Discussion questions

Description

Part 1

TEDtalk Discussion: View the TEDTalks below and post your takeaway thoughts from each of the TEDtalks (Post minimum 3 paragraphs for each)

https://www.ted.com/talks/yassmin_abdel_magied_wha…

https://www.ted.com/talks/mary_bassett_why_your_do…

Part 2

Use the principles of the health belief model (assigned reading) to argue what may have been some barriers to following provider recommendations in each case. Then provide some strategies to facilitate health promotional and illness prevention practices in the select case.

Case 1: A 35-year-old Muslim woman who has newly immigrated to the US is seen in the ER for complains of severe abdominal pain. Her husband accompanies her and is speaking on her behalf. She is referred to a Gyn specialist after it is determined she will need to be seen for a pap smear and cervical cancer screening. They call to make an appointment and find out the OB/Gyn is a man. Since she no longer experiences pain, they decide she no longer needs to be seen.

Case 2: A family brings their 10-year-old to the pediatrician for a well-child visit. The pediatrician is concerned about the child’s weight and sits down to discuss this with the family. The child is overweight with high blood pressure for his age. The pediatrician prescribes a low-fat diet and makes some dietary suggestions and increase in physical activity. When they return in 6 months, there is no changes made to the child’s weight, eating habits, and physical activity.

Part 3

After reading the articles for this week that describes how certain groups are perceived, discuss the following:

Do any of the descriptions provided in the 2 articles about steroetyping surprise you? If so, why and if not, what have you heard or observed?
In what aspects of life other than health care do these stereotypes serve these cultural groups? What barriers can you see these groups facing as a result of these stereotypes?
Reflect on what you are taking away from these readings and how this might impact the way you interact with people who fall in these or other cultural groups?
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The Health Belief Model: A Decade Later
Nancy K. Janz, RN, MS
Marshall H. Becker, PhD, MPH
Since the last comprehensive review in 1974, the Health Belief Model (HBM) has continued
to be the focus of considerable theoretical and research attention. This article presents a critical
review of 29 HBM-related investigations published during the period 1974-1984, tabulates the
findings from 17 studies conducted prior to 1974, and provides a summary of the total 46 HBM
studies (18 prospective, 28 retrospective). Twenty-four studies examined preventive-health behaviors (PHB), 19 explored sick-role behaviors (SRB), and three addressed clinic utilization.
A “significance ratio” was constructed which divides the number of positive, statisticallysignificant findings for an HBM dimension by the total number of studies reporting significance
levels for that dimension. Summary results provide substantial empirical support for the HBM,
with findings from prospective studies at least as favorable as those obtained from retrospective
research. “Perceived barriers” proved to be the most powerful of the HBM dimensions across
the various study designs and behaviors. While both were important overall, “perceived susceptibility” was a stronger contributor to understanding PHB than SRB, while the reverse was
true for “perceived benefits.” “Perceived severity” produced the lowest overall significance
ratios; however, while only weakly associated with PHB, this dimension was strongly related
to SRB. On the basis of the evidence compiled, it is recommended that consideration of HBM
dimensions be a part of health education programming. Suggestions are offered for further
research.
INTRODUCTION
In 1974, Health Education Monographs devoted an entire issue to &dquo;The Health
Belief Model and Personal Health Behavior.&dquo;’ This monograph summarized findings
from research applying the Health Belief Model (HBM) as a conceptual formulation
for understanding why individuals did or did not engage in a wide variety of healthrelated actions, and provided considerable support for the model.
During the decade that has elapsed since the monograph’s publication, the HBM
has continued to be a major organizing framework for explaining and predicting
acceptance of health and medical care recommendations. The present article provides
Nancy K. Janz is Research Associate, and Marshall H. Becker is Professor and Chair,
Department of Health Behavior and Health Education, The University of Michigan.
Address reprint requests to Nancy K. Janz, RN, MS, Department of Health Behavior and
Health Education, The University of Michigan, School of Public Health, 1420 Washington
Heights, Ann Arbor, MI 48109.
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investigations conducted since 1974, and subsequently combines these results with earlier findings to permit an overall assessment of the model’s
performance to date.
a critical review of HBM
Dimensions of the Model
The HBM was developed in the early 1950s by a group of social psychologists at
the U.S. Public Health Service in an attempt to understand &dquo;the widespread failure of
people to accept disease preventives or screening tests for the early detection of
asymptomatic disease&dquo; ; it was later applied to patients’ responses to symptoms,~ and
to compliance with prescribed medical regimens
The basic components of the HBM are derived from a well-established body of
psychological and behavioral theory whose various models hypothesize that behavior
depends mainly upon two variables: (1) the value placed by an individual on a particular
goal; and (2) the individual’s estimate of the likelihood that a given action will achieve
that goal.’’ When these variables were conceptualized in the context of health-related
behavior, the correspondences were: (1) the desire to avoid illness (or if ill, to get
well); and (2) the belief that a specific health action will prevent (or ameliorate) illness
(i.e., the individual’s estimate of the threat of illness, and of the likelihood of being
able, through personal action, to reduce that threat).
Specifically, the HBM consists of the following dimensions.’
Perceived susceptibility.-Individuals vary widely in their feelings of personal vulnerability to a condition (in the case of medically-established illness, this dimension
has been reformulated to include such questions as estimates of resusceptibility, belief
in the diagnosis, and susceptibility to illness in general’). Thus, this dimension refers
to one’s subjective perception of the risk of contracting a condition.
Perceived severitv.-Feelings concerning the seriousness of contracting an illness
(or of leaving it untreated) also vary from person to person. This dimension includes
evaluations of both medical/clinical consequences (e.g., death, disability, and pain)
and possible social consequences (e.g., effects of the conditions on work, family life,
and social relations).
Perceived benefits.-While acceptance of personal susceptibility to a condition also
believed to be serious was held to produce a force leading to behavior, it did not
define the particular course of action that was likely to be taken; this was hypothesized
to depend upon beliefs regarding the effectiveness of the various actions available in
reducing the disease threat. Thus, a &dquo;sufficiently-threatened&dquo; individual would not be
expected to accept the recommended health action unless it was perceived as feasible
and efficacious.
Perceived barriers.-The potential negative aspects of a particular health action
may act as impediments to undertaking the recommended behavior. A kind of costbenefit analysis is thought to occur wherein the individual weighs the action’s effectiveness against perceptions that it may be expensive, dangerous (e.g., side effects,
iatrogenic outcomes), unpleasant (e.g., painful, difficult, upsetting), inconvenient,
time-consuming, and so forth.
Thus, as Rosenstock notes, &dquo;The combined levels of susceptibility and severity
provided the energy or force to act and the perception of benefits (less barriers) provided
a preferred path of action. &dquo;8 However, it was also felt that some stimulus was
necessary
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to trigger the decision-making process. This so-called &dquo;cue to action&dquo; might be internal
(i.e., symptoms) or external (e.g., mass media communications, interpersonal interactions, or reminder postcards from health care providers). Unfortunately, few HBM
studies have attempted to assess the contribution of &dquo;cues&dquo; to predicting health actions.
Finally, it was assumed that diverse demographic, sociopsychological, and structural
variables might, in any given instance, affect the individual’s perception and thus
indirectly influence health-related behavior. The dimensions of the Health Belief Model
are depicted in Figure l.
. Review Procedures
The following criteria were established for the present review: ( 1 ) only HBM-related
investigations published between 1974 and 1984 were included; (2) the study had to
contain at least one behavioral outcome measure; (3) only findings concerning the
relationships of the four fundamental HBM dimensions to behaviors are reported; and
(4) we chose to limit our literature survey to medical conditions (thus, no dental studies
are reviewed), and to studies of the health beliefs and behaviors of adults (the cor-
responding literature for children has recently been examined9).
Results in Table I have been grouped under three headings: ( 1 ) preventive health
behaviors (actions taken to avoid illness or injury); (2) sick-role behaviors (actions
taken after diagnosis of a medical problem in order to restore good health or to prevent
further disease progress); and (3) clinic-visits (clinic utilization for a variety of reasons).
Within each medical category, studies are presented chronologically.
REVIEW OF STUDIES
Preventive Health Behaviors
-a
Influenza
Obtaining vaccination against infectious diseases represents precisely the kind of
preventive health behavior toward which the archetypical HBM was directed, and the
expected outbreak of Swine influenza in 1976 presented a unique opportunity to assess
the model. Overall, we have identified four investigations 10-13 published since 1974
that have applied the HBM in attempts to understand vaccination behavior; three of
these studies concerned Swine Flu, and one dealt with influenza.
Aho’° surveyed the health beliefs and Swine Flu inoculation status of 122 randomlyselected senior citizens (primarily black and Portuguese-American) who were active
members in two senior centers. A 45-item interview schedule elicited respondents’
beliefs along all of the major HBM dimensions.
Findings indicated that HBM variables were able to distinguish inoculation program
participants from nonparticipants, and these relationships were statistically significant
for &dquo;susceptibility,&dquo; &dquo;efficacy,&dquo; and &dquo;safety.&dquo; However, interpretation of the &dquo;severity&dquo;
dimension is more problematic. Two parts of the study interview gathered information
concerning this dimension: a question about whether or not the respondent had ever
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>< 0 W 0. .. S .~ ~ - 0 ü G 8.1111 2~ tZ ii d ,.. tings). The investigation focused on &dquo;perceived susceptibility&dquo; and &dquo;perceived benefits&dquo;; these beliefs were assessed using an instrument developed for this purpose by Stillman.&dquo; A self-administered questionnaire obtained both beliefs and reports on the practice of BSE. Compliance was dichotomized as &dquo;indicated they practiced BSE&dquo; versus &dquo;never practiced BSE.&dquo; Results revealed positive, significant correlations between the subscale scores for &dquo;susceptibility&dquo; and &dquo;benefits&dquo; and the practice of BSE, with the correlation for &dquo;benefits&dquo; about twice that obtained for &dquo;susceptibility.&dquo; Together, these beliefs accounted for 10% of the explained variance in practice. The &dquo;purposive&dquo; nature of the sample and retrospective design limit interpretation and generalizability of these findings. An additional difficulty is created by the dichotomization of the dependent variable so that women were classified as &dquo;practicers&dquo; regardless of frequency of performance of BSE (the author notes that such frequency ranged from less than once a year to more than once a month). Two other studies 11B.19 have included HBM variables in retrospective surveys seeking correlates of BSE knowledge and behavior. However, the fact that one focused solely on BSE-related knowledge and the other did not provide direct comparisons of examiners and nonexaminers precluded the listing of these investigations’ findings in Table 1. Manfredi and her colleagues’~ found that, in a sample of 696 black innercity women, belief in the efficacy of early disease detection (i.e., &dquo;benefits&dquo;) was &dquo;the strongest correlate of the ability to perform BSE,&dquo; and that &dquo;independent effects of fear as reflected in perceived threat and feelings of personal susceptibility were also apparent.&dquo; Finally, comparing examiners with nonexaminers in a population of 158 women seeking care for a breast concern (e.g., lump, pain), Kellyl9 learned that practicers had two major reasons for both initiating and maintaining BSE: &dquo;an awareness that it is desirable to detect breast cancer early&dquo; (i.e., &dquo;benefits&dquo;), and &dquo;an awareness that they themselves could get breast cancer&dquo; (i.e., &dquo;susceptibility&dquo;). She also found another major reason for not performing BSE was agreement with the statement &dquo;selfexamination is too frightening&dquo; (i.e., &dquo;barriers&dquo;). It is interesting to note that, across three BSE studies involving very disparate populations and points in time, perceived 19 susceptibility and perceived efficacy were consistently associated with BSE knowledge and practice. Only one study appears to have focused on the HBM as a predictor of participation in a high blood pressure (HBP) screening program. Using a prospective survey design, King2’ mailed questionnaires to 160 randomly-selected patients at a Health Centre in England who, approximately four days earlier, had received a letter from their GP &dquo;advising them to attend a screening for raised blood pressure.&dquo; Ultimately, HBM data were available for 73 attenders and 29 nonattenders. The investigator wished to examine the predictive value of a larger hypothetical model representing a synthesis of the HBM and attribution theory (specifically, the general and specific causal attributions which the subjects gave to the illness). Here, attributions are viewed as antecedents of the HBM variables. Zero-order correlations yielded significant associations between attendance and both perceived susceptibility to HBP and perceived benefits of screening. In addition, discriminant function analysis revealed &dquo;costs/barriers to screening&dquo; to be a significant predictor of attendance. Finally, although &dquo;perceived severity of HBP&dquo; did not directly predict participation, it was found to be significantly related to the study’s measure of &dquo;behavioral intention,&dquo; which, in turn, was an excellent predictor of attendance. The larger model proposed by King was further supported by the finding that several attribution variables were also significantly and directly related to attendance. Perhaps foremost among this study’s limitations is the potential confounding affect of the GPs letter inviting participation in the screening program (e.g., it limits the subjects, may have accounted for the relatively high attendance, and may even have had a subtle effect on subjects’ health beliefs). On the other hand, this letter may have introduced a conservative bias by enlisting the participation of patients whose health beliefs alone would otherwise have been insufficient to motivate attendance. Other methodological limitations include a relatively small sample of &dquo;noncompliers&dquo; and the fact that the main analyses did not control for the potentially confounding effects of previous HBP screening. Risk-Factor Behaviors In attempting to examine degree of consistency among an individual’s preventive health behaviors (PHBs), Langlie21 also assessed the ability of the HBM to account for variation in these behaviors. A questionnaire was sent to a systematic random sample of the adult population of Rockford, Illinois; telephone and personal follow383). &dquo;Perceived vulnerability&dquo; up was conducted to attain a response rate of 62% (n was operationalized by asking respondents to estimate how likely they were, during the next year, to experience each of a list of untoward health events (e.g.: be in a car accident; get cancer; get an electrical shock; get polio; feel nervous). &dquo;Perceived benefits&dquo; was the respondent’s extent of agreement with statements about the potential benefits of various PHBs (e.g.: eating fruit daily; dental checkups; daily exercise; sharing drinking cups; immunizations). Finally, &dquo;perceived barriers/costs&dquo; was measured by asking respondents how difficult it would be to engage in each of 12 different PHBs (e.g.: wear seat belts; exercise; obtain immunizations; get checkups). The remaining HBM dimension, &dquo;perceived severity,&dquo; was not measured in this study. PHB = 20 was measured by 1 l additive scales: driving behavior; pedestrian behavior; smoking behavior; personal hygiene: seat belt use, medical checkups; dental care; immunizations ; screening exams; exercise behaviors; and nutrition-related behaviors. Using factor analysis, Langlie divided these behaviors into two scales: &dquo;Direct Risk&dquo; PHB (DR) and &dquo;Indirect Risk&dquo; PHB IIR) (see listing in Table 1). Respondents were also classified as &dquo;behaviorally consistent&dquo; if a minimum of 8 of the 11 subscale scores were either above the mean for his/her sex, or below the mean, or within one standard deviation of the mean. or &dquo;behaviorally inconsistent&dquo; if their scores were about equally distributed above and below the mean, or if the respondent was missing more than one subscale score. For &dquo;behaviorally consistent&dquo; subjects, Langlie notes that &dquo;the hypothesized zeroorder relationships [for the HBM variables] are generally supported by our data; the major exception is that loit, rather than high levels of perceived vulnerability are associated with appropriate PHB.&dquo; This significant but negative association may be due to the retrospective nature of the study, wherein individuals who had already undertaken appropriate PHBs were being asked to estimate the likelihood that they would soon incur the negative health event that the particular PHB was designed to protect against (e.g., respondents who had been immunized against polio were being asked how likely it was that they could get polio in the next year). Both &dquo;benefits&dquo; and &dquo;barriers&dquo; were significantly and positively related to DR and IR PHBs. For &dquo;behaviorally inconsistent&dquo; respondents, the trend was essentially the same; however, only &dquo;perceived benefits&dquo; was significantly correlated with the dependent variables. Langlie summarizes her findings relevant to the HBM by stating that &dquo;The data support the hypothesis that the greater the number of appropriate social-psychological characteristics possessed the more likely the individual is to engage in PHB. This relationship is more pronounced among consistents than among inconsistents and for Indirect than for Direct Risk PHB. Possession of a particular constellation of attributes is more important than quantity per se, however. Regardless of their scores on the other scales, 85% of those persons who score above the mean on the Perceived Benefits, Perceived Barriers, and Attitudes Scales (n = 73) have above average Indirect Risk PHB compared to only 19% of those who score low on all three of these scales (n 42).&dquo; Besides its retrospective design, this investigation contains a number of important conceptual and methodologic difficulties. ( 1 ) Many of the PHBs were operationalized in unusual ways; for example, &dquo;exercise&dquo; referred to &dquo;number of blocks walked yesterday, chooses to walk to third floor rather than use elevator&dquo;; &dquo;nutrition&dquo; measured intake of vitamins A and C and protein (rather than asking about caloric or fat intake); &dquo;personal hygiene&dquo; included such items as &dquo;avoids coughing people&dquo; and &dquo;doesn’t pick pimples&dquo;. (2) Inspection of the factor analysis reveals that among the &dquo;behaviorally consistent,&dquo; smoking does not fit particularly well in the dimension labeled Direct Risk-and in a similar manner, a low-loading &dquo;exercise&dquo; is included in the Indirect Risk PHB group. Indeed, the analyses seem to show three (rather than two) dimensions of PHB. (3) There was relatively little variation in DR PHB as measured in this research (most of the respondents were found to have high scores on this dimension). (4) There appears to be no conceptual justification for the arbitrary labels &dquo;Direct&dquo; and &dquo;Indirect&dquo; PHB. In August, 1976 and January, 1977, Aho22 used random digit dialing to conduct telephone interviews of 1,046 persons residing in Rhode Island (combined sample = 21 77%). The 24-item interview focused on the behaviors &dquo;cigarette smoking,&dquo; &dquo;being overweight/underweight,&dquo; and &dquo;regular participation in physical activity.&dquo; For the first two behaviors, Aho asked about &dquo;perceived seriousness,&dquo; while for the last behavior, the subject was asked about &dquo;perceived efficacy.&dquo; Analyses were performed separately for two age categories: subjects aged 65 and over, and those under age 65. For both age categories, a statistically-significant relationship was obtained between &dquo;seriousness&dquo; and smoking, and between &dquo;seriousness&dquo; and being overweight/underweight. With regard to physical activity, the &dquo;efficacy&dquo; variable was significant only for those under age 65 (the author attributes this lack of significance to the fact that some senior citizens are unable to perform regular physical activity because of their health status). Use of this study to evaluate the HBM is limited by its retrospective design and by its focus on only two HBM dimensions (and only one dimension was examined for each preventive health behavior). Rundall and Wheelei-2’ included HBM components among the independent variables they employed to examine use of preventive services (defined as number of physician visits for preventive care). The data came from a household survey of adult residents of Washtenaw County, Michigan; of the 854 interviews completed (response rate 69%), 781 were used for these analyses. A single question was employed to assess each HBM dimension: for &dquo;susceptibility&dquo;-&dquo;How likely do you think it is that you could get [each of four diseases: heart disease, stroke, high blood pressure, lung cancer] in the next five years’?&dquo;; for &dquo;severity&dquo;-&dquo;How much of an effect do you think [each disease] would make on a person’s life?&dquo;; for &dquo;efficacy&dquo;-&dquo;How much do you think a doctor, a dentist, or some other health professional can do to prevent [each disease]’?&dquo;; and, for &dquo;barriers&dquo;-each respondent was asked whether or not he/she had a &dquo;usual source of medical care.&dquo; The dependent variable was derived from responses to the question &dquo;About how often do you visit a physician for a checkup even though you may be feeling well?&dquo; Of the four HBM dimensions, two (&dquo;susceptibility&dquo; and &dquo;barriers&dquo;) were significantly correlated with obtaining preventive medical checkups. Because the investigators were also interested in determining the possible direct and indirect effects of sociodemographic characteristics and perceived health status on utilization, a path analysis was performed. All of the HBM variables were found to have statistically-significant direct paths to use; in addition, income was shown to have significant indirect effects on use through both &dquo;susceptibility&dquo; and &dquo;barriers.&dquo; (These findings are consistent with those obtained by Dutton. 24) Constraints on data interpretation include a retrospective design and the use of only one question to measure each HBM dimension. It should also be noted that, while &dquo;age&dquo; had a negligible direct effect on use, it had a very substantial path to &dquo;susceptibility,&dquo; suggesting that the &dquo;susceptibility&dquo; question (with its five-year time frame) was most meaningful to relatively older respondents. Tirrell and Hart2’ administered the Standardized Compliance Questionnaire26 to 30 patients who, six to eighteen months previously had undergone coronary artery bypass operations, and who had subsequently been given individualized exercise regimens. Nineteen questions addressed subjects’ health beliefs. Compliance was assessed by patients’ self-reports with regard to walking, a training &dquo;heart walk,&dquo; and pulse monitoring in other activities. A composite compliance score was also calculated. Only &dquo;perceived barriers&dquo; was significantly related to exercise compliance. While response rate = = . 22 the correlations for &dquo;perceived efficacy&dquo; were substantial, they failed to reach statistical significance (perhaps because of the small number of subjects studied). An unusual finding was the tendency toward a negative association between &dquo;susceptibility&dquo; and adherence. The authors note that &dquo;many of the patients in this survey gave an unusual response to the questions in this section. For example, many agreed with the statement, ’If you wait long enough, you will get over most any illness,’ because ’you’d die.and then you’d no longer be ill.’ Study limitations include: ( 1 ) a retrospective design; (2) a very small convenience sample; (3) self-reported assessments six to eighteen months after regimen prescription; and (4) nontraditional operationalization of some HBM components (e.g., &dquo;susceptibility&dquo; refers to general illness rather than to the untoward sequelae of noncompliance). The authors note that the subjects’ &dquo;unusual&dquo; responses raise questions about patient interpretation of the health belief items. ln an unusual application of HBM variables, Beck&dquo; examined possible relationships of attitudes and beliefs to drinking/driving behavior in a group of college students. Of 443 undergraduates in health education classes who had agreed to participate in a repeated survey, 272 (61%) completed questionnaires concerning their drinking and driving attitudes and practices. A second questionnaire was administered six weeks later. The HBM items were constructed with regard to two possible outcomes of drinking and driving that might be of concern to college students: &dquo;getting caught by the police,&dquo; and &dquo;causing an accident while driving under the influence of alcohol.&dquo; The behavioral outcome measure asked the respondent how often during the previous six weeks he/she had driven a car &dquo;while you were drunk or when you have known you’ve had too much to drink&dquo; (coded dichotomously). The manner in which the author reports the findings makes it difficult to examine clearly the relationships obtained between HBM dimensions and actual drinking and driving behavior. The HBM variables were found to be correlated (in the predicted direction) with concerns about getting caught by the police (significance levels not reported). A similar outcome was obtained between beliefs and &dquo;causing an accident,&dquo; except that, opposite to prediction, susceptibility to causing an accident while driving under the influence of alochol was positively related to doing so. The authors speculate that &dquo;the students may have adjusted their feelings of susceptibility in accordance with their previous and likely to be continued, drinking and driving behavior.&dquo; A number of study features render interpretation of these findings problematic. Perhaps most important is the unique and nontraditional manner in which belief dimensions were operationalized. For example, perceived &dquo;effectiveness&dquo; (i.e., benefits/ barriers) usually denotes an individual’s assessment of the value of undertaking the recommended health action (which in this instance would be not driving while intoxicated). However, this investigator measured this dimension in terms of how effective the student thought he/she would be &dquo;at avoiding being caught by the police&dquo; and &dquo;at avoiding being in an automobile accident&dquo; while driving after drinking. Moreover, an additional attitude item ( ... &dquo;for men, driving while under the influence of alcohol is: ... &dquo; followed by response scales of good-bad, awful-nice, harmful-beneficial, and wise-foolish) turned out to be the strongest predictor of actual behavior. While Beck employs this item to represent &dquo;attitude toward the act&dquo; in a model developed by Fishbein, it clearly could be interpreted as representing a substantial portion of the HBM. Additional difficulties include dichotomization of the dependent variables (so that possible relationships between health beliefs and frequency of inappropriate be&dquo; 23 havior cannot be assessed), use of a rather sui generis population of college undergraduates (thus limiting generalization of the findings), and the fact that only 107 of the original 272 participants completed the follow-up questionnaire (from which the measure of actual behavior was obtained). To see if beliefs might be useful in discriminating different levels of smoking behavior, Weinberger and his associateS28 interviewed 120 patients receiving care at the outpatient department of a municipal teaching hospital. Subjects were categorized as &dquo;ex-smokers,&dquo; &dquo;moderate smokers&dquo; (presently smoking 10 or fewer cigarettes per day), and &dquo;smokers.&dquo; With regard to health beliefs, respondents were asked about the reasons people should quit smoking, about the potential for negative outcomes of smoking on their own health, and about the likelihood that they would, in fact, experience such smoking-related health problems. Using multiple discriminant analysis, the investigators found ex-smokers significantly more likely to view smoking as a serious health problem and to feel personally susceptible to its potential adverse effects. Moderate smokers also perceive smoking as a serious threat to health, but did not see themselves as susceptible to smokingrelated health problems. Their two discriminant functions were able to correctly classify (i.e., with regard to category of current smoking status) 66% of the study participants. The authors conclude both that &dquo;certain attitudes can discriminate between groups of current smokers, as well as smokers from ex-smokers,&dquo; and that &dquo;in order to quit, it is not sufficient for persons to believe smoking is a serious health problem; they also must see themselves as personally susceptible to any adverse effects.&dquo; Among this study’s important limitations are: (1) its retrospective design; (2) restricted generalizability based on the sociodemographic characteristics of the sample (typical respondent described as &dquo;a 58-year-old black female who has smoked for 29 years&dquo;); and (3) the fact that only two of the HBM dimensions were evaluated. Croog and Richards2,} examined data on 205 postmyocardial infarction males over a period of eight years (patients’ wives were followed for one year) in order to examine the possible influences of sociodemographic, personality and attitudinal variables on smoking behavior. Although repeated reference is made in the article to &dquo;existing theoretical frameworks,&dquo; to the HBM, and to such variables as &dquo;threat,&dquo; &dquo;susceptibility,&dquo; and &dquo;belief in the efficacy of preventive action,&dquo; dimensions of the HBM appear never to have been operationalized (at least, not in any traditional fashion). For example, although the study concerns smoking behavior, &dquo;susceptibility&dquo; was assessed by asking the patient how often during the past month he had experienced various symptoms which might be associated with heart disease. At the end of their discussion, the authors state that &dquo;the conclusions of this study cannot be interpreted as testing the utility of health belief models.&dquo; Finally, one study;&dquo; used as its dependent variable the degree to which wives felt that they could play a role in helping their husbands avoid heart attacks; termed &dquo;preventive health orientation,&dquo; this variable was trichotomized as &dquo;very much,&dquo; &dquo;some,&dquo; and &dquo;a little or not at all.&dquo; Because there is no behavioral outcome assessed, this study is not included in Table 1. Area probability sampling techniques were used to select as survey subjects 199 wives living in Lebanon County, Pennsylvania; Aho used data from 187 of these subjects for his analyses. Findings from this retrospective survey indicated that wives’ HBM scores (husband’s susceptibility to heart attack; chances that a person with heart disease could lead a normal life; and belief that treatment for heart disease was effective) were related to 24 their &dquo;preventive health orientation&dquo; (belief that they could help to prevent heart attacks in their husbands). In addition, preventive health orientation was also related to whether or not these wives had ever suggested any health-related behaviors to their husbands. Sick-Role Behaviors A11lihypertensiB’e Regimens In hope of enhancing patient compliance with antihypertensive therapy, Inui and his colleagues&dquo; developed a tutorial aimed at physicians treating hypertensive patients at a General Medical Clinic of a large teaching hospital. Sixty-two clinic physicians were assigned to control or experimental groups by day of clinic attendance. Observations of physicians’ and patients’ characteristics, attitudes and behaviors were made before and after the experimental intervention. The physician tutorial emphasized strategies for increasing regimen adherence &dquo;based on the ’Health Belief Model’.... stressing the relation of patient ideas bearing on the seriousness of hi