Heitkamp, Darel E.

Abstract Objectives Implicit bias is common and is thought to drive discriminatory behaviour. Having previously demonstrated discrimination against specific applicant demographics by academic radiology departments in a simulated resident selection process, the authors sought to better understand the relationship between implicit bias and discrimination, as well as the potential and mechanisms for their mitigation. Methods A total of 51 faculty reviewers at three academic radiology departments, who had participated in a 2017 audit study in which they were shown to treat applicants differently based on race or ethnicity and physical appearance, were invited to complete testing for implicit racial and weight bias using the Implicit Association Test in 2019. Respondents were also surveyed regarding awareness of their own personal racial and weight biases, as well as any prior participation in formal diversity training. Comparisons were made between implicit bias scores and applicant ratings, as well as between diversity training and self‐awareness of bias. Results A total of 31 out of 51 faculty reviewers (61%) completed and submitted results of race and weight Implicit Association Tests. A total of 74% (23/31) reported implicit anti‐obese bias, concordant with discrimination demonstrated in the resident selection simulation, in which obese applicants were rated 0.40 standard deviations (SDs) lower than non‐obese applicants ( P < .001). A total of 71% (22/31) reported implicit anti‐Black bias, discordant with application ratings, which were 0.47 SDs higher for Black than for White applicants ( P < .001). A total of 84% (26/31) of participants reported feeling self‐aware of potential racial bias at the time of application review, significantly higher than the 23% (7/31) reporting self‐awareness of potential anti‐obese bias ( P < .001). Participation in formal diversity training was not associated with implicit anti‐Black or anti‐fat bias, nor with self‐reported awareness of potential racial or weight‐based bias (all P > .2). Conclusions These findings suggest that implicit bias, as measured by the Implicit Association Test, does not inevitably lead to discrimination, and that personal awareness of implicit biases may allow their mitigation.

Purpose To evaluate for appearance-based discrimination in the selection of radiology residents. Method A deception study simulating the resident selection process examined the impact of attractiveness and obesity on resident selection. Seventy-four core faculty from 5 academic radiology departments reviewed mock residency applications in September and October 2017. Each application included demographic information and a photograph, representing a prespecified distribution of facial attractiveness and obesity, combined with randomized academic and supporting variables. Reviewers independently scored applications for interview desirability. Reviewer scores and application variables were compared using linear mixed fixed- and random-effects models. Results Reviewers evaluated 5,447 applications (mean: 74 applications per reviewer). United States Medical Licensing Examination Step 1 scores were the strongest predictor of reviewer rating (B = 0.35 [standard error (SE) = 0.029]). Applicant facial attractiveness strongly predicted rating (attractive vs unattractive, B = 0.30 [SE = 0.056]; neutral vs unattractive, B = 0.13 [SE = 0.028]). Less influential but still significant predictors included race/ethnicity (B = 0.25 [SE = 0.059]), preclinical class rank (B = 0.25 [SE = 0.040]), clinical clerkship grades (B = 0.23 [SE = 0.034]), Alpha Omega Alpha membership (B = 0.21 [SE = 0.032]), and obesity (vs not obese) (B = −0.14 [SE = 0.024]). Conclusions Findings provide preliminary evidence of discrimination against facially unattractive and obese applicants in radiology resident selection. Obesity and attractiveness were as influential in applicant selection for interview as traditional medical school performance metrics. Selection committees should invoke strategies to detect and manage appearance-based bias.