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. 2012;7(11):e48448.
doi: 10.1371/journal.pone.0048448. Epub 2012 Nov 7.

Implicit and explicit anti-fat bias among a large sample of medical doctors by BMI, race/ethnicity and gender

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Implicit and explicit anti-fat bias among a large sample of medical doctors by BMI, race/ethnicity and gender

Janice A Sabin et al. PLoS One. 2012.

Abstract

Overweight patients report weight discrimination in health care settings and subsequent avoidance of routine preventive health care. The purpose of this study was to examine implicit and explicit attitudes about weight among a large group of medical doctors (MDs) to determine the pervasiveness of negative attitudes about weight among MDs. Test-takers voluntarily accessed a public Web site, known as Project Implicit®, and opted to complete the Weight Implicit Association Test (IAT) (N = 359,261). A sub-sample identified their highest level of education as MD (N = 2,284). Among the MDs, 55% were female, 78% reported their race as white, and 62% had a normal range BMI. This large sample of test-takers showed strong implicit anti-fat bias (Cohen's d = 1.0). MDs, on average, also showed strong implicit anti-fat bias (Cohen's d = 0.93). All test-takers and the MD sub-sample reported a strong preference for thin people rather than fat people or a strong explicit anti-fat bias. We conclude that strong implicit and explicit anti-fat bias is as pervasive among MDs as it is among the general public. An important area for future research is to investigate the association between providers' implicit and explicit attitudes about weight, patient reports of weight discrimination in health care, and quality of care delivered to overweight patients.

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Conflict of interest statement

Competing Interests: This project was supported by Project Implicit, Inc. BN is an officer of Project Implicit, a non-profit organization that includes in its mission, “To develop and deliver methods for investigating and applying phenomena of implicit social cognition, including especially phenomena of implicit bias based on age, race, gender or other factors.”

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