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Perspect Med Educ ; 13(1): 255-265, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38706454

RESUMO

Introduction: Few researchers have examined how medical student mistreatment varies by race/ethnicity and gender, specifically highlighting Black women's experiences. Moreover, researchers often fail to use theoretical frameworks when examining the experiences of minoritized populations. The purpose of this study was to examine the frequency of mistreatment US Black women medical students experience and how this compared to other students underrepresented in medicine (URiM) using intersectionality as a theoretical framework. Methods: We used the Association of American Medical Colleges Graduate Questionnaire (GQ) as the data source for examining descriptive statistics and frequencies. We examined differences between US Black women (N = 2,537) and other URiM students (N = 7,863) with Mann-Whitney U tests. Results: The results from this study highlighted that most Black women medical students did not experience mistreatment, yet a higher proportion of these trainees reported experiencing gendered (χ2(1) = 28.59, p < .01) and racially/ethnically (χ2(1) = 2935.15, p < .01) offensive remarks at higher frequency than their URiM counterparts. We also found US Black women medical students infrequently (27.3%) reported mistreatment from a lack of confidence for advocacy on their behalf, fear of reprisal, and seeing the incident as insignificant. Discussion: A paucity of research exists on Black women medical students and even less using relevant theoretical frameworks such as intersectionality. Failure to extract Black women's experiences exacerbates alienation, invisibility, and inappropriate attention to their mistreatment.


Assuntos
Negro ou Afro-Americano , Estudantes de Medicina , Humanos , Estudantes de Medicina/psicologia , Estudantes de Medicina/estatística & dados numéricos , Feminino , Inquéritos e Questionários , Negro ou Afro-Americano/psicologia , Negro ou Afro-Americano/estatística & dados numéricos , Faculdades de Medicina/estatística & dados numéricos , Adulto , Estados Unidos
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