Differences in Narrative Language in Evaluations of Medical Students by Gender and Under-represented Minority Status.


Journal

Journal of general internal medicine
ISSN: 1525-1497
Titre abrégé: J Gen Intern Med
Pays: United States
ID NLM: 8605834

Informations de publication

Date de publication:
05 2019
Historique:
pubmed: 18 4 2019
medline: 2 10 2020
entrez: 18 4 2019
Statut: ppublish

Résumé

In varied educational settings, narrative evaluations have revealed systematic and deleterious differences in language describing women and those underrepresented in their fields. In medicine, limited qualitative studies show differences in narrative language by gender and under-represented minority (URM) status. To identify and enumerate text descriptors in a database of medical student evaluations using natural language processing, and identify differences by gender and URM status in descriptions. An observational study of core clerkship evaluations of third-year medical students, including data on student gender, URM status, clerkship grade, and specialty. A total of 87,922 clerkship evaluations from core clinical rotations at two medical schools in different geographic areas. We employed natural language processing to identify differences in the text of evaluations for women compared to men and for URM compared to non-URM students. We found that of the ten most common words, such as "energetic" and "dependable," none differed by gender or URM status. Of the 37 words that differed by gender, 62% represented personal attributes, such as "lovely" appearing more frequently in evaluations of women (p < 0.001), while 19% represented competency-related behaviors, such as "scientific" appearing more frequently in evaluations of men (p < 0.001). Of the 53 words that differed by URM status, 30% represented personal attributes, such as "pleasant" appearing more frequently in evaluations of URM students (p < 0.001), and 28% represented competency-related behaviors, such as "knowledgeable" appearing more frequently in evaluations of non-URM students (p < 0.001). Many words and phrases reflected students' personal attributes rather than competency-related behaviors, suggesting a gap in implementing competency-based evaluation of students. We observed a significant difference in narrative evaluations associated with gender and URM status, even among students receiving the same grade. This finding raises concern for implicit bias in narrative evaluation, consistent with prior studies, and suggests opportunities for improvement.

Sections du résumé

BACKGROUND
In varied educational settings, narrative evaluations have revealed systematic and deleterious differences in language describing women and those underrepresented in their fields. In medicine, limited qualitative studies show differences in narrative language by gender and under-represented minority (URM) status.
OBJECTIVE
To identify and enumerate text descriptors in a database of medical student evaluations using natural language processing, and identify differences by gender and URM status in descriptions.
DESIGN
An observational study of core clerkship evaluations of third-year medical students, including data on student gender, URM status, clerkship grade, and specialty.
PARTICIPANTS
A total of 87,922 clerkship evaluations from core clinical rotations at two medical schools in different geographic areas.
MAIN MEASURES
We employed natural language processing to identify differences in the text of evaluations for women compared to men and for URM compared to non-URM students.
KEY RESULTS
We found that of the ten most common words, such as "energetic" and "dependable," none differed by gender or URM status. Of the 37 words that differed by gender, 62% represented personal attributes, such as "lovely" appearing more frequently in evaluations of women (p < 0.001), while 19% represented competency-related behaviors, such as "scientific" appearing more frequently in evaluations of men (p < 0.001). Of the 53 words that differed by URM status, 30% represented personal attributes, such as "pleasant" appearing more frequently in evaluations of URM students (p < 0.001), and 28% represented competency-related behaviors, such as "knowledgeable" appearing more frequently in evaluations of non-URM students (p < 0.001).
CONCLUSIONS
Many words and phrases reflected students' personal attributes rather than competency-related behaviors, suggesting a gap in implementing competency-based evaluation of students. We observed a significant difference in narrative evaluations associated with gender and URM status, even among students receiving the same grade. This finding raises concern for implicit bias in narrative evaluation, consistent with prior studies, and suggests opportunities for improvement.

Identifiants

pubmed: 30993609
doi: 10.1007/s11606-019-04889-9
pii: 10.1007/s11606-019-04889-9
pmc: PMC6502922
doi:

Types de publication

Journal Article Multicenter Study Observational Study Research Support, N.I.H., Extramural

Langues

eng

Sous-ensembles de citation

IM

Pagination

684-691

Subventions

Organisme : NCI NIH HHS
ID : K24 CA212294
Pays : United States

Commentaires et corrections

Type : CommentIn

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Auteurs

Alexandra E Rojek (AE)

University of California, San Francisco School of Medicine, San Francisco, CA, USA.

Raman Khanna (R)

Division of Hospital Medicine, University of California, San Francisco, School of Medicine, San Francisco, CA, USA.

Joanne W L Yim (JWL)

Health Informatics, UCSF Health, University of California, San Francisco, San Francisco, CA, USA.

Rebekah Gardner (R)

Warren Alpert Medical School of Brown University, Providence, RI, USA.

Sarah Lisker (S)

University of California, San Francisco School of Medicine, San Francisco, CA, USA.
UCSF Center for Vulnerable Populations, San Francisco, CA, USA.

Karen E Hauer (KE)

University of California, San Francisco School of Medicine, San Francisco, CA, USA.

Catherine Lucey (C)

University of California, San Francisco School of Medicine, San Francisco, CA, USA.

Urmimala Sarkar (U)

University of California, San Francisco School of Medicine, San Francisco, CA, USA. urmimala.sarkar@ucsf.edu.
UCSF Center for Vulnerable Populations, San Francisco, CA, USA. urmimala.sarkar@ucsf.edu.

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Classifications MeSH