Underrepresented in medicine (URiM) residents: A scoping review on prevalence trends & improving recruitment
Bibliographic record
Abstract
BACKGROUND: Disparities exist in underrepresented in medicine (URiM) resident representation. This review examines recent trends in resident diversity, URiM recruitment strategies, and identifies research gaps in equity, diversity, and inclusion (EDI) for URiM residents. METHODS: MEDLINE, EMBASE, Web of Science, and ERIC databases were searched for studies published from 2017 to 2022 on URiM resident prevalence and recruitment initiatives. RESULTS: 3634 abstracts were reviewed, and 52 articles were included. 35 (67 %) studies reported on prevalence of URiM residents, demonstrating URiM resident composition is lower than residency applicant demographics, particularly in surgery. Seventeen (33 %) studies reported on URiM recruitment interventions, such as visiting clerkship programs, holistic review, and targeted outreach, and demonstrated success in increasing recruitment of URiM candidates to programs. CONCLUSIONS: URiM residents remain disproportionately underrepresented, and markedly so among surgical residency programs. Further research should focus on implementing EDI interventions in surgery and assess URiM resident attrition post-matriculation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 source (direct Gemma or distilled Codex), 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".