: Mental shortcuts are commonly used in medical education to facilitate the learning and application of a large volume of information. However, the use of demographic identifiers such as race, ethnicity, region, and descent from one of these groups as mental shortcuts in association with disease can perpetuate misconceptions about the construction of these identities and reinforce stereotypes. The United States Medical Licensing Exam (USMLE) Step 1 is a major driver of pre-clinical undergraduate medical education that requires memorization of a large volume of information and application of this information to clinical vignettes. This study assesses how demographic identifiers have been used in a nearly universally used study resource for this exam. : The authors analyzed a resource that contains "high yield facts" and was built and maintained based on experiences with the USMLE Step 1 for references to race, ethnicity, region, and descent from one of these groups and the distribution of skin tones used in photos. These findings were subsequently compared to the changes made in the 2021 edition of the resource. : The authors found 50 references in the 2020 edition to race, ethnicity, region, and descent from one of these groups, all in relation to disease. More than half of these references had an associated heritable component. Black or African American race was disproportionately represented, comprising more than half of all racial associations (13/24). Additionally, light skin tone was used in 170/204 photos (84.2%) in the 2020 edition. In the 2021 edition, only 12/209 photos (5.7%) were new or changed. : These findings highlight the trend to associate race with disease while also furthering the misconception that there are innate, heritable differences between socially constructed groups and establishing light skin tone as the norm. While some favorable changes were made to the 2021 text, further work within this resource and across medical education is required to avoid further misuse of race and challenge existing implicit biases.

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http://dx.doi.org/10.1080/10401334.2022.2082434DOI Listing

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