The Reliability and Acceptability of the Multiple Mini-Interview as a Selection Instrument for Postgraduate Admissions
Bibliographic record
Abstract
BACKGROUND: The Multiple Mini-Interview (MMI) is useful in selecting undergraduate medical trainees. Postgraduate applicant pools have smaller numbers of more homogeneous candidates that must be actively recruited while being assessed. This paper reports on the MMI's use in assessing residency candidates. METHOD: Canadian and international medical graduates to three residency programs--obstetrics-gynecology and pediatrics (McMaster University) and internal medicine (University of Alberta)--underwent the MMI for residency selection (n = 484) in 2008 and 2009. Reliability was determined and candidates and interviewers completed an exit survey assessing acceptability. RESULTS: Overall reliability of the MMI was acceptable, ranging from 0.55 to 0.72. Using 10 stations would increase reliability to 0.64-0.79. Eighty-eight percent of candidates believed they could accurately portray themselves, while 90% of interviewers believed they could reasonably judge candidates' abilities. CONCLUSIONS: The MMI provides a reliable way to assess residency candidates that is acceptable to both candidates and assessors across a variety of programs.
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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 | 0.049 | 0.123 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".