A Generalizability Analysis of a Veterinary School Multiple Mini Interview: Effect of Number of Interviewers, Type of Interviewers, and Number of Stations
Bibliographic record
Abstract
BACKGROUND: The number of Multiple Mini Interview (MMI) stations and the type and number of interviewers required for an acceptable level of reliability for veterinary admissions requires investigation. PURPOSE: The goal is to investigate the reliability of the 2009 MMI admission process at the University of Calgary. METHODS: Each applicant (n = 103; female = 80.6%; M age = 23.05 years, SD = 3.96) participated in a 7-station MMI. Applicants were rated independently by 2 interviewers, a faculty member, and a community veterinarian, within each station (total interviewers/applicant N = 14). Interviewers scored applicants on 3 items, each on a 5-point anchored scale. RESULTS: Generalizability analysis resulted in a reliability coefficient of G = 0.79. A Decision study (D-study) indicated that 10 stations with 1 interviewer would produce a G = 0.79 and 8 stations with 2 interviewers would produce a G = 0.81; however, these have different resource requirements. A two-way analysis of variance showed that there was a nonsignificant main effect of interviewer type (between faculty member and community veterinarian) on interview scores, F(1, 1428) = 3.18, p = .075; a significant main effect of station on interview scores, F(6, 1428) = 4.34, p < .001; and a nonsignificant interaction effect between interviewer-type and station on interview scores, F(6, 1428) = 0.74, p = .62. CONCLUSIONS: Overall reliability was adequate for the MMI. Results from the D-study suggest that the current format with 7 stations provides adequate reliability given that there are enough interviewers; to achieve the same G-coefficient 1 interviewer per station with 10 stations would suffice and reduce the resource requirements. Community veterinarians and faculty members demonstrated an adequate level of agreement in their assessments of applicants.
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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 | 0.187 | 0.337 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.006 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".