95 23.52 27.27 0.83

Two-non-biological-parent family 18.27 17.11 1.08 28.85 23.93 1.29

Single-parent family 16.91 17.29 0.97 26.47 24.13 1.13

Poverty status in 1979 23.33 16.12 1.58* 32.67 22.95 1.63*

Race/ethnicity/sex

White Male 15.83 17.70 0.86 16.25 27.16 0.52***

Black Male 10.85 18.21 0.55* 15.50 25.83 0.53**

Hispanic Male 15.07 17.41 0.84 23.29 24.55 0.93

White Female 19.48 16.19 1.25 29.22 22.24 1.45*

Black Female 22.96 16.31 1.53 36.30 22.54 1.96***

Hispanic Female 15.48 17.40 0.87 26.19 24.29 1.11

Region lived in 1979

South 20.46 15.43 1.41* 28.82 22.03 1.43*

Northeast 14.44 17.90 0.77 21.93 25.06 0.84

North central 15.42 17.88 0.84 20.95 25.70 0.77

West 16.48 17.41 0.94 23.63 24.65 0.96

Urban environment at age 14 17.57 15.90 1.13 22.87 30.77 0.67*

Lifestyle

Substances

Alcohol 24.53 16.81 1.61 24.53 24.45 1.00

Cigarettes 20.44 14.40 1.53* 27.25 21.98 1.33

Cocaine 16.50 17.43 0.94 32.52 22.28 1.68**

Crack cocaine 12.00 17.52 0.64 34.00 23.94 1.64

Marijuana 17.70 16.58 1.08 25.84 22.52 1.20

Other/cumulative/structural

Never examined by physician 20.24 17.03 1.24 23.73 24.51 0.96

Never insured 22.22 16.89 1.41 30.16 24.06 1.36

Note: Definitions of characteristics appear in Table 1.

between years married and falling below the typical US person on theMCS-12 scale, the same measures influenced both physical health sta-tus and mental health status in much the same way.

Correlates of Physical Health Status (PCS-12): Logistic RegressionResults

As Table 4 shows in regard to physical health status, the Main EffectsModel failed to fit the data adequately (QRS = 15.7286, df = 3, p = .001),while the Expanded Model did fit the data adequately (Hosmer &Lemeshow Chi-square = 11.0049, df = 8, p = .20). In the ExpandedModel, of the three independent measures of main concern, bothMASTERY (Parameter estimate = -0.0965, p < .01) and SES (Parame-ter estimate = �0.2248, p < .05) were good predictors of physical healthstatus. Those who had lower scores on the Pearlin Mastery Scale andhad lower average annual income-to-poverty ratios were more likely tohave PCS-12 scores below the typical US person.

32 SOCIAL WORK IN HEALTH CARE

TABLE 3. Ordinal and Interval Level Characteristics by Health Status

Health Status

Physical Health-PCS-12 Mental Health-MCS-12

BelowAvg.

Avg. orAbove

T-value BelowAvg.

Avg. orAbove

T-value

Independent Measures of Main Concern

MASTERY 20.77 22.13 5.04*** 20.55 22.33 7.62***

SES 02.71 03.35 5.76*** 02.81 03.38 5.30***

Background

Age of respondent 40.34 40.28 �1.54 40.27 40.30 0.86

HGC 12.70 13.49 3.65*** 12.95 13.49 2.78**

Psychological/Attitudinal

Locus of control 11.52 11.43 �0.71 11.54 11.41 �1.16

Self-esteem 1980 32.33 33.38 3.05** 31.86 33.63 5.91***

Self-esteem 1987 32.39 33.46 2.81** 31.86 33.73 5.72***

Lifestyle

Sexual Intercourse (SI) 16.74 17.10 1.63 17.10 17.02 �0.45

Other/Cumulative/Structural

Average CC 02.34 02.53 0.54 2.84 2.39 �1.26

Average OLF 223.7 158.8 �3.31** 213.1 156.0 �3.46***

Average UR 03.07 02.98 �1.61 2.99 3.00 0.05

Years married 08.81 09.13 0.58 7.66 9.53 3.94***

Note: Definitions of characteristics appear in Table 1.***p < .001, **p < .01.

Richard K. Caputo and Belfer Hall 33

TABLE 4. Odds Ratios, (Parameter Estimates), and [Standard Errors] of Cor-relates of Physical Health–PCS-12

Correlates Main Effects Model Extended ModelIndependent Measures of MainConcern

ANYDISC ---------- nsMASTERY ---------- 0.914** (�0.0902) [0.0325]SES ---------- 0.799* (�0.2248) [0.0911]

Respondent's Mental Health -MCS-12

2.380*** (0.8670) [0.1998] 2.099***

( 0.7415) [0.2048]

HeredityRespondent's father'shealth status

1.299 ( 0.2618) [0.1903] 1.333 ( 0.2871) [0.1921]

Respondent's mother'shealth status

1.653** ( 0.5026) [0.1881] 1.616* ( 0.4796) [0.1899]

BackgroundAge of respondent 1.405 ( 0.3420) [0.1944] 1.495 ( 0.3781) [0.1971]HGC 0.912* (�0.0918) [0.0419] 0.967 (�0.0333) [0.0457]Respondent's family structureat age 14Two-biological-parentfamily

Reference Reference

Two-non-biological-parentfamily

0.875 (�0.1332) [0.2977] 0.834 (�0.1819) [0.3026]

Single-parent family 0.790 (�0.2359) [0.2837] 0.782 (�0.2463) [0.2874]Poverty status in 1979 1.179 ( 0.1648) [0.2530] 1.086 ( 0.0830) [0.2563]Race/ethnicity/sex

White Male Reference ReferenceBlack Male 0.442* (�0.8174) [0.3797] 0.372* (�0.9893) [0.3890]Hispanic Male 0.697 (�0.3603) [0.4191] 0.609 (�0.4951) [0.4239]White Female 1.074 ( 0.0712) [0.2580] 1.049 ( 0.0477) [0.2603]Black Female 0.985 (�0.0147) [0.3346] 0.772 (�0.0239) [0.3371]Hispanic Female 0.625 (�0.4693) (0.4193] 0.564 (�0.5726) [0.4239]

Region lived in 1979South Reference ReferenceNortheast 0.649 (�0.4329) [0.2712] 0.676 (�0.4348) [0.2738]North central 0.679 (�0.3865) [0.2513] 0.691 (�0.3909) [0.2759]West 0.720 (�0.3279) [0.2868] 0.779 (�0.3690) [0.2552]Urban environment atage 14

1.364 ( 0.3106) [0.2352] 1.382 (�0.2272) [0.2908]

Psychological/AttitudinalLocus of control 0.974 (�0.0266) [0.0611] 0.962 (�0.0385) [0.0612]Self-esteem 1980 1.006 ( 0.0064) [0.0269] 1.018 ( 0.0181) [0.0273]Self-esteem 1987 0.993 ( 0.0069) [0.0236] 1.011 ( 0.0112) [0.0247]

LifestyleSexual Intercourse (SI) 0.945 (�0.0567) [0.0414] 0.951 (�0.0506) [0.0409]SubstancesAlcohol 1.635 ( 0.4916) [0.3686] 1.672 ( 0.5140) [0.3744]Cigarettes 1.236 ( 0.2116) [0.2032] 1.279 ( 0.2457) [0.2068]Cocaine 0.909 (�0.0957) [0.2690] 0.978 (�0.0219) [0.2765]Crack cocaine 0.609 (�0.4958) [0.5072] 0.522 (�0.6492) [0.5147]Marijuana 1.024 ( 0.0236) [0.2152] 1.001 (�0.0008) [0.2171]

With the exception of highest grade completed (HGC), factors sig-nificant in the Main Effects Model remained significant in the Ex-panded Model when controlling for sense of mastery of one’s life. Thesefactors were Respondent’s Mental Health-MCS-12, respondent’s mother’shealth status, being a black male, and average number of weeks out of thelabor force (Average OLF). Those youth whose mental health was belowthe typical US person’s and whose mothers either died from or had healthproblems were respectively 2.2 and 1.7 times as likely to fall below thephysical health of the typical US person than were youth whose PCS-12scores were above the typical US person. Compared to white males,black males were 2.4 (1/.42) times less likely to have PCS-12 scores be-low the typical US person. Youth who spent more time out of the laborforce were also more likely to have below average physical health. Noother factors were found significant, including Respondent’s familystructure at age 14, all Lifestyle measures and the remaining other/cu-mulative/structural measures.

Correlates of Mental Health Status (MCS-12): Logistic Regression Results

As Table 5 shows in regard to mental health status, the Main EffectsModel also failed to fit the data adequately (QRS = 27.2811, df = 3, p <.001), while the Expanded Model did fit the data adequately (Hosmer &Lemeshow Chi-square = 13.0114, df = 8, p = .12). In the ExpandedModel, of the three independent measures of main concern, bothANYDISC and MASTERY were good predictors of mental health sta-tus (Parameter estimate = 0.3469, p < .01 and -0.1344, p < .001 respec-tively). Those who reported perceived discrimination were 1.4 timesmore likely to have MCS-12 scores below the typical US person, whilethose who had lower scores on the Pearlin Mastery Scale were alsomore likely to have MCS-12 scores were below the typical US person.

34 SOCIAL WORK IN HEALTH CARE

TABLE 4 (continued)

Correlates Main Effects Model Extended ModelOther/cumulative/structural

Average CC 0.994 (�0.0065) [0.0226] 0.998 (�0.0020) [0.0227]Average OLF 1.001* ( 0.0009) [0.0004] 1.001* ( 0.0007) [0.0005]Average UR 1.256 ( 0.2280) [0.1514] 1.220 ( 0.1987) [0.1526]Never examined by physician 1.161 ( 0.1491) [0.3663] 1.106 ( 0.1003) [0.3744]Never insured 0.913 (�0.0908) [0.3595] 0.791 (�0.2340) [0.3637]Years married 0.994 (�0.0060) [0.0158] 1.000 (�0.0005) [0.0159]

Max-rescaled R2 0.1429 0.1572QRS Chi-square 15.7286, df = 3, p = .0013 ------------Hosmer & Lemeshow Chi-square 11.0049, df = 8, p = .20

***p < .001, **p < .01, *p < .05.

Richard K. Caputo 35

TABLE 5. Odds Ratios, (Parameter Estimates), and [Standard Errors] of Cor-relates of Mental Health–MCS-12

Correlates Main Effects Model Extended Model

Independent Measures of Main Concern

ANYDISC 1.415* (0.3469) [0.1721]

MASTERY ---------- 0.874*** (�0.1344) [0.0299]

SES ---------- ns

Respondent's Physical Health - PCS-12 2.352*** (0.8552) [0.2012] 2.112*** ( 0.7478) [0.2048]

Heredity

Respondent's father's health status 0.995 (�0.0048) [0.1682] 0.987 (�0.0126) [0.1712]

Respondent's mother's health status 1.104 ( 0.0993) [0.1677] 1.077 ( 0.0746) [0.1704]

Background

Age of respondent 0.812 (�0.2076) [0.1835] 0.863 ( 0.3699) [0.1977]

HGC 0.994 (�0.0057) [0.0374] 1.015 ( 0.0150) [0.0387]

Respondent's family structure at age 14

Two-biological-parent family Reference Reference

Two-non-biological-parent family 1.131 ( 0.1233) [0.2616] 1.081 ( 0.0781) [0.2676]

Single-parent family 1.055 ( 0.0532) [0.2476] 1.007 ( 0.0072) [0.2538]

Poverty status in 1979 1.033 ( 0.0320) [0.2276] 0.985 (�0.0150) [0.2308]

Race/ethnicity/sex

White Male Reference Reference

Black Male 0.693 (�0.3672) [0.3450] 0.632 (�0.4584) [0.3517]

Hispanic Male 1.272 ( 0.2409) [0.3655] 1.194 ( 0.1776) [0.3737]

White Female 1.932** ( 0.6585) [0.2434] 1.986** ( 0.6864) [0.2480]

Black Female 1.911* ( 0.6478) [0.3083] 1.857* ( 0.6189) [0.3136]

Hispanic Female 1.408 ( 0.3422) (0.3662] 1.501 ( 0.4063) [0.3718]

Region lived in 1979

South Reference Reference

Northeast 0.702 (�0.3537) [0.2413] 0.664 (�0.4098) [0.2477]

North central 0.690 (�0.3714) [0.2265] 0.687 (�0.3752) [0.2296]

West 0.735 (�0.3074) [0.2540] 0.758 (�0.2774) [0.2609]

Urban environment at age 14 0.581** (�0.5422) [0.1986] 0.553** (�0.5915) [0.2018]

Psychological/Attitudinal

Locus of control 0.974 (�0.0266) [0.0611] 0.962 (�0.0385) [0.0612]

Self-esteem 1980 0.941* (�0.0611) [0.0244] 0.955 (�0.0465) [0.0251]

Self-esteem 1987 0.935** (�0.0676) [0.0212] 0.952* (�0.0488) [0.0221]

Lifestyle

Sexual Intercourse (SI) 1.040 ( 0.0396) [0.0372] 1.039 ( 0.0383) [0.0372]

Substances

With the exceptions of self-esteem in 1980, factors significant in theMain Effects Model remained significant in the Expanded Model whencontrolling for sense of mastery of one’s life. These factors were Re-spondent’s Physical Health-PCS-12, being a white female, being ablack female, living in an urban environment at age 14, self-esteem in1987, use of cocaine, and number of years married. Those youth whosephysical health was below the typical US person’s, who lived in an ur-ban environment at age 14, and who used cocaine were respectively 2.1,1.8 (1/.553), 2.0 times as likely to fall below the mental health of thetypical US person than were youth whose MCS-12 scores were abovethe typical US person. Compared to white males, white females andblack females were nearly twice as likely to have MCS-12 scores belowthe typical US person. Longer married youth were more likely to haveabove average mental health. Neither of the two heredity measures norrespondent’s family structure at age 14 was found significant.

DISCUSSION

This study shows that SES over the life course is a robust predictor ofphysical health but not mental health when accounting for heredity,background, psychological/attitudinal, lifestyle, and other measures,including race, ethnicity, and sex. Perceived discrimination affects only

36 SOCIAL WORK IN HEALTH CARE

Correlates Main Effects Model Extended Model

Alcohol 0.607 (�0.4989) [0.3734] 0.597 (�0.5155) [0.3831]

Cigarettes 1.053 ( 0.0520) [0.1835] 1.107 ( 0.1013) [0.1872]

Cocaine 2.077** ( 0.7309) [0.2360] 2.042** ( 0.7138) [0.2405]

Marijuana 1.133 ( 0.1253) [0.1995] 1.081 ( 0.0777) [0.2026]

Other/cumulative/structural

Average CC 1.009 ( 0.0094) [0.0195] 1.020 ( 0.0197) [0.0197]

Average OLF 1.001 ( 0.0007) [0.0004] 1.001 ( 0.0007) [0.0004]

Average UR 1.039 ( 0.0382) [0.1395] 1.069 ( 0.0668) [0.1414]

Never examined by physician 0.939 (�0.0629) [0.3514] 0.912 (�0.0926) [0.3546]

Never insured 0.872 (�0.1366) [0.3327] 0.803 (�0.2191) [0.3399]

Years married 0.959** (�0.0415) [0.0143] 0.962** (�0.0391) [0.0144]

Max-rescaled R2 0.1941 0.2283

Crack cocaine 1.123 (0.1160) [0.3834] 1.000 (�0.0002) [0.3907]

QRS Chi-square 27.2811, df = 3, p < .001

Hosmer & Lemeshow Chi-square 13.0114, df = 8, p = .12

***p < .001, **p < .01, *p < .05.

mental health status. Of the three measures of main concern, only mas-tery of one’s life, measured by the Pearlin Mastery Scale, improves theodds of having both a physical health status and a mental health statusabove the typical US person. This is so even when controlling for hered-ity, lifestyle, education, race/ethnicity/sex, and social conditions likeunemployment rates in area of residence. This last finding corroboratesPearlin, Lieberman, Menaghan, & Mullan (1981) and others (e.g.,Bosma, Marmot, Hemingway et al.,1997; Marmot & Wilkinson, 2001)who show a direct buffering effect of psychosocial factors in generaland the self-concept of mastery in particular on life event stressors.

Findings are consistent with those who report that health and well-be-ing depend in part on one’s sense of being able to influence forces or fac-tors affecting her/his life in general (Gorin, 2000; Syme, 1994). Theysuggest appropriate pathways or vectors that social workers in healthcare and other health-related practitioners can use to advocate for allo-cating resources to reduce health disparities. To the extent such masteryis a learned behavior, public programs and private initiatives can directresources to assist individuals in developing self-help or mastery-relatedattitudes and skills in a variety of settings, from schools and jobs, to pub-lic assistance programs and within families.

The importance the positive relationship between a sense of masteryover one’s life, which includes her/his physical and mental health as find-ings of this study suggest, validates the efforts of social work and relatedprofessions that stress self-determination and empowerment enabling in-dividuals to enhance their own social functioning, and improve condi-tions in their communities, as well as in society at large. Further instillinga sense of mastery is consistent with government efforts that encourageat-risk individuals to participate in their own health care, in addition to as-suring that they receive evidence-based care from their providers (Gaston &Collins, 2001). This is not to say that developing a self-concept of mas-tery is a purely cognitive process divorced from structural influences ordeterminants, nor does it imply that mastery can be enhanced without dueattention to a variety of personal attributes and environmental factors thathave a direct bearing on it. Findings suggest that the efforts of socialworkers in health care and other health-related professionals to discernfactors affecting mastery are worth the effort of future research. Thestudy provides evidence for social workers in health care and otherhealth-related professionals who rely on the person-in-environment ap-proach endemic to social work practice to seek effective change interven-tions that enhance the self-concept of mastery of clients. Such efforts can

Richard K. Caputo 37

complement other interventions designed to change the environment orstructural positions of their clients that also affect health status.

Findings also suggest that race/ethnicity/sex remain robust predic-tors of health status, but in unexpected, perhaps counterintuitive, waysthat should be taken into account when advocating for decreased healthdisparities, even in light of perceived discrimination. In regard to physi-cal health, black men in this study are less likely than white men to fallbelow the typical US person. They also have the highest PCS-12 scores.These findings go against the grain of some prior research showing dis-parate health outcomes along racial lines (e.g., Kochanek, Maurer, &Rosenberg, 1994; Shea, Miles, Hayward, 1996; Smith, 2000). Theymay be an artifact of the cohort used in this study, or reflect somebroader gains in the economy as a whole throughout the 1980s and1990s. To the extent they are correct, however, these findings challengeblanket appeals for race-related interventions aimed at reducing healthdisparities. They weaken advocacy claims based on the redistributionprinciple of “equity as priority to the sickest” or “worst off” based onrace. This is so because the condition of black males in this study satis-fies only one of two criteria that serve as a minimum for this principle,namely, those who have the shortest life expectancy and most seriousdiseases and illnesses (Merchand, Wikler, & Landesman, 1998). Asnoted, the former condition holds, while the latter does not.

Findings in regard to mental health, however, support appeals forgender-related interventions aimed at reducing health disparities. Spe-cifically, compared to white males, white and black females are morelikely to fall below the typical US person when accounting for a varietyof factors, including perceived discrimination. Social workers in healthcare and other health-related professionals can make a case on socialjustice principles to redistribute resources in such a way benefitingblack and white maturing women perhaps at the expense of black andwhite maturing men, given that their mental health scores were highestand that some diminution would still leave them well-off in an absolutesense (Nathanson, 1998).

Finally, findings of the study also suggest that advocacy efforts to re-duce health disparities might be better focused on improving one’ssense of mastery over one’s life rather than on other factors like improv-ing access to health care, despite claims to the contrary linking aggre-gate data showing, for example, that black Americans are more likely torequire health care but less likely to receive health care services (e.g.,Scott, 2001). This is so in part because of the finding that neither of thetwo health care utilization measures accounts for the physical and

38 SOCIAL WORK IN HEALTH CARE

health outcomes when controlling for other factors. And it is so in partbecause black males had better physical health outcomes when com-pared to white males. Although, as noted, this finding might be an arti-fact of the higher rate of mortality among black males in the entirecohort, on the whole black adolescents are more likely to die from acci-dents and homicides, which are less likely to be health related in themore traditional or common understanding of the term, than they arefrom suicide or from physical diseases per se (Singh & Yu, 1996).

Manuscript Received: 11/30/01Accepted for Publication: 05/09/02

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42 SOCIAL WORK IN HEALTH CARE

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