Multiple mini‐interviews versus traditional interviews: stakeholder acceptability comparison
Bibliographic record
Abstract
CONTEXT: The McGill University Faculty of Medicine undertook a pilot, simulation-based multiple mini-interview (MMI) for medical school applicant selection, which ran simultaneously with traditional unstructured interviews (all applicants underwent both processes). This paper examines major stakeholder (applicants and evaluators) opinions towards the MMI compared with traditional interviews, including perceptions about the feasibility and utility of the MMI. METHODS: A total of 100 candidates applying to McGill University Medical School were enrolled in the pilot comparison of the MMI with the traditional, unstructured interview. Applicants' opinions were obtained by questionnaire shortly after the process (for all applicants) and approximately 6 months after the interviews (for non-accepted applicants). Evaluators' perceptions were also surveyed. Questionnaires contained both quantitative items and space for qualitative impressions. Descriptive statistics, repeated measures analysis of variance (manova) and analysis of the topics raised in written comments were conducted. RESULTS: Univariate analyses of response scores revealed statistically significant differences, with the MMI rated more highly than the traditional interview on fairness, imposition of stress and effectiveness as a measurement tool. Compared with the traditional interview, applicants also felt the MMI: (i) allowed them to be competitive; (ii) was enjoyable, and (iii) was often a favourite part of their interview experience. It should be noted that applicants were aware that their MMI score would be included in their overall interview rating. Written comments were positive with regard to, for example, fairness, the provision of opportunities to show one's strengths, and appreciation of the fidelity of the simulations. Evaluators' responses were in agreement with applicants' responses, albeit that overall they expressed more caution about the MMI. CONCLUSIONS: Results suggest the MMI is a promising selection tool from the point of view of both applicants and evaluators. Both groups expressed concerns, but overall the response was favourable for the MMI in comparison with traditional interviews, and the MMI has been adopted by McGill University's medical school.
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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 | 0.083 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".