Seeking Inclusion Excellence: Understanding Racial Microaggressions as Experienced by Underrepresented Medical and Nursing Students.
Ontology highlight
ABSTRACT: PURPOSE:To describe how racial microaggressions may affect optimal learning for under-represented health professions students. METHOD:The authors conducted focus groups and individual interviews from November 2017 to June 2018 with 37 students at University of California, Davis and Yale University who self-identified as underrepresented in medicine or nursing. Questions explored incidence, response to, and effects of racial microaggressions, as well as students' suggestions for change. Data were organized and coded, then thematic analysis was used to identify themes and subthemes. RESULTS:The data showed consistent examples of microaggressions across both health professions and schools, with peers, faculty, preceptors, and structural elements of the curricula all contributing to microaggressive behavior. The 3 major themes were: students felt devalued by microaggressions; students identified how microaggressions affected their learning, academic performance, and personal wellness; and students had suggestions for promoting inclusion. CONCLUSIONS:The data indicated that students perceived that their daily experiences were affected by racial microaggressions. Participants reported strong emotions while experiencing racial microaggressions including feeling stressed, frustrated, and angered by these interactions. Further, students believed microaggressions negatively affected their learning, academic performance, and overall well-being. This study shows the need for leadership and faculty of health professions schools to implement policies, practices, and instructional strategies that support and leverage diversity so that innovative problem-solving can emerge to better serve underserved communities and reduce health disparities.
SUBMITTER: Ackerman-Barger K
PROVIDER: S-EPMC7185051 | biostudies-literature | 2020 May
REPOSITORIES: biostudies-literature
ACCESS DATA