Comparing the traditional and Multiple Mini Interviews in the selection of post-graduate medical trainees
Bibliographic record
Abstract
Background: The traditional, panel style interview and the multiple mini interview (MMI) are two options to use in the selection of medical trainees with each interview format having inherent advantages and disadvantages. Our aim was to compare the traditional and MMI on the same cohort of postgraduate applicants to the Department of Otolaryngology – Head & Neck Surgery at the University of Toronto. Method: Twenty-seven applicants from the 2010 Canadian Residency Matching Service selected for interview at the University of Toronto, Department of Otolaryngology – Head & Neck Surgery were included in the study. Each applicant participated in both a traditional interview and MMI. Results: Traditional interviews marked out of a total maximum score of 570. On the traditional interview, scores ranged from 397-543.5 (69.6 - 95.3%), the mean was 460.2. The MMI maximum score was out of 180. MMI scores ranged from 93 – 146 (51.7 - 81.1%) with a mean of 114.8. Traditional interview total scores were plotted against MMI total scores. Scores correlated reasonably well, Pearson Correlation = 0.315 and is statistically significant at p = 0.001. Inter-interview reliability for the two interview methods was 0.038, with poor overall agreement 0.07%. Conclusions: MMI and traditional interview scores are correlated but do not reliably lead to the same rank order. We have demonstrated that these two interview formats measure different characteristics. One format may also be less reliable leading to greater variation in final rank. Further validation research is certainly required. Key Words: Multiple mini interview; medical education; traditional interview; postgraduate admissions
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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 | 0.027 | 0.050 |
| 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 | 0.003 |
| 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".