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Differences in words used to describe racial and gender groups in Medical Student Performance Evaluations
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Differences in words used to describe racial and gender groups in Medical Student Performance Evaluations
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Journal Article
Differences in words used to describe racial and gender groups in Medical Student Performance Evaluations
2017
Overview
The transition from medical school to residency is a critical step in the careers of physicians. Because of the standardized application process-wherein schools submit summative Medical Student Performance Evaluations (MSPE's)-it also represents a unique opportunity to assess the possible prevalence of racial and gender disparities, as shown elsewhere in medicine.
The authors conducted textual analysis of MSPE's from 6,000 US students applying to 16 residency programs at a single institution in 2014-15. They used custom software to extract demographic data and keyword frequency from each MSPE. The main outcome measure was the proportion of applicants described using 24 pre-determined words from four thematic categories (\"standout traits\", \"ability\", \"grindstone habits\", and \"compassion\").
The data showed significant differences based on race and gender. White applicants were more likely to be described using \"standout\" or \"ability\" keywords (including \"exceptional\", \"best\", and \"outstanding\") while Black applicants were more likely to be described as \"competent\". These differences remained significant after controlling for United States Medical Licensing Examination Step 1 scores. Female applicants were more frequently described as \"caring\", \"compassionate\", and \"empathic\" or \"empathetic\". Women were also more frequently described as \"bright\" and \"organized\".
While the MSPE is intended to reflect an objective, summative assessment of students' qualifications, these data demonstrate for the first time systematic differences in how candidates are described based on racial/ethnic and gender group membership. Recognizing possible implicit biases and their potential impact is important for faculty who strive to create a more egalitarian medical community.
Publisher
Public Library of Science,Public Library of Science (PLoS)
Subject
/ Adult
/ African Americans - statistics & numerical data
/ African Continental Ancestry Group - statistics & numerical data
/ Analysis
/ Computer and Information Sciences
/ Data
/ Education, Medical, Undergraduate - statistics & numerical data
/ Educational Measurement - statistics & numerical data
/ Empathy
/ European Continental Ancestry Group - statistics & numerical data
/ Female
/ Gender
/ Habits
/ Humans
/ Internship and Residency - statistics & numerical data
/ Keywords
/ Language
/ Male
/ Medicine
/ Medicine and Health Sciences
/ Race
/ Racism
/ School Admission Criteria - statistics & numerical data
/ Schools
/ Schools, Medical - statistics & numerical data
/ Sexism
/ Software
/ Students
/ Students, Medical - statistics & numerical data
/ Studies
/ Sympathy
/ Women
