Multiple mini‐interview test characteristics: ‘tis better to ask candidates to recall than to imagine
Bibliographic record
Abstract
CONTEXT: The multiple mini-interview (MMI), used to facilitate the selection of applicants in health professional programmes, has been shown to be capable of generating reliable data predictive of success. It is a process rather than a single instrument and therefore its psychometric properties can be expected to vary according to the stations generated, the alignment between the stations and the qualities an institution prioritises, and the outcomes used. The purpose of this study was to explore the MMI's test characteristics when station type is manipulated. METHODS: A 12-station MMI was established in which four stations were presented in three different ways. These included: situational judgement (SJ) stations, in which applicants were asked to imagine what they would do in specific situations; behavioural interview (BI) stations, in which applicants were asked to recall what they did in experienced situations, and free form (FF) stations, which were unstructured in that the examiner was simply given a brief explanation of the intent of the station without further guidance on how to conduct the discussion. Four circuits of the 12 stations were run with one examiner within each station. Candidates and examiners were surveyed regarding their experience. The reliability of the scores derived from the assessment was analysed separately for each station type. RESULTS: A total of 41 medical school candidates participated after completing the regular admission process. Although the score assigned did not differ across station type, BI stations more reliably differentiated between candidates (g = 0.77) than did the other station types (SJ, g = 0.69; FF, g = 0.66). The correlation between actual MMI scores and BI stations was also greatest (BI, r = 0.57; SJ, r = 0.45; FF, r = 0.42). Candidates' opinions indicated that FF stations were more anxiety-provoking, less clear, and more difficult than structured stations (SJ and BI stations). Examiner opinions indicated equivalence on these measures. CONCLUSIONS: The results suggest that structuring stations has value, although that value was gained only through the use of BI stations, in which candidates were asked to recall and discuss a specific experience of relevance to the purpose of the interview station.
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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 | 0.007 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".