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Implicit and explicit weight bias in a national sample of 4,732 medical students: The medical student CHANGES study
Author(s) -
Phelan Sean M.,
Dovidio John F.,
Puhl Rebecca M.,
Burgess Diana J.,
Nelson David B.,
Yeazel Mark W.,
Hardeman Rachel,
Perry Sylvia,
Ryn Michelle
Publication year - 2014
Publication title -
obesity
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.438
H-Index - 199
eISSN - 1930-739X
pISSN - 1930-7381
DOI - 10.1002/oby.20687
Subject(s) - implicit association test , implicit bias , test (biology) , implicit attitude , feeling , psychology , racial bias , association test , psychological intervention , specialty , association (psychology) , social psychology , race (biology) , paleontology , biochemistry , chemistry , botany , psychiatry , gene , genotype , single nucleotide polymorphism , biology , psychotherapist
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. Methods A web‐based survey was completed by 4,732 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.