Purpose: Anti-obesity prejudices affect the quality of care obese individuals receive. The authors sought to determine the prevalence of weight-related biases among medical students and whether they were aware of their biases.
Method: Between 2008 and 2011, the authors asked all third-year medical students at Wake Forest School of Medicine to complete the Weight Implicit Association Test (IAT), a validated measure of implicit preferences for “fat” or “thin” individuals. Students also answered a semantic differential item assessing their explicit weight-related preferences. The authors determined students’ awareness of their biases by examining the correlation between students’ explicit preferences and their IAT scores.
Results: Of 354 medical students, 310 (88%) completed valid surveys and consented to participate. Overall, 33% (101/310) self-reported a significant (“moderate” or “strong”) explicit anti-fat bias. No students self-reported a significant explicit anti-thin bias. According to the IAT scores, over half of students had a significant implicit weight bias: 39% (121/310) had an anti-fat bias and 17% (52/310) an anti-thin bias. Two-thirds of students (67%, 81/121) were unaware of their implicit anti-fat bias. Only male gender predicted an explicit anti-fat bias (odds ratio 3.0, 95% confidence interval 1.8–5.3). No demographic factors were associated with an implicit anti-fat bias. Students’ explicit and implicit biases were not correlated (Pearson r = 0.03, P = .58).
Conclusions: Over one-third of medical students had a significant implicit anti-fat bias; few were aware of that bias. Accordingly, medical schools’ obesity curricula should address weight-related biases and their potential impact on care.
Dr. Miller is associate professor, Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina.
Dr. Spangler is professor, Department of Family and Community Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina.
Dr. Vitolins is professor, Department of Epidemiology and Prevention, Wake Forest School of Medicine, Winston-Salem, North Carolina.
Mr. Davis is assistant professor, Department of Family and Community Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina.
Dr. Ip is professor, Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina.
Dr. Marion is professor, Department of Family and Community Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina.
Dr. Crandall is professor, Department of Physician Assistant Studies, Wake Forest School of Medicine, Winston-Salem, North Carolina.
Correspondence should be addressed to Dr. Miller, Wake Forest School of Medicine, General Internal Medicine, Medical Center Boulevard, Winston-Salem, NC 27157; telephone: (336) 713-4156; e-mail: email@example.com.
The prevalence of anti-obesity bias is on the rise in the United States.1 Obese individuals are frequent targets of inappropriate humor and discrimination,2–4 and Americans often associate obese individuals with negative attributes and laziness.5 On average, physicians share the general population’s strong anti-obesity bias.6 Over half of primary care physicians in one study, for example, reported viewing obese individuals as “awkward, unattractive, ugly, and noncompliant with therapy.”7 Even physicians who specialize in treating obese patients possess unconscious anti-obesity attitudes.8,9
Physicians’ prejudice in general and their anti-obesity bias specifically can damage the physician–patient relationship and influence treatment decisions, thus affecting the quality of patient care. Medical students have reported that obese individuals are frequent objects of derogatory humor by their peers, residents, and faculty physicians.10 Additionally, physicians and medical students are less likely to respect obese patients than normal-weight patients, and they are more likely to view obese patients negatively.11–14 Obese patients have reported that they notice these negative attitudes, describing that they receive derogatory comments from health professionals.15 This anti-obesity stigma contributes to obese individuals avoiding public exercise and seeking fewer preventive medicine services.16–20
A prerequisite to combating prejudice is first acknowledging its existence. However, we do not know to what extent medical students possess an anti-obesity bias and whether they are aware of that bias. To find that medical students possess significant anti-obesity biases would have important implications for medical educators developing curricula to equip students with the skills they need to prevent and manage obesity, a condition that now affects one-third of Americans.21
To that end, we first must distinguish between the two types of biases: explicit and implicit.22,23 Individuals are consciously aware of their explicit biases, which researchers can measure through individuals’ responses to survey items. In contrast, implicit biases operate on an unconscious level. An implicit bias can be seen in an individual’s first reaction or elicited emotion to a person, before conscious thought mediates the situation. Prior research shows that explicit and implicit biases are related but distinct constructs, and models that measure both are superior to models that attempt to capture preferences in general.24
Social scientists argue that most of our behaviors arise from automatic, unconscious (or implicit) reactions to stimuli in the environment.25–27 These implicit reactions derive from our past experiences, cultural norms, and values.26 Once we have the impulse to act, our conscious mind evaluates the impulse and chooses either to accept, reject, or modify it. Although both implicit and explicit biases guide behavior and are possible targets for educational interventions, implicit bias appears to be a better predictor of prejudice and discrimination.28 We conducted a three-year survey study to determine the prevalence of implicit (unconscious) weight-related biases among medical students and whether they were aware of those biases.
Study setting and participants
We conducted our study at the Wake Forest School of Medicine. As part of an obesity management educational grant from the National Cancer Institute, we asked all third-year medical students to complete the Weight Implicit Association Test (IAT) during their family medicine clerkship. Three consecutive classes of students completed the survey between 2008 and 2011. The Wake Forest Baptist Health institutional review board approved the study protocol, and all participants provided informed consent.
The IAT is a validated instrument that measures latent response times to determine individuals’ implicit (unconscious) biases.29–31 Several versions of the IAT are available to examine individuals’ implicit biases related to race, age, gender, and sexuality, among other topics. The IAT has been used in numerous previous research studies examining bias.28,30
The Weight IAT asks students to pair images of “thin” or “fat” people with negative or positive words using a computer keyboard. The IAT is based on the principle that individuals can group similar items more rapidly than they can disparate items. Therefore, if an individual associates a particular group with negative attributes, it will be harder for that person to rapidly pair members of that group with positive words. By measuring latent response times over multiple pairings (or “trials”), the IAT estimates an individual’s implicit preferences for one group over another (i.e., fat people versus thin people). Individuals complete a series of practice trials followed by 64 timed trials, which together require approximately 7 to 8 minutes to complete.
The IAT D score reflects the standardized mean difference in latency timings between groups of pairings. The D score can range from −2 to 2 but typically ranges from −1 to 1. On the basis of prior published studies, we used students’ D scores to stratify their implicit preferences as neutral (−0.15 to 0.15), slight (−0.16 to −0.35 or 0.16 to 0.35), moderate (−0.36 to −0.65 or 0.36 to 0.65), and strong (< −0.65 or > 0.65).32,33 We considered a student’s bias to be significant if his or her D score was “moderate” or “strong.” We considered IATs with an error rate of at least 30% on repeated measures invalid and excluded them from our analysis.34
Students also completed a semantic differential item assessing their explicit preferences for fat or thin individuals with possible answers on a seven-point Likert scale ranging from “I (strongly) (moderately) (slightly) prefer fat people to thin people” to “I like thin people and fat people equally” to “I (slightly) (moderately) (strongly) prefer thin people to fat people.” This semantic differential item is routinely included in IATs to measure explicit preferences.29 Scores for this item ranged from −3 (I strongly prefer fat people) to +3 (I strongly prefer thin people).
To determine which factors, if any, predicted a significant anti-obesity bias, we created logistic regression models for the outcomes of having a significant implicit (unconscious) anti-obesity bias and a significant explicit (conscious) anti-obesity bias. We included as covariates age (as a continuous variable), gender, race/ethnicity, and time in the academic year when the survey was taken (beginning of third year, middle of third year, end of third year). We conducted all analyses using SPSS software, version 19 (IBM Corporation, Armonk, New York) with two-sided tests and an alpha of .05.
We examined whether students were aware of their implicit bias in three ways. First, we compared students’ self-reported biases with their implicit (unconscious) biases (prefer fat, slight or no preference, prefer thin) using chi-square tests. Second, we used the Pearson correlation coefficient to determine whether students’ self-reported biases (measured on the seven-point Likert scale) predicted their implicit biases (measured by the difference in latency times from the IAT). Third, we reran our multivariate logistic regression model for implicit bias, including students’ explicit bias as a predictor variable to obtain the beta coefficient and significance level for explicit bias.
The three third-year medical school classes (2008–2009, 2009–2010, and 2010–2011) that we surveyed included 354 geographically diverse students, representing at least 25 different states and 12 countries outside the United States. Of those 354 students, 324 took the IAT and consented to let us use their data (response rate of 92%). Response rates for individual classes ranged from 88% (106/121) in 2010–2011 to 96% (114/119) in 2009–2010. We excluded 14 students’ surveys—4 were incomplete and 10 had too many errors—yielding a final sample size of 310 students (effective response rate of 88%). See Table 1 for complete demographics data.
Explicit (conscious) bias
The majority of students (72%, 223/310) reported that they preferred “thin” people to “fat” people, and approximately one-third (33%, 101/310) reported this preference was moderate or strong. In comparison, only 1% (4/310) of students reported any preference for fat people, and this preference was no greater than a slight preference. Only gender was associated with students’ preferences, with males being twice as likely as females to report a significant anti-fat bias (P < .001). See Table 2 for complete data.
Implicit (unconscious) bias
The majority of students (56%, 173/310)had a moderate or strong implicit weight-related bias. Overall, 39%(121/310) had an anti-fat bias, and17% (52/310) had an anti-thin bias. The presence of an implicit weight-related bias did not vary by gender, race, age, clerkship timing, or academicyear. See Table 3 for completedata.
Awareness of bias
Among the students with a significant weight-related bias, only 23% (40/173) were aware of that bias. Two-thirds of students (67%, 81/121) with a significant anti-fat bias thought they were neutral, and all students (100%, 52/52) with an anti-thin bias thought they were neutral or had an anti-fat bias. We found no significant correlation between students’ stated bias and their implicit bias when examining the entire sample (Pearson correlation coefficient 0.03, P = .58) or individual subgroups by gender, age, or race. Similarly, an explicit weight-related bias was not a significant predictor of an implicit bias in our logistic regression model (β = −0.14, P = .30).
In our multivariate logistic regression model, only male gender predicted an explicit anti-fat bias (odds ratio [OR] 3.0, 95% confidence interval [CI] 1.8–5.3). Students’ explicit anti-fat bias decreased with age, but this finding was not statistically significant (OR 0.9 for each one-year increase in age, P = .18). Similar to the results of our bivariate analyses, no demographic or clerkship timing factors were associated with an implicit anti-fat bias in our multivariate model.
We found that the majority of medical students had a significant weight-related bias, and most students were unaware of their bias. This lack of awareness is not unexpected given that a much larger study of the general population using the Weight IAT found only a weak correlation between implicit and explicit weight bias (r = 0.20).6
Because the literature confirms that obese individuals face discrimination in the general population,5 our main interest was to determine whether medical students possessed an anti-obesity bias and whether they were aware of that bias. We did find that approximately 40% of students had a moderate or strong implicit anti-fat bias, yet few were aware of it. Although male gender was associated with an explicit anti-fat bias, no demographic factors predicted an implicit bias. Because one prior study35 found that medical residents were more likely to express anti-fat attitudes as they progressed through training, we looked for a similar effect in medical students over the course of their third year. We found that the prevalence of an anti-fat bias remained consistent throughout the year.
Although anti-fat was the most common bias we observed, 17% of medical students had an anti-thin bias of which they were unaware, and some students who reported an explicit anti-fat preference actually had no significant implicit weight-related bias. This discordance may arise from students recognizing the current anti-fat societal norms and believing that they too hold those norms. Alternatively, medical students who are learning about the adverse health effects of obesity may assume that they should prefer thin people to fat people.
Although we are not aware of prior studies that measured medical students’ implicit weight-related biases, our results are consistent with previous studies that found a high prevalence of implicit anti-fat bias among university students.36,37 Similarly, a separate study found that over half of internal medicine residents in their final year of training reported having negative reactions toward the appearance of obese patients.35
Given that anti-fat biases are prevalent and act as a significant barrier to physicians’ treatment of obesity, teaching medical students to recognize and mitigate this bias is crucial to improving the care for the two-thirds of American adults who are now overweight or obese.21 Researchers suggest that minimizing the effects of an implicit bias likely requires a multilevel approach.38–41 First, students must acknowledge that the implicit bias exists. For this reason, we continue to require that all third-year medical students complete the online IAT during the family medicine clerkship, followed by an in-class discussion of implicit bias and students’ experiences with bias. Second, students must accept that their bias could affect their actions. To accomplish this goal, we created an online educational module about obesity bias and stigmatization, which we have made freely available via the Internet.42 Lastly, students must reduce their bias and/or learn new strategies for mitigating it.
This final step, reducing bias, has been challenging. Anti-obesity prejudice has been linked to the belief that body weight is under an individual’s control and, consequently, that the individual can be blamed for being obese.43,44 Although this would suggest that educating students about the genetic and environmental causes of obesity should decrease prejudice, this strategy has yielded mixed results.45–47 Efforts to increase or evoke empathy have been reported to reduce bias in some interventions48,49 but not in all.45,50,51 Other suggested educational strategies have included viewing encounters with stigmatized groups as opportunities to practice egalitarian goals, looking for counter-stereotypical attributes and commonalities, and altering views of social norms.39,40 Other research has stressed that learning these new skills requires repeated opportunities for practice.41
The mixed results from these efforts to reduce bias highlight the need for more educational research to determine which strategies are most effective. Toward this goal, we have created a 30-minute video documentary of obese individuals describing their daily challenges and experiences with the health care system, including both positive and negative interactions with physicians. At our institution, we show this video to small groups of third-year medical students and follow the viewing with a debriefing activity. We are currently evaluating whether this strategy effectively evokes empathy, creates awareness, and changes attitudes in medical students.
Our study has a number of limitations. First, we conducted it at a single medical school. Although the students at our school originate from geographically diverse places, the prevalence of anti-fat bias may vary in different regions, thus affecting the generalizability of our findings. Second, we did not attempt to measure students’ body weights, so we are unable to determine how students’ personal weight classification affects their biases. Although one may assume that obese students would be less likely to have an anti-fat bias, other studies have found that an anti-fat bias is prevalent in both normal-weight and obese populations.52,53 Even obese physicians demonstrate on average a moderate anti-fat bias.6
In conclusion, we found that over one-third of medical students have a significant anti-fat bias, and few were aware of that bias. To prevent anti-fat biases from compromising patient care, medical schools should develop curricula to address weight-related biases in a comprehensive manner. Such a curriculum should include educating students about the impact of implicit biases on patient care and giving students multiple opportunities to reflect on their biases and practice strategies for minimizing the impact of those biases on their patient interactions and treatment decisions. Further research is needed todetermine which educational strategiesaremost effective in reducing weight-related biases.
Acknowledgments: The authors are indebted to Karen Vaden and Donna Kronner for their coordination of study activities.
Funding/Support: This study was funded by a grant from the National Cancer Institute (1R25CA117887-01A1).
Other disclosures: None.
Ethical approval: The Wake Forest Baptist Health institutional review board approved the study protocol. All participants provided informed consent.
Previous presentations: Preliminary results from this study were presented at the Society of General Internal Medicine Annual Meeting, April 2010, Minneapolis, Minnesota.
1. Andreyeva T, Puhl RM, Brownell KD. Changes in perceived weight discrimination among Americans, 1995–1996 through 2004–2006. Obesity (Silver Spring). 2008;16:1129–1134
2. Brown H. For obese people, prejudice in plain sight. NY Times. March 16, 2010:D6
3. Puhl RM, Andreyeva T, Brownell KD. Perceptions of weight discrimination: Prevalence and comparison to race and gender discrimination in America. Int J Obes (Lond). 2008;32:992–1000
4. Wang L. Weight discrimination: One size fits all remedy? Yale Law J. 2008;117:1900–1945
5. Puhl RM, Heuer CA. The stigma of obesity: A review and update. Obesity (Silver Spring). 2009;17:941–964
6. Sabin JA, Marini M, Nosek BA. Implicit and explicit anti-fat bias among a large sample of medical doctors by BMI, race/ethnicity and gender. PLoS One. 2012;7:e48448
7. Foster GD, Wadden TA, Makris AP, et al. Primary care physicians’ attitudes about obesity and its treatment. Obes Res. 2003;11:1168–1177
8. Schwartz MB, Chambliss HO, Brownell KD, Blair SN, Billington C. Weight bias among health professionals specializing in obesity. Obes Res. 2003;11:1033–1039
9. Teachman BA, Brownell KD. Implicit anti-fat bias among health professionals: Is anyone immune? Int J Obes Relat Metab Disord. 2001;25:1525–1531
10. Wear D, Aultman JM, Varley JD, Zarconi J. Making fun of patients: Medical students’ perceptions and use of derogatory and cynical humor in clinical settings. Acad Med. 2006;81:454–462
11. Huizinga MM, Cooper LA, Bleich SN, Clark JM, Beach MC. Physician respect for patients with obesity. J Gen Intern Med. 2009;24:1236–1239
12. Huizinga MM, Bleich SN, Beach MC, Clark JM, Cooper LA. Disparity in physician perception of patients’ adherence to medications by obesity status. Obesity (Silver Spring). 2010;18:1932–1937
13. Wigton RS, McGaghie WC. The effect of obesity on medical students’ approach to patients with abdominal pain. J Gen Intern Med. 2001;16:262–265
14. Hebl MR, Xu J. Weighing the care: Physicians’ reactions to the size of a patient. Int J Obes Relat Metab Disord. 2001;25:1246–1252
15. Puhl RM, Brownell KD. Confronting and coping with weight stigma: An investigation of overweight and obese adults. Obesity (Silver Spring). 2006;14:1802–1815
16. Lewis S, Thomas SL, Hyde J, Castle DJ, Komesaroff PA. A qualitative investigation of obese men’s experiences with their weight. Am J Health Behav. 2011;35:458–469
17. Vartanian LR, Novak SA. Internalized societal attitudes moderate the impact of weight stigma on avoidance of exercise. Obesity (Silver Spring). 2011;19:757–762
18. Amy NK, Aalborg A, Lyons P, Keranen L. Barriers to routine gynecological cancer screening for white and African-American obese women. Int J Obes (Lond). 2006;30:147–155
19. Wee CC, McCarthy EP, Davis RB, Phillips RS. Screening for cervical and breast cancer: Is obesity an unrecognized barrier to preventive care? Ann Intern Med. 2000;132:697–704
20. Mitchell RS, Padwal RS, Chuck AW, Klarenbach SW. Cancer screening among the overweight and obese in Canada. Am J Prev Med. 2008;35:127–132
21. Flegal KM, Carroll MD, Ogden CL, Curtin LR. Prevalence and trends in obesity among US adults, 1999–2008. JAMA. 2010;303:235–241
22. Greenwald AG, Banaji MR. Implicit social cognition: Attitudes, self-esteem, and stereotypes. Psychol Rev. 1995;102:4–27
23. Wilson TD, Lindsey S, Schooler TY. A model of dual attitudes. Psychol Rev. 2000;107:101–126
24. Nosek BA, Smyth FL. A multitrait–multimethod validation of the implicit association test: Implicit and explicit attitudes are related but distinct constructs. Exp Psychol. 2007;54:14–29
25. Wyer RS The Automaticity of Everyday Life. 1997 Mahwah, NJ Lawrence Erlbaum Associates
26. Bargh JA, Morsella E. The unconscious mind. Perspect Psychol Sci. 2008;3:73–79
27. Shepherd H. The cultural context of cognition: What the implicit association test tells us about how culture works. Sociol Forum. 2011;26:121–143
28. Greenwald AG, Poehlman TA, Uhlmann EL, Banaji MR. Understanding and using the Implicit Association Test: III. Meta-analysis of predictive validity. J Pers Soc Psychol. 2009;97:17–41
29. Greenwald AG, McGhee DE, Schwartz JL. Measuring individual differences in implicit cognition: The implicit association test. J Pers Soc Psychol. 1998;74:1464–1480
30. Nosek BA, Greenwald AG, Banaji MRBargh JA. The Implicit Association Test at age 7: A methodological and conceptual review. In: Automatic Processes in Social Thinking and Behavior. 2006 New York, NY Psychology Press:262–292
31. Cunningham WA, Preacher KJ, Banaji MR. Implicit attitude measures: Consistency, stability, and convergent validity. Psychol Sci. 2001;12:163–170
32. Sriram N, Greenwald AG. The Brief Implicit Association Test. Exp Psychol. 2009;56:283–294
33. Haider AH, Sexton J, Sriram N, et al. Association of unconscious race and social class bias with vignette-based clinical assessments by medical students. JAMA. 2011;306:942–951
34. Sriram N. Research scientist, University of Virginia. Personal communication with S. Crandall. June 9, 2010
35. Davis NJ, Shishodia H, Taqui B, Dumfeh C, Wylie-Rosett J. Resident physician attitudes and competence about obesity treatment: Need for improved education. Med Educ Online. 2008;13:5
36. Chambliss HO, Finley CE, Blair SN. Attitudes toward obese individuals among exercise science students. Med Sci Sports Exerc. 2004;36:468–474
37. O’Brien KS, Hunter JA, Banks M. Implicit anti-fat bias in physical educators: Physical attributes, ideology and socialization. Int J Obes (Lond). 2007;31:308–314
38. Dovidio JF, Fiske ST. Under the radar: How unexamined biases in decision-making processes in clinical interactions can contribute to health care disparities. Am J Public Health. 2012;102:945–952
39. Daníelsdóttir S, O’Brien KS, Ciao A. Anti-fat prejudice reduction: A review of published studies. Obes Facts. 2010;3:47–58
40. Stone J, Moskowitz GB. Non-conscious bias in medical decision making: What can be done to reduce it? Med Educ. 2011;45:768–776
41. Teal CR, Gill AC, Green AR, Crandall S. Helping medical learners recognise and manage unconscious bias toward certain patient groups. Med Educ. 2012;46:80–88
42. Miller D, Crandall S, Davis S, et al. NEW Lifestyle: A downloadable teaching and learning program for Nutrition, Exercise, and Weight management. MedEdPORTAL. October 24, 2012 www.mededportal.org/publication/9256
. Accessed April 16, 2013
43. Vartanian LR. Disgust and perceived control in attitudes toward obese people. Int J Obes (Lond). 2010;34:1302–1307
44. Lund TB, Sandøe P, Lassen J. Attitudes to publicly funded obesity treatment and prevention. Obesity (Silver Spring). 2011;19:1580–1585
45. Teachman BA, Gapinski KD, Brownell KD, Rawlins M, Jeyaram S. Demonstrations of implicit anti-fat bias: The impact of providing causal information and evoking empathy. Health Psychol. 2003;22:68–78
46. O’Brien KS, Puhl RM, Latner JD, Mir AS, Hunter JA. Reducing anti-fat prejudice in preservice health students: A randomized trial. Obesity (Silver Spring). 2010;18:2138–2144
47. Persky S, Eccleston CP. Impact of genetic causal information on medical students’ clinical encounters with an obese virtual patient: Health promotion and social stigma. Ann Behav Med. 2011;41:363–372
48. Robinson BE, Bacon JG, O’Reilly J. Fat phobia: Measuring, understanding, and changing anti-fat attitudes. Int J Eat Disord. 1993;14:467–480
49. Wiese HJ, Wilson JF, Jones RA, Neises M. Obesity stigma reduction in medical students. Int J Obes Relat Metab Disord. 1992;16:859–868
50. Harris MB, Walters LC, Waschull S. Altering attitudes and knowledge about obesity. J Soc Psychol. 1991;131:881–884
51. Gapinski KD, Schwartz MB, Brownell KD. Can television change anti-fat attitudes and behavior? J Appl Biobehav Res. 2006;11:1–28
52. Wang SS, Brownell KD, Wadden TA. The influence of the stigma of obesity on overweight individuals. Int J Obes Relat Metab Disord. 2004;28:1333–1337
53. Schwartz MB, Vartanian LR, Nosek BA, Brownell KD. The influence of one’s own body weight on implicit and explicit anti-fat bias. Obesity (Silver Spring). 2006;14:440–447