Evidence map›Paper›PMID 40395228›Full record

ArticleAEM education and training2025

Differences in language used to describe racial groups in emergency medicine standardized letter of evaluation.

Felisha Gonzalez, Laura Welsh, Johanna Caicedo, Avery Clark, Ijeoma M Okafor, Kerrie P Nelson, Sula Frausto, Emily C Cleveland Manchanda

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Article in AEM education and training, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
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

5 citing papers in PubMed.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Felisha GonzalezDepartment of Emergency Medicine Boston Medical Center, and Boston University Chobanian and Avedisian School of Medicine Boston Massachusetts USA.ORCID https://orcid.org/0000-0003-1137-1942
Laura WelshDepartment of Emergency Medicine Massachusetts General Hospital Boston Massachusetts USA.
Johanna CaicedoDepartment of Emergency Medicine Boston Medical Center, and Boston University Chobanian and Avedisian School of Medicine Boston Massachusetts USA.
Avery ClarkDepartment of Emergency Medicine Boston Medical Center, and Boston University Chobanian and Avedisian School of Medicine Boston Massachusetts USA.
Ijeoma M OkaforDepartment of Emergency Medicine Boston Medical Center Boston Massachusetts USA.
Kerrie P NelsonDepartment of Biostatistics Boston University School of Public Health Boston Massachusetts USA.
Sula FraustoDepartment of Emergency Medicine Boston Medical Center Boston Massachusetts USA.
Emily C Cleveland ManchandaDepartment of Emergency Medicine Boston Medical Center, and Boston University Chobanian and Avedisian School of Medicine Boston Massachusetts USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: According to program directors in emergency medicine (EM), the standardized letter of evaluation (SLOE) is the most important component of the EM residency application. Understanding possible biases in SLOE language is critical for an equitable review process. Past studies have shown differences in the way medical students are described in narrative evaluations by race and gender; however, research on SLOE narratives has been limited to gender. This study seeks to evaluate narrative linguistic differences in applicant SLOEs by race. Methods: This is a narrative analysis of all U.S. MD and DO SLOEs from applicants to the study institution in the 2022 application cycle. We used Linguistic Inquiry and Word Count (LIWC) to complete two analyses. Analysis 1 used frequency of words within 19 categories to evaluate differences between underrepresented minorities in medicine (URiM) and non-URiM applicants and within racial subgroups. Analysis 2 used LIWC to evaluate dichotomous use of 21 key words in these same groups. Linear mixed models were performed for each of the outcomes to evaluate for associations between URiM/non-URiM status or racial subgroup and each outcome. Results: Of the 809 unique applicants, 18.3% identified as URiM, 57.5% identified as White, 17.4% identified as Asian, 10% identified as Latinx, and 6.3% identified as Black. The analysis revealed applicants who are Black contained on average 0.537 (SE 0.154, Bonferroni-adjusted Conclusion: Applicants who were URiM or Black were more likely to be described with communal words than their peers. URiM applicants had twice the amount of empathic words in their SLOEs when compared to their non-URiM peers; however, this finding did not meet statistical significance (

Identifiers

PMID40395228
PMCPMC12086979

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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.