Utility of a Writing Station in the Multiple Mini-Interview
Bibliographic record
Abstract
The multiple mini-interview (MMI) is a reliable and valid method of selecting applicants for admission to health professional schools on the basis of non-cognitive traits. Because the MMI is a series of short interview stations that applicants rotate through in coordinated sequence, it can potentially be resource intensive. However, the MMI design has room for innovation and efficiency. At the University of Manitoba Faculty of Medicine, a 10-minute unsupervised writing station (WS) was incorporated into the MMI to obtain a writing sample from each applicant, to increase the number of independent scores per applicant, and to increase the number of applicants interviewed per circuit without increasing interviewer numbers. One assessor evaluated all the writing samples and assigned a score ranging from 1 to 7. With the inclusion of a WS into an 11-station MMI, the faculty's capacity to interview applicants increased by 9% (from 297 to 324) without substantially increasing interviewer hours needed per day. For 1,257 applicants interviewed in 2008-2011, the mean WS score was 4.03 (SD=1.36), whereas applicants' mean of 10 oral station (OS) scores was 4.62 (SD=0.69). Correlations between WS score and mean OS score ranged from .16 to .27 (p<.01) over the four years. Because inter-station correlations for OS ranged from .01 to .37, the correlation of .21 between WS and mean OS scores for all four years combined appears reasonable. Institutions that want to effectively increase the capacity of their MMI process might consider adding a WS.
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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 | 0.033 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".