Race and Gender Bias in Clerkship Grading.

Abstract

Phenomenon: Existing literature, as well as anecdotal evidence, suggests that tiered clinical grading systems may display systematic demographic biases. This study aimed to investigate these potential inequities in-depth. Specifically, this study attempted to address the following gaps in the literature: (1) studying grades actually assigned to students (as opposed to self-reported ones), (2) using longitudinal data over an 8-year period, providing stability of data, (3) analyzing three important, potentially confounding covariates, (4) using a comprehensive multivariate statistical design, and (5) investigating not just the main effects of gender and race, but also their potential interaction. Approach: Participants included 1,905 graduates (985 women, 51.7%) who received the Doctor of Medicine degree between 2014 and 2021. Most of the participants were white (n = 1,310, 68.8%) and about one-fifth were nonwhite (n = 397, 20.8%). There were no reported race data for 10.4% (n = 198). To explore potential differential grading, a two-way multivariate analysis of covariance was employed to examine the impact of race and gender on grades in eight required clerkships, adjusting for prior academic performance. Findings: There were two significant main effects, race and gender, but no interaction effect between gender and race. Women received higher grades on average on all eight clerkships, and white students received higher grades on average on four of the eight clerkships (Medicine, Pediatrics, Surgery, Obstetrics/Gynecology). These relationships held even when accounting for prior performance covariates. Insights: These findings provide additional evidence that tiered grading systems may be subject to systematic demographic biases. It is difficult to tease apart the contributions of various factors to the observed differences in gender and race on clerkship grades, and the interactions that produce these biases may be quite complex. The simplest solution to cut through the tangled web of grading biases may be to move away from a tiered grading system altogether.