An admissions OSCE: the multiple mini‐interview
Bibliographic record
Abstract
CONTEXT: Although health sciences programmes continue to value non-cognitive variables such as interpersonal skills and professionalism, it is not clear that current admissions tools like the personal interview are capable of assessing ability in these domains. Hypothesising that many of the problems with the personal interview might be explained, at least in part, by it being yet another measurement tool that is plagued by context specificity, we have attempted to develop a multiple sample approach to the personal interview. METHODS: A group of 117 applicants to the undergraduate MD programme at McMaster University participated in a multiple mini-interview (MMI), consisting of 10 short objective structured clinical examination (OSCE)-style stations, in which they were presented with scenarios that required them to discuss a health-related issue (e.g. the use of placebos) with an interviewer, interact with a standardised confederate while an examiner observed the interpersonal skills displayed, or answer traditional interview questions. RESULTS: The reliability of the MMI was observed to be 0.65. Furthermore, the hypothesis that context specificity might reduce the validity of traditional interviews was supported by the finding that the variance component attributable to candidate-station interaction was greater than that attributable to candidate. Both applicants and examiners were positive about the experience and the potential for this protocol. DISCUSSION: The principles used in developing this new admissions instrument, the flexibility inherent in the multiple mini-interview, and its feasibility and cost-effectiveness are discussed.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".