Assessment of non‐cognitive traits through the admissions multiple mini‐interview
Bibliographic record
Abstract
CONTEXT: Contemporary studies have shown that traditional medical school admissions interviews have strong face validity but provide evidence for only low reliability and validity. As a result, they do not provide a standardised, defensible and fair process for all applicants. METHODS: In 2006, applicants to the University of Calgary Medical School were interviewed using the multiple mini-interview (MMI). This interview process consisted of 9, 8-minute stations where applicants were presented with scenarios they were then asked to discuss. This was followed by a single 8-minute station that allowed the applicant to discuss why he or she should be admitted to our medical school. Sociodemographic and station assessment data provided for each applicant were analysed to determine whether the MMI was a valid and reliable assessment of the non-cognitive attributes, distinguished between the non-cognitive attributes, and discriminated between those accepted and those placed on the waitlist (waiting list). We also assessed whether applicant sociodemographic characteristics were associated with acceptance or waitlist status. RESULTS: Cronbach's alpha for each station ranged from 0.97-0.98. Low correlations between stations and the factor analysis suggest each station assessed different attributes. There were significant differences in scores between those accepted and those on the waitlist. Sociodemographic differences were not associated with status on acceptance or waiting lists. DISCUSSION: The MMI is able to assess different non-cognitive attributes and our study provides additional evidence for its reliability and validity. The MMI offers a fairer and more defensible assessment of applicants to medical school than the traditional interview.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".