Evidence map›Paper›PMID 30993609›Full record

Observational studyJournal of general internal medicine2019

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

Alexandra E Rojek, Raman Khanna, Joanne W L Yim, Rebekah Gardner, Sarah Lisker, Karen E Hauer, Catherine Lucey, Urmimala Sarkar

Open access · bronzeAbstract readMulticenter StudyObservational Study
In one paragraph

Observational study in Journal of general internal medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 85 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
85citing papers in PubMed, 1 pooled it
33.1field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

85 citing papers in PubMed, 1 synthesis or guideline pooled it, 223 citations in OpenAlex.

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  10. Be AWARE: An Anti-bias Toolkit to Limit Bias in Narrative Evaluation of Medical Trainees.MedEdPORTAL : the journal of teaching and learning resources · 2026
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25 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors at 3 institutions in 1 country.

Alexandra E RojekUniversity of California, San Francisco School of Medicine, San Francisco, CA, USA.
Raman KhannaDivision of Hospital Medicine, University of California, San Francisco, School of Medicine, San Francisco, CA, USA.
Joanne W L YimHealth Informatics, UCSF Health, University of California, San Francisco, San Francisco, CA, USA.
Rebekah GardnerWarren Alpert Medical School of Brown University, Providence, RI, USA.
Sarah LiskerUniversity of California, San Francisco School of Medicine, San Francisco, CA, USA.
Karen E HauerUniversity of California, San Francisco School of Medicine, San Francisco, CA, USA.
Catherine LuceyUniversity of California, San Francisco School of Medicine, San Francisco, CA, USA.
Urmimala SarkarUniversity of California, San Francisco School of Medicine, San Francisco, CA, USA. urmimala.sarkar@ucsf.edu.
University of California, San Francisco · USBrown University · USSan Francisco General Hospital · US

Funding

Improving Survivorship Care for Diverse Cancer Patients Cared for in Safety-net SettingsK24CA212294 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI SARKAR, URMIMALA · 2017 to 2021
$831k
NCI NIH HHS K24 CA212294
6 · The paper itself

Abstract

backgroundIn 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.

objectiveTo 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.

designAn observational study of core clerkship evaluations of third-year medical students, including data on student gender, URM status, clerkship grade, and specialty.

participantsA 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

resultsWe 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).

conclusionsMany 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.

Indexed as

Educational MeasurementEducation, MedicalFemaleHumansMaleMinority GroupsPrejudiceProgram EvaluationSexismStudents, MedicalTerminology as Topicmedical educationmedical education—assessment/evaluationmedical student and residency education

Identifiers

PMID30993609
PMCPMC6502922
OpenAlexW2938521672

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.