Multiple mini‐interviews predict clerkship and licensing examination performance
Bibliographic record
Abstract
OBJECTIVE: The Multiple Mini-Interview (MMI) has previously been shown to have a positive correlation with early medical school performance. Data have matured to allow comparison with clerkship evaluations and national licensing examinations. METHODS: Of 117 applicants to the Michael G DeGroote School of Medicine at McMaster University who had scores on the MMI, traditional non-cognitive measures, and undergraduate grade point average (uGPA), 45 were admitted and followed through clerkship evaluations and Part I of the Medical Council of Canada Qualifying Examination (MCCQE). Clerkship evaluations consisted of clerkship summary ratings, a clerkship objective structured clinical examination (OSCE), and progress test score (a 180-item, multiple-choice test). The MCCQE includes subsections relevant to medical specialties and relevant to broader legal and ethical issues (Population Health and the Considerations of the Legal, Ethical and Organisational Aspects of Medicine[CLEO/PHELO]). RESULTS: In-programme, MMI was the best predictor of OSCE performance, clerkship encounter cards, and clerkship performance ratings. On the MCCQE Part I, MMI significantly predicted CLEO/PHELO scores and clinical decision-making (CDM) scores. None of these assessments were predicted by other non-cognitive admissions measures or uGPA. Only uGPA predicted progress test scores and the MCQ-based specialty-specific subsections of the MCCQE Part I. DISCUSSION: The MMI complements pre-admission cognitive measures to predict performance outcomes during clerkship and on the Canadian national licensing examination.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".