Marriage and Family Therapy Trainees' Reports of Explicit Weight Bias

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Abstract

Discrimination based on an individual's weight has been observed in health care, education, retail, and other public sectors (Puhl & Huer, Obesity, 17, 941, 2007). Such inequity, known as "weight bias," generates negative short-term and long-term consequences for the individuals that experience it (Puhl & Brownell, Weight bias in health care settings, 2007). Past research has shown that healthcare trainees exhibit weight bias (Phelan et al., Obesity, 22, 1201, 2014; Wigton & McGaghie, Journal of General Internal Medicine, 16, 262, 2001), yet little focus is given to weight bias in marriage and family therapy (MFT) education. The purpose of this study was to survey MFT students (N = 162) to explore weight bias and how contextual factors associate with weight bias. Participants in MFT programs reported explicit weight bias, with specific contextual factors associating with more bias. Female participants reported more fear of gaining weight, and individuals who identified as overweight had higher rates of explicit weight bias. Contextual differences and implications for training programs are discussed.

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Weight bias among health professionals specializing in obesity.

Jana Schwartz, Kelly D. Brownell, Steven N. Blair … (2003)

To determine the level of anti-fat bias in health professionals specializing in obesity and identify personal characteristics that correlate with both implicit and explicit bias. The Implicit Associations Test (IAT) and a self-report questionnaire assessing explicit attitudes, personal experiences with obesity, and demographic characteristics was administered to clinicians and researchers attending the opening session of an international obesity conference (N = 389). The IAT was used to assess overall implicit weight bias (associating "obese people" and "thin people" with "good" vs. "bad") and three ranges of stereotypes: lazy-motivated, smart-stupid, and valuable-worthless. The questionnaire assessed explicit bias on the same dimensions, along with personal and professional experiences with obesity. Health professionals exhibited a significant pro-thin, anti-fat implicit bias on the IAT. In addition, the subjects significantly endorsed the implicit stereotypes of lazy, stupid, and worthless using the IAT. Level of bias was associated with several personal characteristics. Characteristics significantly predictive of lower levels of implicit anti-fat bias include being male, older, having a positive emotional outlook on life, weighing more, having friends who are obese, and indicating an understanding of the experience of obesity. Even professionals whose careers emphasize research or the clinical management of obesity show very strong weight bias, indicating pervasive and powerful stigma. Understanding the extent of anti-fat bias and the personal characteristics associated with it will aid in developing intervention strategies to ameliorate these damaging attitudes.

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The influence of one's own body weight on implicit and explicit anti-fat bias.

Kelly D. Brownell, Lenny R Vartanian, Austin Schwartz … (2006)

This study examined the influence of one's own body weight on the strength of implicit and explicit anti-fat bias. Implicit and explicit anti-fat attitudes and obesity stereotypes were assessed among a large online sample (N = 4283) that included representation from across the weight spectrum (from underweight to extremely obese). Respondents also indicated their willingness to make a range of personal sacrifices in exchange for not being obese. All weight groups exhibited significant anti-fat bias, but there was an inverse relation between one's own weight and the level of observed bias. Thinner people were more likely to automatically associate negative attributes (bad, lazy) with fat people, to prefer thin people to fat people, and to explicitly rate fat people as lazier and less motivated than thin people. However, when the lazy stereotype was contrasted with another negative attribute (anxious), obese and non-obese people exhibited equally strong implicit stereotyping. Finally, a substantial proportion of respondents indicated a willingness to endure aversive life events to avoid being obese. For example, 46% of the total sample indicated that they would rather give up 1 year of life than be obese, and 30% reported that they would rather be divorced than be obese. In each case, thinner people were more willing to sacrifice aspects of their health or life circumstances than were heavier people. Although the strength of weight bias decreased as respondents' body weight increased, a significant degree of anti-fat bias was still evident among even the most obese group of respondents, highlighting the pervasiveness of this bias.

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Implicit and Explicit Weight Bias in a National Sample of 4732 Medical Students: The Medical Student CHANGES Study

Sean Phelan, John Dovidio, Rebecca Puhl … (2014)

Objective To examine the magnitude of explicit and implicit weight biases compared to biases against other groups; and identify student factors predicting bias in a large national sample of medical students. Design and Methods A web-based survey was completed by 4732 1st year medical students from 49 medical schools as part of a longitudinal study of medical education. The survey included a validated measure of implicit weight bias, the implicit association test, and 2 measures of explicit bias: a feeling thermometer and the anti-fat attitudes test. Results A majority of students exhibited implicit (74%) and explicit (67%) weight bias. Implicit weight bias scores were comparable to reported bias against racial minorities. Explicit attitudes were more negative toward obese people than toward racial minorities, gays, lesbians, and poor people. In multivariate regression models, implicit and explicit weight bias was predicted by lower BMI, male sex, and non-Black race. Either implicit or explicit bias was also predicted by age, SES, country of birth, and specialty choice. Conclusions Implicit and explicit weight bias is common among 1st year medical students, and varies across student factors. Future research should assess implications of biases and test interventions to reduce their impact.