Gender differences in worksite health promotion activities

Sot. Sci. Med. Vol. 26, No. 5, pp. 525-535, Printed in Great Britain. All rights reserved

1988 Copyright

0277-9536188 53.00 + 0.00 Q 1988 Pergamon Journals Ltd

GENDER DIFFERENCES IN WORKSITE HEALTH PROMOTION ACTIVITIES M. A. 295 North Maple Avenue,

SPILUAN

Room 7128L2, Basking Ridge, NJ 07920, U.S.A.

Abstract-A

model of intentional health-related behaviors was tested to predict men’s and women’s participation in six worksite health promotion programs. The model was best at predicting participation in programs that treat unhealthy conditions or behaviors. It was least successful at predicting participation in programs than can appeal to both those with ‘health risks’ and to health ‘maximizers’. Women had higher rates of participation than men in three of the four ‘treatment’ programs, and they participated in more programs. In every program type, the factors that influence women’s participation were different from those affecting men; and women with children showed different patterns of influence from women without children. The patterns of influence are consistent with two sources for women’s greater concern with treating poor health: their nurturant role responsibilities, and a particular emphasis by the medical profession on women and women’s concerns. Key uoords-worksite

health promotion,

gender differences, health control belief

INTRODUCTION

Medical research has increasingly maintained that an effective means of promoting health is to change individual lifestyles [l-8]. The major causes of morbidity and mortality today-heart disease and cancer [g-have been linked to diet 13, l&12], exercise [3, 13-151, smoking [2,3,8, 11, 121, and even to the ability to cope with stress [16, I?]. This realization that individuals have considerable control over their own health has led to increased emphasis on health promotion by public health and occupational health professionals within government agencies, business and industry [3, 18, 191. More and more employers have joined this health promotion effort, not only in response to pressure from occupational health professionals, but also in an effort to control their rising health care costs [18-231 and to improve employee morale [2 I, 24,251. If worksite health promotion programs are to realize their potential benefits for employees and employers alike, they must be effective at recruiting participants, particularly those whose health is most at risk. In an attempt to understand how people decide to participate in worksite health promotions, Davis et al. [26] adapted Ossorio’s [27] model of intentioned behavior to predict individual intentions fo parricipate. That same conceptual framework is used here in a multivariate regression model to explain actual participnrion in worksite health promotion programs. In addition, since men tend to be medically more ‘at risk’ than women for chronic diseases [28-301, yet are less inclined to engage in a host of positive health behaviors [31], it is important to understand gender differences in the factors that encourage participation in worksite health promotion activities. In order to fully investigate possible gender differences, this model is tested separately for men and women. The data are from a pilot group of 350 AT&T employees who were offered the following health

promotion programs: weight reduction, stress management, interpersonal skills development, exercise, smoke-ending, and management of low back pain. These employees were given feedback from health risk appraisals which may have suggested the need for certain programs, but they were free to select or not select any program they wished, and participation was permitted on company time. This group of employees was part of an experimental study designed to test the effectiveness of AT&T’s Total Life Concept (TLC) health promotion effort (see Ref. [24J for details). CONCEIWJAL

MODEL

The basic model used in this analysis is shown in Fig. 1. As described by Davis et al. 1261,this model of intentioned behavior integrates factors from the health belief model [37] and holds that intentional health-related behavior involves: (A) cognitive variables (beliefs about health); (B) motivational variables (intentions to change and the ‘at risk’ conditions one intends to change); (C) skills and abilities needed to bring about the desired change (such as an adequate educational level and knowledge about health issues). While skills and knowledge reflect the aspect of an ‘ability’ that is internal to an individual, opportunity to engage in desired behaviors reflects the socio-structural component of the ability dimensions. Measures such as job status, marital status and number of children represent an individual’s role responsibilities and privileges and therefore reflect the individual’s relative opportunity to engage in desired behaviors [38]. This structural component of ability, which Davis et al. 126) call the ‘social-ecological component’ is therefore represented in Fig. 1 by demographic characteristics. Finally, intentional behavior depends upon (D) an individual’s enduring personality characteristic (such as reflected in type A behaviors and psychological well-being) and (E) more 525

M. A.

526 COGNITIVE

SPILMAN

ABILITIES/OPPORTUNITIES

FACTORS

Health Knowledge

Perceived Health

Educatron

Health Control Belref

Socral Support Job Level Sex Age

PARTICIPATION

Race

IN

Relrgion

HEALTH PROMOTION

\

I,

Marital Status Number of Children (for women)

PROGRAMS ,J #OF PROGRAMS

% PARTICIPATION

~ /

IN EACH

/ MOTIVATIONAL FACTORS INTENTION TO CHANGE RISK FACTORS (PHYSIOLOGICAL): - Overweioht

PERSONALITY CHARACTERISTICS AND DISPOSITIONS RISK FACTORS (PSYCHOLOGICAL): - Psychological

Well-Being

- High Blood Pressure

- Type A Behaviors

- High Blood Sugar

- Job Pressure

- Smoking

- Work Enthusiasm

- Exercise - Alcohol Use

Fig. I.

Conceptual

model of the factors affecting participation in worksite

temporary personal dispositions (such as attitudes toward one’s job). These last two dimensions are labeled ‘psychological risk factors’ in this healthrelated model, since physical health can be strongly influenced by psychological and attitudinal characteristics. Figure 1 details the specific variables representing each of these dimensions. The full model is used to predict, separately for males and females, the number of programs they select and their degree of participation in each of the six health promotion programs.

SAMPLE AND MEASURES

The 180 men and 170 women in this sample are the experimental group from a pilot offering of AT&T’s health promotion program (‘Total Life Concept’), that was begun in May, 1983. Employees were randomly selected from one AT&T location, and one entire work group was selected from a second location. Seventy-four percent of those selected filled out the initial questionnaire. The employees available for this analysis are those for whom all data are available for 1983, 1984, and 1985, representing 29% of those who filled out the initial questionnaire. Separate analyses of drop-outs between 1983 and 1985 indicates that the employees lost from this sample have been the younger, nonmanagement females and less educated employees generally. The greater loss of younger, nonmanagement females has

health

promotion

programs.

made the males and females in this sample more similar demographically than is true of the entire AT&T population (and than is probably true of any corporate environment). This greater similarity between the men and women permits a better focus on gender differences, exclusive of status differences which tend to be correlated with sex. But, pending further research, one must be cautious about generalizing findings from this relatively high status population of corporate employees to those with very different demographic characteristics. Demographic characteristics of the sample are shown in Table I. The independent variables used in this analysis are from a questionnaire filled out by study participants before they elected to participate in any of the health promotion programs (May, 1983). The dependent variables-degree of participation in each of the offered programs and number of programs selected--came from records kept by each program manager over the following two-year period. A person who completed a program was given a score of loo%, a person who elected not to participate in the program was given a score of zero. Since programs were offered more than once and participation was allowed in the same program more than once, degree of participation could be greater than 100% (more common in the exercise program). Cognitive factors (1) Perceived health was a single question asking respondents to rate their health on a five-point scale

Gender differences in worksite health promotion Table

Dcmosraahic

I.

descriotions

of men

and

women

Educarion: High

school

College Graduate

I8 4

degree

Job level: Nonmanagement First

or second

Third Race

level

77 21 2

78

diploma

degree

71 29 0 89 43 55

258 level

manager

67

manager

8

or above

95;

as white

Religion

as Protestant

Marned

currently

sat 89.

Number of childxn : Never

gave

birth

Gave

birth

once

Gave

birth

twice

Gave

birth

three

Gave

birth

four

.x-ore for

:

Mean Age

times or more

times

in years

38 5.9

438 6.0

Social

suooort

Based

on chi-square

$P 5 0.06.

42 16 22 12 8

N/A

Based

lf < 0.001; §P 5 0.0001.

test of independence: on f-test:

tP

s 0.01;

constructed from multiple indicators, using principal component factor analysis: (1) Psychological well-being is constructed from nine questions asking how often respondent has had feelings of weakness, tiredness, dullness, hopelessness, depression, and suicidal impulses and how respondent has felt ‘in general’ in the last month (very low spirits to excellent spirits). (2) Type A behavior is constructed from six questions asking respondent’s general tendency to be annoyed impatient, competitive and aggressive. (3) Perceived job pressures and (4) enthusiasm for work are separate factors derived from a set of 21 job satisfaction questions. Job pressure is represented primarily by degree of agreement with two statements: ‘I have too much to do’ and ‘I don’t have time to get the job done’. Work enthusiasm is represented primarily by degree of agreement with six statements covering: overall satisfaction with one’s job; belief in the importance of one’s job; and never feeling worn out at work, or having trouble concentrating at work, or having to force oneself to go to work. Abilities,

from poor to excellent (there were no ‘poor’ responses). (2) Health control belief is the mean score on six questions asking ‘how much can you personally do to prevent yourself from getting’ arthritis, cancer, serious depression, pneumonia, heart disease, and serious injury. The internal consistency of this index is 0.67 (Cronbach’s alpha). Motivational

j&actors

(1) Intention to change is derived from a principal component factor analysis of nine questions dealing with the respondent’s stated intention to: give up smoking; reduce alcohol consumption, blood pressure, cholesterol, and weight; increase seatbelt usage and exercise; improve diet; and schedule regular medical exams. Intention was measured as: don’t need to do this; need to, but have no plans; plan to do this soon; am doing this now. (2) Risk factors: Degree of overweight was constructed by comparing self-reported weight, body frame, and height, to insurance tables that adjusted for sex, height, and body frame. It is calculated as percent over the highest ‘normal’ weight for each category. High blood pressure and high blood sugar were measured by a yes/no question asking whether respondents had ever been informed by a physician that they had either of these conditions. The smoking variable measures current smoking behavior, based on self-reported number of cigarettes, pipes, or cigars smoked per day. Level is coded as nonsmoker, low, moderate, high, or very high. Level of exercise is calculated based on the time spent in each of seven aerobic activities, multiplied by the reported intensity of each activity (mild, moderate, or very vigorous). Alcohol use is measured as the number of glasses of wine, beer, and liquor consumed per week. Personality Each

characteristics

of these

and dispositions

‘psychological

risk’

measures

is

527

opportunities

Health knowledge is the mean score on six questions asking how much each of the following behaviors can help prevent serious illness: vigorous physical activity, medical tests or check-ups, taking vitamins or other supplements, reducing anxiety and tension, not smoking cigarettes, not drinking too much alcohol. Although health knowledge is an important predictor in only one of the participation models, it might have been more effective if it were not restricted to this narrow range of knowledge. Social support is a summation of the number of friends and relatives respondent feels ‘close’ to, plus the number of those friends and relatives respondent is in contact with at least once a month. Religion and race are dummy variables (1 = Protestant, 0 = other; 1 = white, 0 = other). The remaining demographic measures are described in Table 1. Number of children was available for women only, and was asked as ‘number of times you have given birth’. Giving birth is not a very good indicator of motherhood, per se, since it excludes adoptive mothers and women who are mothers through marriage, and it includes women who may have given birth but are no longer in or may never have been in the role of mother. Nevertheless, as a gross indicator of the distinction, it can at least detect whether there are significant and compelling differences between working mothers and working nonmothers.

FINDINGS

As shown in Table 2, there is a clear pattern of greater participation by women: women were most likely to participate in three or four programs over the two year period, while men were most likely to participate in one or two programs over that same period. In addition, a higher proportion of women than men participate in three of the six program types. (Separate regression analysis found women’s greater participation in the interpersonal skills pro-

528

M. A.

Table

2. Partwpation

in worksite

health

promotion

by men

and

SPILMAN

PARTICIPATION

MODELS

women Percent

particmatlon

Me” (N

bv

Women

= 180)

(N

= 170)

Number of programs : None

19*

8

One-two

46’

33

Three-four

308 d’

50

24.

40

Stress management

28

34

Interpersonal

IO’

25

Five

or more

8

Type OJprograms : Weight skills

Smoking

8t

Exercise Low

back

Based

15

57 management

on chi-square

test: ‘P

63

121 < 0.002;

tP

I

23

0.05

gram to be a function of their lower job level; but all other participation differences are independent of status differences between men and women.) These differences in rate of participation cannot be attributed to greater health risks on the part of the women. As shown in Table 3, the men and women in this sample are remarkably similar in their initial risk profiles: women have a significantly lower score on psychological well-being, which puts them at some greater health risk; but the men use more alcohol, which puts them at greater risk on this measure. (When age differences are controlled, men are no more overweight than the women, but men do drink more alcohol at every age level.) While risk differences seem to offset each other, Table 3 shows that women have a slightly higher score on health knowledge and a greater intention to change their health behaviors. These differences in favor of women are quite small, but their ability to explain participation differences may be magnified if they have more influence on women’s participation than on men’s

Table

3. Se.x differences

in uredictor

variables

Mean scores fol Men Health

control

Health

knowledge

Intention

belief

to change

Women

3.5

3.6

4.1.

4.4

-0.2’

0.3

Risk ficocrors: Percent Smoking

overweight level

Alcohol

use

Exercise

level

_

12.2t

8.9

0.7

0.6

5.8’

2.9

IO.8

Psychological well-being Type A behaviors

9.R

0.2’

-02

0.0

Job pressure Work enthusiasm

0.0

-0.1

0.0

-0.1

0.0 Percentage

Men

Perceiwd

health good

Good Fair Told

had

high

blood

Told

had

hieb

blood

Based $P

on

r-test:

= 0.03.

Women

:

Excellent Very

of

pressure wear

lP I 0.001;

II 46

41

14

36

3x

8

7

25:

16

5 tP

I

0.01.

Based

5 on

chi-square

As summarized in Table 4, this intentioned behavior model varies in its ability to predict participation. Since it assumes risk factors as a primary source of motivation, the model predicts best for programs like smoking and weight control, where the ‘at risk’ population is easily distinguished. And it predicts least well for programs without identifiable ‘risk’ indicators-interpersonal skills and exercise. Programs for stress management and management of low back pain fall somewhere in between, since they have a variety of risk indicators, but they are risks likely to be experienced by most people at one time or another. The model also predicts participation better for men than for women in all programs except smoking and low back pain, where it predicts better for women. Predictors of participation are different for men and women, but they are also different for each sex according to the particular program selected. Because there is no general pattern of variables which predict ‘health promotion participation’ for men or for women, the model to predict number of programs selected (regardless of program type), did not produce a good fit to the data for either men or women (not shown: adjusted R-square = 0.06 for men and 0.03 for women). Predictive ability for women was not improved by adding number of children (births) to the models (R-square improvement was 0.038 in weight program, but 0.005 or less in the others). However, a test for interaction effects revealed that in some programs women with children differ significantly from women without children in the factors that influence their participation (Table 5). The explained variance in women’s participation increased from 25 to 5 1% for the stress program when only women with children were modeled (adjusted R-square from 0.093 to 0.271). And the explained variance in the low back program increased from 29 to 61% when only women withour children were modeled (adjusted R-square from 0.145 to 0.365). Two other programs showed similar changes: prediction for the smoking program is much better for women without children (adjusted R-square increase from 0.614 to 0.855) than it is for women with children (adjusted R-square = 0.509). For interpersonal skills, although prediction is not high for any group, the model still predicts better for women with children (adjusted R-square from 0.106 to 0.166) than for women without children (adjusted R-square = 0.061). In this sample, having children does not limit a woman’s opportunity to participate in health promotion programs. Probably because participation was on employer time, employees’ nonwork role obligations have little direct effect on their work-time opportunities to participate. On the other hand, the indirect effects of nonwork related roles can be seen in the very different patterns of influence for women compared to men and for women without children compared to women with children [40]. SMOKING

PROGRAM

test:

For both men and women,

the smoking

program

enthusiasm

0.129 (6.56) -0.026 (- 1.30) -0.068 (-0.21) 0.022 (2.72) O.l56t (8.75) 0.009 (0.79) 0.027 (0.30) 0.379 0.281 O.ooO

Men

Women

Men

Women

0.095

-0.079 (-0.24) 0.272, (23.00) 0.037 (4.46) O.ooO (-0.01) -0.136 (-1.23) -0.002 (-0.01) -0.028 (-0.93) 0.238t (7.75) 0.035 (1.20) -0.140 (-5.18)

0.038 -0.152 0.106 (2.55) (-9.10) (6.38) 0.028 -0.227' -0.129 (1.88) (-13.32) (-9.61) 0.007 -0.248' -0.083 (0.02) (-0.90) (-0.26) 0.063 -0.101 0.110 (16.32) (7.59) (- 11.04) -0.021 0.118 -0.121 (7.86) (-1.48) (-7.67) 0.130 0.141 -0.030 (9.13) (15.18) (- 1.90) 0.156 -0.035 0.070 (2.27) (0.92) (-0.46) 0.252 0.248 0.172 0.093 0.130 0.003 0.068 0.006 0.494

-0.081 (-5.65) 0.292' (40.92) -0.007 (-0.18) 0.087 (0.37) 0.012 (0.04) -0.131 (-4.80) -0.056 (-1.94) -0.066 (-2.25) 0.101 (3.34)

( -0.28)

Women

Men

Women

Exercise

-0.084 (-2.41) -0.047 (- 1.75) -0.035 (-1.41) -0.027 (-0.58)

0.041 (1.99) 0.052 (2.91) -0.035 (-2.77) -0.040 (-1.77)

Women

Low back Men

0.063 -0.119 -0.173f (2.43) (-5.25) (-10.16) 0.012 -0.058 -0.068 (0.47) (-2.49) (-4.52) -2.31. -0.187 -0.045 (-0.62) (-0.52) (-0.10) 0.048 0.027 -0.041 (4.60) (2.97) (-4.38) 0.046 -0.066 -0.033 (1.99) (-3.21) (-2.07) 0.110 0.211t 0.059 (3.66) (16.68) (7.64) -O.l2l$ 0.015 -0.060 (2.77) (-0.78) (-1.03) 0.201 0.189 0.406 0.312 0.075 0.016 0.000 0.059 0.365

0.018 0.125 -0.191t 0.093 (1.21) (10.76) (-16.17) (3.82) -0.084 -0.178f -0.156 -0.150 (-6.43) (-14.88) (-14.79) (-6.00) 0.091 0.037 -0.019 -0.057 (0.12) (-0.10) (-0.23) (0.23) 0.056 -0.016 -0.099 O.OMl (- 1.98) (-20.99) (5.63) (0.04) -0.022 0.047 -0.018 0.041 (4.48) (- 1.62) (-1.60) (1.85) 0.013 0.121 -0.048 -0.039 (0.92) (18.57) (-4.28) (-2.85) -0.131t -o.lslt 0.005 -0.040 (-1.97) (-3.41) (-0.10) (-0.36) 0.682 0.210 0.167 0.134 0.614 0.086 -0.009 -0.003 0.000 0.040 0.533 0.492

0.294 0.145 0.014

(1.W

0.066

-0.008 (-0.59) -0.017 (-1.41) 0.437. (1.51) 0.028 (3.80) -0.048 (-3.71) -0.003 (-0.24)

-0.120 -0.010 -0.065 0.055 -0.014 -0.175 -0.039 -0.273. (0.18) (-0.06) (-0.69) (-0.08) (-0.93) (-0.26) (-0.03) (-0.12) -0.143 0.060 0.081 -0.024 -0.051 -0.051 0.054 -0.132 (4.49) (3.65) ( -2.09) (-5.12) (-5.43) (2.55) (-12.27) (-7.32) -0.092 -0.007 0.046 -0.129 -0.011 -0.012 0.060 0.106 (-1.19) (-1.51) (6.99) (-12.33) (6.34) (9.58) I(-11.27) (-1.32) -0.182t 0.123 0.570. 0.884' -0.177t -0.314. -0.189t -0.202t (-6.72) (-3.60) (3.30) (9.92) (27.55) (-6.83) (-12.09) (-3.48) 0.055 0.118 -0.016 -0.090 -0.065 0.112 0.027 -0.087 (0.55) (0.72) (-0.18) (-0.26) (-0.20) (0.896) (0.07) (-0.81) -0.002 -0.147 -0.051 -0.1821 -0.115 -0.008 -0.051 0.050 (-0.55) (-0.02) (-0.19) (0.10) (-0.01) (-0.68) (-0.28) (-0.40) -0.020 0.095 0.179t O;l62 0.008 -0.032 0.045 -0.015 (9.38) (6.89) (0.21) (-0.75) (-0.87) (1.32) (-0.36) (3.25) -0.129 O.l19$ 0.009 -0.066 0.134 -0.019 -0.046 -0.068 (3.98) (0.47) (-2.74) (3.28) (-4.61) (-0.49) (-1.33) (-1.52) -0.018 0.045 -0.119 -0.053 -0.054 0.110 0.067 0.029 (-0.69) (1.59) (-5.80) (-2.30) (-1.25) (2.77) (2.01) (0.64) -0.070 -0.045 -0.144 -0.055 0.002 0.086 0.060 0.128 (0.06) (-2.84) (1.96) (2.73) (-1.73) (-6.84) (-2.56) (2.09)

-0.138$ -0.061 -0.050 -0.145 (-3.73) (-2.77) (-3.03) (-8.11) -0.014 0.062 0.012 0.069 (-0.47) (3.22) (0.94) (4.42) -0.027 -0.018 -0.064 -0.034 (-1.04) (-1.36) (-5.39) (-3.10) -0.046 0.055 285' -0.116 (-0.91) (2.25) (12.56) (-5.87)

Men

DEPENDENTVARIABLES Interpersonal skills Smoking

-0.013 -0.147 -0.051 -0.171$ 0.237t (-0.53) (-6.19) (-2.25) (-5.28) (9.19) 0.074 -0.069 0.202: -0.089 0.078 (3.39) (-3.76) (10.20) (-3.55) (3.49) -0.102 -0.073 -0.057 0.172t 0.000 (-6.64) (-4.29) (-4.14) (7.44) (-0.02) 0.112 0.042 -0.033 0.091 0.287' (4.03) (1.30) (-1.33) (2.07) (10.12)

0.384' 0.130 (0.95) (0.36) -0.150 -0.005 (-0.27) (-11.40) 0.076 -0.050 (8.99) (-5.41) -0.042 -0.014 (-0.97) (-0.38) 0.103 -0.079 (0.37) (-0.64) -0.029 -0.099 (-0.08) (-0.38) 0.270t 0.012 (8.09) (0.36) 0.052 -0.l5lf (-4.43) (1.52) -0.103 -0.005 (-0.15) (-3.13) -0.009 -0.1727 (-4.81) (-0.30)

-0.102 (-3.62) -0.148~ (-6.80) -0.103 (-5.12) 0.151t (3.93)'

Women

stress

lP ~0.01; tP 10.05; tP
R’ AdjustedR2 Probability of F

Socialsupport

Married

Religion as Protestant

Race as white

Age

Job level

DEMOGRAPHICS: Education

@Work

@Job pressure

@Type A behaviors

@Psychological well-being

l Exercise

.Alcohol

@Smoking

@High blood sugar

@High blood pressure

l Overweight

RISK FACTORS:

Intention to change

Healthknowledge

Healthcontrolbelief

Perceived health

Men

Weight

Table 4. Effects of predictor variables on healthpromotion participation for ment and womenr (standardized regression coefficients, with unstandardized coefficients in parentheses)

A behaviors

enthusiasm

as Protestant

0.070 (4.40) -0.184 (- 14.00) -0.063 ( - 0.23) -0.016 (- 1.67) 0.103 (7.38) 0.180 (13.90) 0.26lt (4.06) 0.510 0.27 I 0.017

-0.192 (-0.55) 0.475’ (35.31) 0.154 (16.03) -0.168 ( - 5.94) -0.179 ( - I .75) 0.102 (0.51) -0.102 (- 3.66) 0.271t (8.29) -0.046 (-1.82) 0.273

0.150 (6.57) 0. I57 (8.04) -0.079 (-5.21) -0.1 I3 (-5.01)

promotion

Interpersonal

N = 56.

- 0.2641 ( ~ 13.65) 0.029 (1.82) -0.399’ (- 1.19) -0.124 (- 10.96) 0.03 I (1.81) -0.129 (-8.23) 0.066 (0.84) 0.440 0.166 0.093

-0.139 (-0.33) 0.135 (8.32) 0.197 ( 16.90) O.Olb (0.41) 0.01 I (0.09) 0.101 (0.42) -0.131 (- 3.87) -0.337. (-8.54) - 0.03 I (- 1.02) -0.103

0.359’ (13.00) 0.134 (5.65) -0.317’ (-17.34) 0.32l$ (I 1.73)

-0.140 ( - 9.69) -0.121 (-8.39) 0.105 (0.30) -0.265 (-38.53) -0.044 (-2.91) -0.010 ( - 0.66) -0.018 (-0.24) 0.420 0.061 0.332

0.080 (0.27) -0.013 (-1.38) -0.073 (- IO.661 ~ 0.09; (2.47) 0.077 (0.62) -0.205 (-0.73) 0.179 (5.35) 0.205 (6.96) 0.049 (1.38) 0.066

0. I35 (5.70) 0.068 (3.29) 0.248 (19.67) 0.139 (4.76)

No children

DEPENDENT skills

children

- 0.036 (- 1.99) -0.193 (- 12.98) -0.014 (-0.04) - 0.025 (-2.35) -0.063 (- 3.94) 0.025 (1.70) -0.155 (-2.13) 0.509 0.269 0.018

0.060 (0.15) 0.017 (1.15) - 0.027 ( - 2.49) 0.645. (20.13) -0.105 ( - 0.90) -0.153 ( - 0.68) 0. I35 (4.26) 0.171 (4.62) 0.051 (1.76) -0.040

(-1.71)

-0.044

0.028 (1.07) 0.207 (9.33) -~O.Wi (- 5.39)

Children

VARIABLES

for women with and without coefficients in parentheses)

Children

participation

N = 65. No children:

-0.062 (-4.52) 0.079 (5.70) -0.078 (-0.24) 0.143 (21.84) -0.135 (-9.32) 0.167 (I 1.86) 0.076 (1.06) 0.313 -0.112 0.767

0.000 (0.00) -0.185 (-20.54) -0.112 ( - 17.03) 0.142 (3.78) -0.307 ( - 2.57) 0.014 (0.05) 0.114 (3.57) 0.119 (4.23) 0.106 (3.15) -0.176

-0.333 (- 14.76) 0.279 (14.10) -0.052 (-4.31) -0.192 (-6.88)

No children

Stress

on health

Children

variables

‘P -s 0.01; tP 5 0.05; #P 5 0. IO. With children:

R* Adjusted R2 Probability of F

Social support

Married

Religion

Race as white

Age

Job level

DEMOGRAPHICS: Education

@Work

@Job pressure

@Type

@Psychological

@Exercise

l Alcohol

well-being

blood sugar

@High

*Smoking

blood pressure

@High

RISK FACTORS: @Overweight

IO change

knowledge

Health

Intention

control

Health

belief

5. Effects of predictor

Perceived health

Table

0.08 I (6.82) 0.013 (1.13) 0. I33 (0.47) -- 0.030 (-5.30) -0.032 (-2.58) -0.049 (-4.05) -0.132t (-2.14) 0.897 0.834 0.000

0.042 (0.17) -0.075 (-9.71) -0.027 (-4.79) I .093* (33.81) -0.187’ (- 1.82) 0.130% (0.57) 0.124 (4.53) 0.138% (5.70) 0.050 (1.73) -0.048

-0.144i ( - 7.39) 0.018 (I .04) 0.086 (8.33) 0.000 (0.01)

Children

(-

-0.149 I I .02) - 0.022 (- 1.97) 0.302% (I .29) 0.027 (3.41) 0.030 (2.56) 0.042 (3.80) 0. I38 (2.53) 0.355 0.040 0.360

-0.410’ (- 1.39) O.OllO (0.01) 0.108 (13.29) -0.227 ( ~ 9.48) 0.096 (1.11) -0.143 (-0.85) 0.068 (2.89) -O.lcm ( 3.60) -0.085 ( - 3.96) 0.022

9.077 (i.95) 0.169 (10.21) - 0.083 ( - 6.47) -0.252 (-13.13)

with unstandardized

0.413. (29.54) --0.116 (-8.29) 0.539’ (I .62) -0.025 (-3.81) 0.010 (0.66) - 0.220 (- 15.36) 0.126 (I .74) 0.607 0.365 0.008

-0.007 ( - 0.02) - 0.406’ (-44.50) -0.200 (-30.12) -0.112 (-2.95) 0.108 (0.90) -0.252% ( - 0.93) 0.009 (0.28) 0.472. (16.53) - 0.079 (-2 31) -- 0.095

0.187 (8.16) -0.200 ( - 9.9e) -0.090 (-7.37) 0.256 (9.03)

No children

Low back pain

regression coefficien!s.

No children

Smoking

(standardized

Gender differences in worksite health promotion is the one best predicted by this participation model. Male smokers (B = 0.570) who perceive themselves as relatively less healthy (B = -0.138) are most likely to participate in the smoking program. For both men and women, those who have less social support are somewhat more inclined to participate (B = 0.121 for men; B = 0.131 for women). This common influence for men and women suggests that one of the program’s strengths may be its ability to provide a supportive environment to people who are making a difficult transition without external support. Unlike men, women are also more likely to participate if they have type A behavior patterns (B = 0.119). Perhaps being able to stop smoking represents a competitive challenge to women. Or it may be that women with type A behaviors have the most difficulty quitting on their own. The fact that type A behavior is unrelated to men’s participation suggests that different types of program support may be needed for women than for men. Even though this model predicts well for women, there are significant differences in the predictors for women without children compared to those with children (Table 5). Women smokers wirhout children (but not mothers) are also more likely to participate if they perceive themselves as relatively less healthy. Unlike men, however, and unlike women with children, these women show fairly healthy lifestyles in the form of lower alcohol consumption and more exercise (than their counterparts who do not participate). Yet the fact that they perceive themselves as relatively unhealthy suggests that smoking may be a primary focus for their poor health perceptions and a stronger motivator for their participation. Women smokers with children participate regardless of their health perceptions, suggesting that the children themselves may provide some motivation. WEIGHT PROGRAM

The model explains 38% of the variation in men’s participation in weight reduction classes. Overweight men (B = 0.384) who lack enthusiasm for their work (B = -0.172) and who lack competitive type A behaviors (f3 = -0.151). are most likely to participate. There is some indication that men who intend to make health-related changes (B = 0.151) yet feel less personal control over their health (B = -0.148) are also more likely to join. On the other hand, women participate in weight reduction whether or not they are overweight, indicating that women perceive overweight even when it is not a real health issue. The fact that this risk-based model cannot predict women’s participation is probably because weight is, for women, primarily an issue of appearance and beauty, not health. STRESS MANAGEMENT

PROGRAM

Men’s participation in the stress program is encouraged when they have been told by a physician that they have high blood sugar (B = 0.292), when they are young (B = 0.248) and when they are in lower status jobs (B = -0.227). Women’s participation is not as well predicted

531

until women with children are separated out (Table 5). The model cannot predict for women without children, but women with children are more inclined to participate when they have been told by a physician that they have high blood pressure (B = 0.475), when they exhibit type A behavior patterns (B = 0.271) and when they lack enthusiasm for their work (B = -0.273). In addition, the more social support these women have, the more likely they are to participate in the stress program (B = 0.261). It is not clear whether working mothers feel freer to take advantage of this type of opportunity when they have a support network, or whether the network itself is a source of stress that imposes additional role obligations on working mothers. While women’s motivation seems closely linked to stress symptoms emphasized by physicians (type A behaviors and high blood pressure), men’s motivation seems more closely linked to conditions or situations which they may perceive as stressful in a corporate environment: being fairly young and lacking in corporate status. It is not clear how high blood sugar relates to stress, whether it is directly experienced as a stressful condition, or whether it is perceived as a symptom of some other type of medical risk. It is clear, however, that men and women differ in the ‘stress’ indicators that lead them to this program. PROGRAM

FOR LOW BACK PAIN MANAGEMENT

This model cannot predict participation for men, yet it is fairly successful at predicting for women (R-square = 0.294), particularly if they are without children (R-square = 0.607). Women without children participate when they are older (B = 0.539), have type A behavior patterns (B = 0.472) and have higher education (B = 0.413). They are less inclined to participate if they have high blood pressure (B = -0.406) or if they are exercisers (B = -0.252). Except for age and lack of exercise, these are different predictors from those for women with children. Although the model does not predict well for women with children, it appears that type A behaviors and educational level have negative effects for mothers, and blood pressure has no effect. Older, well-educated women with type A personalities who have never had children may share some lifestyle characteristics that have produced particular kinds of physical stresses for the back. For women with children, lifestyle factors most likely to produce back strain may be related to the number and spacing of their children, or to their age at first pregnancy, all ‘risk’ variables not included in this model. Similarly, the kinds of ‘risks’ likely to produce low back pain in men may be very different from those for women (e.g. old sports injuries). If this model were to incorporate a measure of perceived pain and risk measures based on lifestyle factors (e.g. sedentary or active work style, amount of driving, previous injuries, number and spacing of children), overall prediction would probably be improved for men and women. Yet, the effects of particular risk indicators would very likely differ for men and women. Adding these measures would also make it possible to determine whether women par-

532

M. A.

ticipate more than men because they feel more pain, because they are exposed to greater physical risk. and/or because they are more likely to respond to such risks by joining ‘treatment’ programs. Moreover, if perceived pain were controlled, gender differences in the effects of other variables-abilities and opportunities, as well as personality factorscould be more easily identified. EXERCISE AND INTERPERSONAL

SKILLS PROGRAMS

The model does not effectively predict participation in either of these programs, although in both cases it is slightly better at predicting for men than for women. For the interpersonal skills program, there is a considerable improvement in predictive ability for women with children, although even that model does not reach an acceptable level of statistical significance (P = 0.093). Both these programs are as likely to appeal to people trying to improve ‘poor health’ as to people trying to maximize current states of ‘good health’. It is not surprising, therefore, that generalized health perceptions and intentions to change, rather than specific health risks, are more important motivational influences in these two programs. While men and women are equally likely to participate in these health promoting programs and are influenced by the same types of motivational measures, the way in which those factors affect men and women are again very different: Table 4 indicates that women participate in the interpersonal skills program when they perceive themselves to be more healthy, while men participate when they perceive themselves as less healthy. And exercise appeals to men committed to changing their health (intention to change B = 0.285**) while interpersonal skills development appeals to women commited to health change (intention to change B = 0.287**). These intentions to change can occur because of ‘risk’ perceptions or because of a proactive attitude toward health among those who are already healthy. By combining people with different health values, e.g. those who are health ‘risk’ motivated and those who are pro-active health ‘maximizers’, the models for these programs may mask very different motivational influences. As a preliminary test of this hypothesis, the model for exercise participation was run separately, within each sex, for people who perceive themselves as already ‘healthy’ (very good to excellent health)--and therefore most likely to be health maximizers-and for those who perceive themselves as less ‘healthy’ (fair to good health)--and therefore most likely to be risk motivated. While this preliminary test of the ‘health maximizing’ hypothesis lacks the measure of health values needed to test for interactions between health values, health perceptions and risk based influence, it can at least reveal whether such interactions seem likely. For the exercise program, the model predicts 47% of the variance in participation by men who perceive themselves as less healthy and are therefore assumed to be ‘risk driven’ (N = 86; adjusted Rsquare = 0.248, P = 0.017). These men participate primarily because they have a strong intention to improve their ‘poor health’ (B = 0.305). yet the only

SPILMAN

possible ‘health risk’ effect is low job status (B = -0.277) [41]. Consistent with the hypothesis. this risk-based model cannot predict at all for men who perceive themselves as already healthy, e.g. those most likely to be health maximizers if such an attitudinal measure were available (N = 68: adjusted R-square = -0.057. P = 0.742). This separate analysis of those with healthy and less healthy self perceptions also improves prediction for women, but the relationships are just the opposite: the model helps to explain exercise participation for women who perceive themselves as healthy (N = 69; adjusted R-square = 0.220. P = 0.027) but does not predict for women who perceive themselves as less healthy (N = 50: adjusted R-square = 0.042, P = 0.389). Healthy women participants had no previous intention to change their health behaviors (B = -0.434) suggesting that they are nor actively seeking maximum health. On the other hand, their combination of a strong sense of personal control over health (B = 0.400) with less formal education (B = -0.336) and less health knowledge (B = -0.387) suggests that these women might have been particularly susceptible to TLC promotions about the health-enhancing benefits of exercise [42]. For the interpersonal skills program, it is more difficult to distinguish people who might already have good interpersonal skills from those who are ‘at risk’ of poor social functioning. The mode1 was run separately, within each sex, by health perception; for people who score at or below the mean and those who score above the mean on type A behaviors; and for people with and without strong social support networks. None of these distinctions could improve prediction, probably because none can adequately distinguish ‘risk’ motivated participants from those wanting to ‘maximize’ their social skills. It was therefore not possible to test the hypothesis that the risk motivated have different patterns of influence from health maximizers. Nevertheless. the analyses of exercise participants suggest that there may be interactions between health values, health or risk perceptions, and intentions to change that are worth further investigation, and that such interactions are likely to differ for men and women. DlSCUSSlON

The factors that affect women’s participation are different from those affecting men’s participation in nearly every program. Among women patterns affecting participation are different for women who are mothers compared to women who are not. These variations indicate that gender-based socialization experiences and gender-specific roles have profound effects on how people perceive and respond to ‘health’ issues. This research was not designed to test any particular theories for why women have a greater tendency to engage in health promoting behaviors. But the findings are consistent with some aspects of both ‘medical expansionist’ and ‘nurturant role’ explanations. A medical expansionist view maintains that women

Gender differences in worksite health promotion have historically been encouraged by the medical community to define a wider variety of their life concerns as ‘medical’ problems [34,43453. Because they have more ‘medically defined’ problems, women would be expected to monitor their ‘health’ status more closely and pay more attention to treating poor health. These data provide some support for this theory, in that women participate in more health promotion programs and their rate of participation is greater in three of the four programs devoted to treating ‘medical’ problems (weight loss, smoking, low back pain). And in the fourth ‘treatment’ oriented program (stress control), women differ from men by participating for ‘medically defined’ reasons (high type A behaviors and high blood pressure vs low job status and youth). In addition, women participate in the weight loss program even without evidence of being overweight. This is consistent with the medical expansionist theory that issues of concern primarily to women (e.g. feminine beauty) have been defined by health professionals as medical issues, thus making women appear to be more concerned with ‘health’. On the other hand, medical expansionist theory implies that women are encouraged to perceive themselves as less ‘healthy’ than men. Yet, the women in this sample are no more likely than the men to perceive themselves as unhealthy, and they are no more likely to participate when they have poor health perceptions. The medical expansionist view, therefore, receives qualified support from these data: women do tend to be more interested in health treatment programs, not because they perceive themselves as generally less healthy than men do, but possibly because they monitor their physical health more closely (and respond accordingly) and possibly because some of women’s pre-existing ‘feminine’ concerns have been defined as ‘health’ concerns. A ‘nurturant role’ perspective holds that women’s gender role defines them as health care-takers for themselves and their families [28,34,46]. This theory implies that, because of their nurturing responsibilities, women should be more responsive to health information, should know more about health, and should have a greater sense of personal responsibility for and control over health outcomes. Moreover, women with more nurturing responsibilities (married and those with children) should have more health knowledge and a greater sense of health control. The evidence supporting a nurturant role explanation is also mixed: consistent with the theory, the women in this sample do have a slightly higher score on health knowledge than the men, although married women and women with children have no greater knowledge than those who are unmarried or without children. On the other hand, healthy women did tend to participate in the exercise program when they had no previous intention to do so but were relatively lacking in previous health knowledge. This suggests that women may be more responsioe than men to health-related information. Contrary to expectations from nurturant role theory, the women in this sample do not have a greater sense of control over health outcomes than do their

533

male counterparts. And married women and those with children have no greater sense of health control than do unmarried women or women without children. However, women’s participation in all programs is consistently encouraged by a stronger sense of health control, while men’s participation is consistently discouraged by health control beliefs. And in three of the four programs in which women with children differ from women without children, health control belief has a greater positive effect on participation for women with children compared to those without. Because these health control effects reach statistical significance only in the stress (for women) and weight (for men) programs, these data provide only weak support for this aspect of nurturant role theory. Nevertheless, this is the only explanatory factor that consistently differentiates women’s participation patterns from men’s (and mothers’ from nonmothers’). For this reason, and because other research has found health control belief to have important effects on health behaviors [S, 32,47-49], this concept and the role theory from which it is derived requires additional research. CONCLUSIONS

The intentioned behavior model used here is more successful at modeling participation in ‘illness’ treatment programs than in programs also aimed at maximizing good health. In order to improve modeling for this latter type of program, future research should measure health value orientations, as factors likely to differentially affect program selection and rate of participation. In addition, the risk factors most likely linked to these general ‘health’ improving programs (for those who are risk driven) should be very clearly specified. This first attempt to model actual participation in worksite health promotion programs has also revealed that gender is a critical factor influencing participation patterns. Consistent with a medical expansionist theory, women participate in more programs, and they participate more actively than men in ‘health treatment’ programs. And in at least one case it appears that women’s greater participation is motivated by ‘feminine aesthetic’ concerns that are perceived as ‘health promotion’ by the medical community. There was also some support for a ‘nurturant role’ theory, with weak indications that women are more likely to participate in any program when they have a positive sense of health control and that women with children are more influenced by a health control belief than are women without children. Research designed to test ‘nurturant role’ theory will need a more precise measure of this health control belief, a more comprehensive measure of health knowledge, and an improved measure of parental role responsibilities. Testing a ‘medical expansionist’ theory will require a ‘health saliency’ measure that could determine whether women are concerned with a larger number of health issues and whether their concern is primarily for health reasons or for other ‘feminine role’ related reasons. This research has revealed significant differences in the factors that influence worksite health promotion

534

M. ‘A.

behaviors for white collar corporate men and women. Additional research with very different populations is needed to determine how widespread these gender differences are. Any model that incorporates lifestyle risk factors should find not only gender differences, but differences among social and occupational classes as well. An understanding of these gender-by-class influences is critical if health professionals are to be successful at recruiting, retaining, and encouraging good health among working men and women. Acknowledgements-The data from the TLC program have been made available for analysis because of the efforts and support of Dorothea Johnson, M.D., John Kroll, M.D.,

and Molly McCauley, R.N., B.S., in the AT&T Medical Department. Stan provided statistical

DeViney, support.

Ph.D.,

Business

Research,

REFERENCES 1. Berkman

L. and Breslow L. He&h and Ways of living. Oxford University Press, New York, 1983. 2. Burnett M. Genes, Dreams, and Realities. Basic Books, New York, 1971. 3. Fielding J. E. Health promotion and disease prevention at the worksite. A. R-ev. publ. Hlrh 5, 237-265, 1984. 4 Haggerty R. J. Changing life styles to improve health. Prevent. Med. 6, 276-289, 1977. M. and Neutra R. Relationship of health 5 Jordan-Marsh locus of control to lifestyle change programs. Res. Nursg Hlrh 8, 3-11, 1985. Knowles J. H. The responsibility of the individual. Daedalus 106, 57-80, 1977. Lelonde M. A New Perspective on the Health of Canadians. Information Canada, Ottowa, 1975. United States Department of Health, Education and Welfare. Forward Plan for Health FY 1977-1981. DHEW Publication No. (OS) 76-50024, pp. 97-121. Washington, D.C., 1975. 9. National Center for Health Statistics, Monrhly VimI Stntistics Report (Table 6). Hyattsville, Md, 1985. J. A hazardous society? The role of epi10. Higginson demiology in determining the individual versus group risk in modern societies. Rosenhaus Lecture, Presented at the American Public Health Association Annual Meeting, Chicago, Ill., 17 November, 1975. 11 Kristein M. How much can business expect to profit from smoking cessation? Prevent. Med. 12, 358-381, 1983. M. H. Cancer-A social disease? Paper 12 Schneiderman presented at the Interagency Collaborarive Group on Environmenral Curcinogenesis, National Institutes of Health, Bethesda, Md, 11 June, 1975. K. H., Pollock M. L., Martin R. P. er al. 13 Cooper Physical fitness levels vs. selected coronary risk factors: a cross sectional study. J. Am. med. Ass. 236, 116169, 1978. 14. Fox S. M.. Naughton I. P. and Haskell W. Physical activity and the prevention of coronary heart disease. Ann. Clin. Res. 404432, 1971. 15. Peters R. K., Lee L. D., Bischoff D. P. ef nl. Physical fitness and subsequent myocardial infarction in health workers. J. Am. med. Ass. 22, 3052-3056, 1983. 16. Mumford E., Schlesinger H. and Glass G. The effects of psychological intervention on recovery from surgery and heart attacks: an analysis of the literature. Am. J. publ. Hlth 72, 141, 1982. 17. Elliott G. and Eisdorfer C. (Eds). Srress ond Human Health. Springer, New York, 1982. 18. Davis M. F.. Rosenberg K., lverson D. C., Vernon T. M. and Bauer J. Worksite health promotion in Colorado. Publ. Hhh Rep. 99, 538-543, 1984.

SPILMAN

19. Rosen R. Washington business group on health (editorial)., Corp. Comment. l(4), June 1985. oro20. Fielding J. E. and Breslow L. Health promotion grams sponsored by California employers. Am. J. publ. Hlth 73. 538-542. 1983. 21. Gibbs J. 0.. Mulvaney D., Henes C. and Reed R. W. Work-site health promotion. Five-year trend in employee health care costs. J. occup. Med. 27, 826830. 1985. 22. Rogers P. J., Eaton E. K. and Bruhn J. G. Is health promotion cost effective? Prevenr. Med. 10, 324339. 1981. 23. Baun W. B.. Bemacki E. J. and Tsai S. P. A preliminary investigation: Effect of a corporate fitness program on absenteeism and health care cost. J. occup. Med. 28, 18-22, 1986. 24 Spilman M. A., Goetz A., Schultz J.. Bellingham R. and Johnson D. Effects of a corporate health promotion program. J. occup. Med. 28, 285-289. 1986. 25 Wilbur C. S. The Johnson & Johnson program. Precenr. Med. 12, 672681. 1983. 26 Davis K. E., Jackson K. L., Kronenfeld J. J. and Blair S. N. Intent to participate in worksite health promotion activities: a model of risk factors and psychosocial variables. Hlth Educn Q. 11, 361-377, 1984. 27 Ossorio P. G. An outline of descriptive psychology for personality theory and clinical applications. In Advances in Descriptive Psychology (Edited by Davis K. E.), Vol. 1, pp. 57-81. Greenwich, Conn.. 1981. 28 Lewis C. E. and Lewis M. A. The potential impact of sexual equality on health. New’ Bngl. J. Med. 297, 863-869, 1977. 29. Nathanson C. A. Sex roles as variables in the interpretation of morbidity data: a methodological critique. Inr. J. Epidem. 7, 253-262, 1978. 30. Waldron I. Sex differences in illness incidence, prognosis and mortality: Issues and evidence. Sot. Sci. Med. 17, 1107-l 123, 1983. 31. Women are less likely than men to engage in many health-threatening behaviors [30,32-341, and they are more likely than men to use preventive medical services [30,33,34] and to engage in a host of healthmaintaining behaviors, such as taking vitamins and brushing their teeth [35, 361, going to the dentist [34], having blood pressure checks [30] and polio immunizations [34]. 32 Waldron I. Why do women live longer than men? Sot. Sci. Med. 10, 349-362, 1976. 33 Mechanic D. and Cleary P. D. Factors associated with the maintenance of positive health behavior. Prevent. Med. 9, 805-814, 1980. 34. Nathanson C. A. Sex roles as variables in preventive health behavior. J. commun. Hlth 3, 142 1977. 35. Verbrugae L. M. Sex differentials in health. Publ. H&h Rep. 97;~417, 1982. 36. U.S. National Center for Health Statistics. Basic data from wave I of the national survey of personal health practices and consequences. Vital Hlfh Star. Series 15, No. 2, 1981. 37. Becker M., Maiman L., Kirscht J. et al. The health belief model and dietary compliance: a field experiment. J. Hhh sot. Behav. 18. 348-366. 1977. 38. Martial and parental status are especially important variables to include when examing health related behaviors. Marital status is related to lower morbidity and mortality for men and women [39]. And there is evidence that having children “disinclines women to adopt the sick role” (39, p. 601. Of particular relevance here is the evidence that, in spite of their greater role responsibilities, working women with children use fewer disability days but make more use of medical services than their counterparts without children [39]. This suggests that the parent role in itself may have

Gender

differences

in worksite

some positive impact on women’s health related behaviors. 39. Nathanson C. A. Illness and the feminine role: a theoretical review. Sot. Sci. Med. 9, 57-62, 1975. 40. The models were also run separately for married and unmarried women to determine whether having children was a more important interaction variable than being married. Prediction of stress, low back pain and interpersonal skills participation was somewhat better when the break was having children rather than being married. Only in the smoking program does the model predict better for married women than for women with children. Generally, the married and having children patterns were similar and the unmarried and no children patterns were similar, with the differences being primarily in the strength of the coefficients. However, there were some differences in the pattern of effects, depending on which split was used. For instance, social support is not an important predictor in either stress or smoking participation when married status is used instead of having children. It appears, then that there may be some additional interactions between marital status, having children, and type of program that would be worth exploring in a larger data set, which should contain all these variables for both men and women.

health

promotion

535

41. Other standardized coefficients, significant at 0.05 level were: married (B = 0.279); and Protestant (B = 0.266). 42. Other standardized coefficients, significant at 0.05 level: age (B = 0.295); told had high blood pressure (B = -0.363); social support (B = -0.288). 43. Boston Women’s Health Collective. Our Bodies Ourselves: A Book By and For Women. Simon & Schuster, New York, 1972. 44. Chesler P. Women and Madness. Doubleday, Garden City, N.Y., 1972. 45. Ehrenreich B. and English D. Wirches. Midwives and Nurses: A History of Women Healers. Feminist Press, Old Westbury, N.Y., 1972. 46. Becker M. and Green L. A family approach to compliance with medical treatment: A selective review of the literature. Int. J. Hifh Educn, 18, 173-182. 1975. 47. O’Connell J. and Price J. Health locus of control of physical fitness program participants. Percept. Moror Skills 55, 925-926, 1982. 48. Slenker S., Price J. and O’Connell J. Health locus of control of joggers and nonexercisers. Percepr. Motor Skills 61, 323-328, 1985. 49. Wallston K., Wallston S. and DeVellis R. Development of the multidimensional health locus of control (mhlc) scales. Hlfh Educn Monogr. 6, 16Gl71, 1978.