Association Between Resident Race and Ethnicity and Clinical Performance Assessment Scores in Graduate Medical Education
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".