Trust Me, I Know Them: Assessing Interpersonal Bias in Surgery Residency Interviews
Bibliographic record
Abstract
ABSTRACT Background Residency selection integrates objective and subjective data sources. Interviews help assess characteristics like insight and communication but have the potential for bias. Structured multiple mini-interviews may mitigate some elements of bias; however, a halo effect is described in assessments of medical trainees, and degree of familiarity with applicants may remain a source of bias in interviews. Objective To investigate the extent of interviewer bias that results from pre-interview knowledge of the applicant by comparing file review and interview scores for known versus unknown applicants. Methods File review and interview scores of applicants to the University of Ottawa General Surgery Residency Training Program from 2019 to 2021 were gathered retrospectively. Applicants were categorized as “home” if from the institution, “known” if they completed an elective at the institution, or “unknown.” The Kruskal-Wallis H test was used to compare median interview scores between groups and Spearman's rank-order correlation (rs) to determine the correlation between file review and interview scores. Results Over a 3-year period, 169 applicants were interviewed; 62% were unknown, 31% were known, and 6% were home applicants. There was a statistically significant difference (P=.01) between the median interview scores of home, known, and unknown applicants. Comparison of groups demonstrated higher positive correlations between file review and interview scores (rs=0.15 vs 0.36 vs 0.55 in unknown, known, and home applicants) with increasing applicant familiarity. Conclusions There is an increased positive correlation between file review and interview scores with applicant familiarity. The interview process may carry inherent bias insufficiently mitigated by the current structure.
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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 | 0.080 | 0.177 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".