The Ability of the Multiple Mini-Interview to Predict Preclerkship Performance in Medical School
Bibliographic record
Abstract
PROBLEM STATEMENT AND BACKGROUND: One of the greatest challenges continuing to face medical educators is the development of an admissions protocol that provides valid information pertaining to the noncognitive qualities candidates possess. An innovative protocol, the Multiple Mini-Interview, has recently been shown to be feasible, acceptable, and reliable. This article presents a first assessment of the technique's validity. METHOD: Forty five candidates to the Undergraduate MD program at McMaster University participated in an MMI in Spring 2002 and enrolled in the program the following autumn. Performance on this tool and on the traditional protocol was compared to performance on preclerkship evaluation exercises. RESULTS: The MMI was the best predictor of objective structured clinical examination performance and grade point average was the most consistent predictor of performance on multiple-choice question examinations of medical knowledge. CONCLUSIONS: While further validity testing is required, the MMI appears better able to predict preclerkship performance relative to traditional tools designed to assess the noncognitive qualities of applicants.
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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 | 0.008 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".