Assessment of Applicants to the Veterinary Curriculum Using a Multiple Mini-Interview Method
Bibliographic record
Abstract
This study describes the development, implementation, and psychometric assessment of the multiple mini-interview (MMI) for the inaugural class of veterinary medicine applicants at the University of Calgary Faculty of Veterinary Medicine (UCVM). The MMI is a series of approximately five to 12 10-minute interviews that consist of situational events. Applicants are given a scenario and asked to work through an issue or behavioral-type questions that are meant to assess one attribute (e.g., empathy) at a time. This structure allows for multiple assessments of the applicants by trained interviewers on the same questions. MMI scenario development was based on a review of the noncognitive attributes currently assessed by the 31 veterinary schools across Canada and the United States and the goals and objectives of UCVM. The noncognitive attributes of applicants (N=110) were assessed at five stations, by two interviewers within each station, on three items using a standardized rating form on an anchored 1-5 scale. The method was determined to be reliable (G-coefficient=0.88) and demonstrated evidence of validity. The MMI score did not correlate with grade-point average (r=0.12, p=0.22). While neither the applicants nor interviewers had participated in an MMI format before, both groups reported the process to be acceptable in a post-interview questionnaire. This analysis provides preliminary evidence of the reliability, validity, and acceptability of the MMI in assessing the noncognitive attributes of applicants for veterinary medical school admissions.
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How this classification was reachedexpand
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 | 0.002 | 0.005 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".