A Generalizability Analysis of a Veterinary School Multiple Mini Interview: Effect of Number of Interviewers, Type of Interviewers, and Number of Stations
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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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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it