The Ability of the Multiple Mini-Interview to Predict Preclerkship Performance in Medical School
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it