Acceptability and reliability of multiple mini interviews for admission to otolaryngology residency
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: To evaluate the acceptability and reliability of the Multiple Mini Interview (MMI) for the selection of applicants to an Otolaryngology-Head and Neck (OTL-HNS) residency program. STUDY DESIGN: Prospective observational study. METHODS: Canadian medical graduates applying to the OTL-HNS residency program at McGill University in 2011 and 2012 underwent a 7-station MMI. Upon completion, the major stakeholders commented on and rated various aspects of the MMI using a 7-point Likert scale. Descriptive statistics were used to analyze the quantitative portion of the exit survey, while content analysis and thematic description was applied to qualitative data. Interrater reliability was examined with intraclass correlation coefficients. RESULTS: Data was collected from 45 applicants and 18 evaluators. The majority of applicants (>80%) felt that the MMI helped them present their strengths and was free of any gender, cultural, or age bias. Assessors (>85%) agreed the MMI evaluated a valid range of competencies, and that it tested more aspects of an applicant than did traditional interviews. Both applicants and assessors (>70%) agreed that the MMI was a fair process, and both preferred it over the traditional interview. Overall, interrater reliability of the MMI was good. CONCLUSION: This is the first study to examine how the MMI interview process can be adapted for admission to an OTL-HNS residency program, while showing both good acceptability for all major stakeholders and good reliability.
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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 | 0.122 | 0.293 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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 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".