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Record W4280534059 · doi:10.1097/acm.0000000000004743

Association Between Resident Race and Ethnicity and Clinical Performance Assessment Scores in Graduate Medical Education

2022· article· en· W4280534059 on OpenAlexaff
Robin Klein, Nneka N. Ufere, Sarah Schaeffer, Katherine A. Julian, Sowmya R. Rao, Jennifer Koch, Anna Volerman, Erin D. Snyder, Vanessa Thompson, Ishani Ganguli, Sherri‐Ann M. Burnett‐Bowie, Kerri Palamara

Bibliographic record

VenueAcademic Medicine · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicDiversity and Career in Medicine
Canadian institutionsThompson Rivers University
FundersNational Institute on Aging
KeywordsEthnic groupMedicineGraduate medical educationSpecialtyFamily medicineAccreditationPercentile rankPercentileEducational measurementPsychologyMedical educationCurriculum

Abstract

fetched live from OpenAlex

PURPOSE: To assess the association between internal medicine (IM) residents' race/ethnicity and clinical performance assessments. METHOD: The authors conducted a cross-sectional analysis of clinical performance assessment scores at 6 U.S. IM residency programs from 2016 to 2017. Residents underrepresented in medicine (URiM) were identified using self-reported race/ethnicity. Standardized scores were calculated for Accreditation Council for Graduate Medical Education core competencies. Cross-classified mixed-effects regression assessed the association between race/ethnicity and competency scores, adjusting for rotation time of year and setting; resident gender, postgraduate year, and IM In-Training Examination percentile rank; and faculty gender, rank, and specialty. RESULTS: Data included 3,600 evaluations by 605 faculty of 703 residents, including 94 (13.4%) URiM residents. Resident race/ethnicity was associated with competency scores, with lower scores for URiM residents (difference in adjusted standardized scores between URiM and non-URiM residents, mean [standard error]) in medical knowledge (-0.123 [0.05], P = .021), systems-based practice (-0.179 [0.05], P = .005), practice-based learning and improvement (-0.112 [0.05], P = .032), professionalism (-0.116 [0.06], P = .036), and interpersonal and communication skills (-0.113 [0.06], P = .044). Translating this to a 1 to 5 scale in 0.5 increments, URiM resident ratings were 0.07 to 0.12 points lower than non-URiM resident ratings in these 5 competencies. The interaction with faculty gender was notable in professionalism (difference between URiM and non-URiM for men faculty -0.199 [0.06] vs women faculty -0.014 [0.07], P = .01) with men more than women faculty rating URiM residents lower than non-URiM residents. Using the 1 to 5 scale, men faculty rated URiM residents 0.13 points lower than non-URiM residents in professionalism. CONCLUSIONS: Resident race/ethnicity was associated with assessment scores to the disadvantage of URiM residents. This may reflect bias in faculty assessment, effects of a noninclusive learning environment, or structural inequities in assessment.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.018
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.079
Threshold uncertainty score0.695

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0180.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.101
GPT teacher head0.448
Teacher spread0.347 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations57
Published2022
Admission routes1
Has abstractyes

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