The Influence of Applicant and Reviewer Gender on Resident Selection for Internal Medicine
Bibliographic record
Abstract
BACKGROUND: While gender bias in medicine, including physician training, has been well described, less is known about gender bias in the selection process for post graduate residency training programs. This analysis reviews the potential role of gender on resident selection for an internal medicine residency program. METHODS: File review and interview overall and component scores were analyzed based on the gender of the applicant. File review scores were further analyzed based on the reviewer's gender. RESULTS: Women applicants scored higher than men applicants on their file review. There were no differences in any one component score except for leadership in art. Women file reviewers scored applicants higher than men file reviewers, but there was no difference between gender scores. There was no difference in overall or component interview scores between men or women applicants. Scoring did not impact the expected rank performance of applicants based on gender at any stage of the selection process. CONCLUSIONS: While higher scores were observed in women applicants upon their file review, and women reviewers provided higher file review scores, this did not appear to impact the expected number of women and men applicants at each stage of the applicant process. This suggests a potential lack of gender bias at these stages of applicant selection.
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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 | 0.033 | 0.147 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".