Discerning quality: using the multiple mini‐interview in student selection for the Australian National University 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
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.
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.
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 | 0.005 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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