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MedEdPORTAL ; 20: 11412, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38957523

RESUMO

Introduction: Medical curricula implicitly teach that race has a biological basis. Clinical rotations reinforce this misconception as race-based algorithms are used to guide clinical decision-making. This module aims to expose the fallacy of race in clinical algorithms, using the estimated glomerular filtration rate (eGFR) equation as an example. Methods: We created a 60-minute module in consultation with nephrologists. The format was an interactive, case-based presentation with a didactic section. A third-year medical student facilitated the workshops to medical students. Evaluation included pre/post surveys using 5-point Likert scales to assess awareness regarding use of race as a biological construct. Higher scores indicated increased awareness. Results: Fifty-five students participated in the module. Pre/post results indicated that students significantly improved in self-perceived knowledge of the history of racism in medicine (2.6 vs. 3.2, p < .001), awareness of race in clinical algorithms (2.7 vs. 3.7, p < .001), impact of race-based eGFR on quality of life/treatment outcomes (4.5 vs. 4.8, p = .01), differences between race and ancestry (3.7 vs. 4.3, p < .001), and implications of not removing race from the eGFR equation (2.7 vs. 4.2, p < .001). Students rated the workshops highly for quality and clarity. Discussion: Our module expands on others' work to expose the fallacy of race-based algorithms and define its impact on health equity. Limitations include a lack of objective assessment of knowledge acquisition. We recommend integrating this module into preclinical and clinical curricula to discuss the use of race in medical literature and clinical practice.


Assuntos
Algoritmos , Currículo , Taxa de Filtração Glomerular , Estudantes de Medicina , Humanos , Estudantes de Medicina/estatística & dados numéricos , Estudantes de Medicina/psicologia , Taxa de Filtração Glomerular/fisiologia , Inquéritos e Questionários , Grupos Raciais/estatística & dados numéricos , Educação de Graduação em Medicina/métodos , Masculino , Racismo , Feminino
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