In-group bias in residency selection
Bibliographic record
Abstract
BACKGROUND: More than half of all Canadian medical graduates match to residency programs within the same university as their medical school. Here we describe two studies designed to explore whether there is partiality for internal applicants in the resident selection process. METHODS: We first performed an observational study in which we compared the ratings of 14 'internal' and 89 'external' applicants to the University of Calgary Internal Medicine Training Program by resident and faculty raters. Following this we then asked residents to rate anonymous application packages in which we manipulated applicants' affiliation to our training program. RESULTS: In our first study, we found that residents rated internal applicants significantly higher for both application packages (mean (SD)) rating for internal versus external applicants (4.86 (0.36) vs. 4.36 (0.57), d = 1.05, p = 0.002) and interviews (4.93 (0.27) vs. 4.36 (0.7), d = 1.07, p = 0.003). There was no difference in the faculty ratings of internal and external applicants. In our second study, we found that residents rated applicants with an affiliation to our program - either attending the local medical school or having completed an elective - higher than applicants with no affiliation to our program. CONCLUSIONS: Our finding support in-group bias during resident selection, possibly due to the interdependent relationship between residents and students. Considering the career implications of residency matching, we feel that further studies are needed to identify and mitigate sources of bias in the residency application process.
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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 | 0.054 | 0.136 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".