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Ann Diagn Pathol ; 40: 23-25, 2019 Jun.
Article in English | MEDLINE | ID: mdl-30852247

ABSTRACT

CONTEXT: A diverse workforce has been shown to improve productivity and innovation. The level of diversity among pathology residency applicants has not previously been reported. OBJECTIVE: This study aims to characterize the applicants at an academic pathology department to gain a better understanding of diversity among them. DESIGN: Between 2015 and 2017, data from a tertiary care center were analyzed for gender, US and international medical school, USMLE scores, and self-identified racial or ethnic group. For 2017, data was compared to that published by the Association of American Medical Colleges (AAMC). RESULTS: There were 1293 pathology applicants with 48-49% being female. The overall underrepresented minority (URM) applicant pool in pathology represented 12.6%, 9.5%, and 11.1% of our applicants for 2015, 2016, and 2017, respectively. URMs from US medical schools alone represented 4.7%, 3.7%, and 4.5% of our applicants for 2015, 2016, and 2017, respectively. The percentage of 2017 US medical school graduates applying to any US pathology training program was 22.2% versus 38.7% applying to pathology at our center (p ≤0.001). URM applicants for pathology from US medical schools were significantly lower than URM applicants to all AAMC medical specialties (p = 0.035). Among our pathology applicants in 2017, USMLE I scores and number of applicants with any publications were higher for non-URMs compared to URMs (p = 0.048 and p = 0.046, respectively). CONCLUSION: No significant difference related to gender was identified among our applicants while racial/ethnic groups remain underrepresented with significantly fewer URM applicants from US medical schools compared to published AAMC data for all medical specialties.


Subject(s)
Cultural Diversity , Ethnicity/statistics & numerical data , Internship and Residency/organization & administration , Minority Groups/statistics & numerical data , Pathologists/statistics & numerical data , Female , Humans , Male , United States
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