Discerning quality: using the multiple mini‐interview in student selection for the Australian National University Medical School
Bibliographic record
Abstract
OBJECTIVE: To describe the development and pilot testing of a set of admissions instruments based on the McMaster University multiple mini-interview (MMI) and designed to assess desirable, non-cognitive characteristics in order to inform final decisions on candidate selection for entry to medical school. METHODS: Community and faculty consultation on desirable, non-cognitive characteristics of medical students informed the development of a 10-station interview. Two stations occurred as part of a group problem-based learning scenario and 8 occurred as individual observations. All interviewers were trained. Interviews were offered to 115 candidates on an academic merit list. Interview performance was used to exclude candidates considered unsuitable, but not to re-order the academic merit list. Admissions decisions were examined in terms of individual interview station performance. RESULTS: This method proved to be an efficient process by which to interview candidates and to determine suitability. Retained and rejected candidates had significantly different total scores and mean scores for each station. Ten independent observations contributed to each decision, without significant interviewer or logistic burden. Candidates reported high levels of satisfaction with the interview process. CONCLUSIONS: Admissions interviews can be streamlined and efficient, yet remain informative. A longitudinal study is in progress to evaluate the value of the admissions processes in predicting successful graduation to medical practice.
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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 | 0.045 | 0.095 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 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".