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Race, Ethnicity, and Immigration Status in a Medical Licensing Educational Resource: a Systematic, Mixed-Methods Analysis

BACKGROUND: Medical students preparing for the United States Medical Licensing Exam (USMLE) Step 2 Clinical Knowledge (CK) Exam frequently use the UWorld Step 2 CK Question Bank (QBank). Over 90% of medical students use UWorld QBanks to prepare for at least one USMLE. Although several questions in t...

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Autores principales: Cerdeña, Jessica P., Jaswaney, Rohit, Plaisime, Marie V.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8971213/
https://www.ncbi.nlm.nih.gov/pubmed/33987787
http://dx.doi.org/10.1007/s11606-021-06843-0
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author Cerdeña, Jessica P.
Jaswaney, Rohit
Plaisime, Marie V.
author_facet Cerdeña, Jessica P.
Jaswaney, Rohit
Plaisime, Marie V.
author_sort Cerdeña, Jessica P.
collection PubMed
description BACKGROUND: Medical students preparing for the United States Medical Licensing Exam (USMLE) Step 2 Clinical Knowledge (CK) Exam frequently use the UWorld Step 2 CK Question Bank (QBank). Over 90% of medical students use UWorld QBanks to prepare for at least one USMLE. Although several questions in the QBank mention race, ethnicity, or immigration status, their contributions to the QBank remain underexamined. OBJECTIVE: We conducted a systematic, mixed-methods content analysis to assess whether and how disease conditions might be racialized throughout this popular medical education resource. DESIGN: We screened 3537 questions in the QBank between May 28 and August 11, 2020, for mentions of race, ethnicity, or immigration status. We performed multinomial logistic regression to assess the likelihood of each racial/ethnic category occurring in either the question stem, answer explanation, or both. We used an inductive technique for codebook development and determined code frequencies. MAIN MEASURES: We reviewed the frequency and distribution of race or ethnicity in question stems, answer choices, and answer explanations; assessed associations between disease conditions and racial and ethnic categories; and identified whether and how these associations correspond to race-, ethnicity-, or migration-based care. RESULTS: References to Black race occurred most frequently, followed by Asian, White, and Latinx groups. Mentions of race/ethnicity varied significantly by location in the question: Asian race had 6.40 times greater odds of occurring in the answer explanation only (95% CI 1.19–34.49; p < 0.031) and White race had 9.88 times greater odds of occurring only in the question stem (95% CI 2.56–38.08; p < 0.001). Qualitative analyses suggest frequent associations between disease conditions and racial, ethnic, and immigration categories, which often carry implicit or explicit biological and genetic explanations. CONCLUSIONS: Our analysis reveals patterns of race-based disease associations that have potential for systematic harm, including promoting incorrect race-based associations and upholding cultural conventions of White bodies as normative. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11606-021-06843-0.
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spelling pubmed-89712132022-04-20 Race, Ethnicity, and Immigration Status in a Medical Licensing Educational Resource: a Systematic, Mixed-Methods Analysis Cerdeña, Jessica P. Jaswaney, Rohit Plaisime, Marie V. J Gen Intern Med Original Research BACKGROUND: Medical students preparing for the United States Medical Licensing Exam (USMLE) Step 2 Clinical Knowledge (CK) Exam frequently use the UWorld Step 2 CK Question Bank (QBank). Over 90% of medical students use UWorld QBanks to prepare for at least one USMLE. Although several questions in the QBank mention race, ethnicity, or immigration status, their contributions to the QBank remain underexamined. OBJECTIVE: We conducted a systematic, mixed-methods content analysis to assess whether and how disease conditions might be racialized throughout this popular medical education resource. DESIGN: We screened 3537 questions in the QBank between May 28 and August 11, 2020, for mentions of race, ethnicity, or immigration status. We performed multinomial logistic regression to assess the likelihood of each racial/ethnic category occurring in either the question stem, answer explanation, or both. We used an inductive technique for codebook development and determined code frequencies. MAIN MEASURES: We reviewed the frequency and distribution of race or ethnicity in question stems, answer choices, and answer explanations; assessed associations between disease conditions and racial and ethnic categories; and identified whether and how these associations correspond to race-, ethnicity-, or migration-based care. RESULTS: References to Black race occurred most frequently, followed by Asian, White, and Latinx groups. Mentions of race/ethnicity varied significantly by location in the question: Asian race had 6.40 times greater odds of occurring in the answer explanation only (95% CI 1.19–34.49; p < 0.031) and White race had 9.88 times greater odds of occurring only in the question stem (95% CI 2.56–38.08; p < 0.001). Qualitative analyses suggest frequent associations between disease conditions and racial, ethnic, and immigration categories, which often carry implicit or explicit biological and genetic explanations. CONCLUSIONS: Our analysis reveals patterns of race-based disease associations that have potential for systematic harm, including promoting incorrect race-based associations and upholding cultural conventions of White bodies as normative. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11606-021-06843-0. Springer International Publishing 2021-05-13 2022-04 /pmc/articles/PMC8971213/ /pubmed/33987787 http://dx.doi.org/10.1007/s11606-021-06843-0 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Original Research
Cerdeña, Jessica P.
Jaswaney, Rohit
Plaisime, Marie V.
Race, Ethnicity, and Immigration Status in a Medical Licensing Educational Resource: a Systematic, Mixed-Methods Analysis
title Race, Ethnicity, and Immigration Status in a Medical Licensing Educational Resource: a Systematic, Mixed-Methods Analysis
title_full Race, Ethnicity, and Immigration Status in a Medical Licensing Educational Resource: a Systematic, Mixed-Methods Analysis
title_fullStr Race, Ethnicity, and Immigration Status in a Medical Licensing Educational Resource: a Systematic, Mixed-Methods Analysis
title_full_unstemmed Race, Ethnicity, and Immigration Status in a Medical Licensing Educational Resource: a Systematic, Mixed-Methods Analysis
title_short Race, Ethnicity, and Immigration Status in a Medical Licensing Educational Resource: a Systematic, Mixed-Methods Analysis
title_sort race, ethnicity, and immigration status in a medical licensing educational resource: a systematic, mixed-methods analysis
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8971213/
https://www.ncbi.nlm.nih.gov/pubmed/33987787
http://dx.doi.org/10.1007/s11606-021-06843-0
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