Are Female Applicants Rated Higher Than Males on the Multiple Mini-Interview? Findings From the University of Calgary
Bibliographic record
Abstract
PURPOSE: The multiple mini-interview (MMI) improves reliability and validity of medical school interviews, and many schools have introduced this in an attempt to select individuals more skilled in communication, critical thinking, and ethical decision making. But every change in the admissions process may produce unintended consequences, such as changing intake demographics. In this article, two studies exploring gender differences in MMI ratings are reported. METHOD: Cumulative meta-analysis was used to compare MMI ratings for female and male applicants to the University of Calgary Cumming School of Medicine between 2010 and 2014. Multiple linear regression was then performed to explore gender differences in MMI ratings after adjusting for other variables, followed by a sensitivity analysis of the impact of varying the weight given to MMI ratings on the odds of females being ranked in the top 150 applicants for 2014. RESULTS: Females were rated higher than male applicants (standardized mean difference 0.21, 95% CI [0.11, 0.30], P < .001). After adjusting for other explanatory variables, there was a positive association between female applicant and MMI rating (regression coefficient 0.23 [0.14, 0.33], P < .001). Increasing weight assigned to MMI ratings was associated with increased odds of females being ranked in the top 150 applicants. CONCLUSIONS: In this single-center study, females were rated higher than males on the MMI, and the odds of a female applicant being offered a position increased as more weight was given to MMI ratings. Further studies are needed to confirm and explain gender differences in MMI ratings.
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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 | 0.019 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".