Multiple Mini Interviews: The McMaster Experience Thus Far
Bibliographic record
Abstract
The major challenge for an undergraduate medical admissions committee is to select students with desirable non-cognitive qualities from a large pool of high-achieving applicants. The selection process needs to be comprehensive as accepting the wrong person into medical school can have serious consequences, especially for patients. Thus, medical schools have complemented their selection tools of cognitive performance with assessments of non-cognitive attributes considered important in future doctors. However, traditional admissions tools designed to measure non-cognitive attributes have been plagued by questionable reliability, poor predictive validity and tenuous test security. Interest in improving the medical school admissions processes led to the development of the Multiple Mini Interviews (MMI) at McMaster University. The MMI is a series of short, structured interviews designed to assess applicants' non-cognitive attributes. Since each candidate is assessed multiple times by various assessors in various contexts, the MMI greatly dilutes the issues of chance and examiner bias inherent in traditional assessment tools. Consistent with expectations, initial research on the MMI at McMaster showed that it has high reliability and validity, findings which were replicated in subsequent studies conducted at other universities. Further studies demonstrated the MMI's ability to predict non-cognitive performances in licensing examinations. Finally, the MMI was found to be a feasible and cost-effective method of interviewing compared to traditional types of interviews. Currently, the MMI is implemented in the majority of medical schools in Canada. As well, it has gained international recognition and has been used or is in plans for use in many other countries.
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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 | 0.022 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".