Multiple Mini Interview for Selection into OTL Residency
Bibliographic record
Abstract
Objective 1) To determine the reliability of the Multiple Mini Interview (MMI) for resident selection into an otolaryngology–head and neck surgery (OTL‐HNS) program. 2) To assess the degree of acceptance by major stakeholders (interviewers and applicants) towards the MMI when compared to traditional interviews. Method Canadian medical graduates applying to OTL‐HNS residency programs underwent MMI in 2011 and 2012. MMI had 7 stations evaluating unique candidate attributes. Stations include surgical skills assessment and 2 simulation scenarios with standardized actors for noncognitive traits. Reliability was determined, and upon completion, stakeholders rated aspects MMI using 7‐point Likert scale. Results Data were collected from a total of 45 applicants and 19 evaluators. Overall interrater reliability of the MMI was good. The majority of applicants (>80%) felt that MMI helped them present their strengths and that it did not have any gender, cultural, or age bias. Assessors (>85%) felt that the MMI evaluated a valid range of competencies and that it tested more aspects of an applicant than traditional interviews. Both applicants and assessors (>70%) agreed that the MMI was a fair process, and both preferred the MMI over the traditional interview. Conclusion The MMI is a reliable tool for the selection of applicants to an OTL‐HNS residency program. It is well accepted by both applicants and assessors, with the majority of stakeholders preferring the MMI over traditional interviews.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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".