Feasibility and Acceptability of a Multiple Mini-interview Format for Evaluation of Small Animal Internal Medicine Residency and Internship Candidates
Bibliographic record
Abstract
The multiple mini-interview (MMI) format assesses candidates’ performance in various competencies and is becoming commonplace in medical school and residency programs. This interview format compares to and surpasses the traditional interview in validity, reliability, feasibility, and acceptability. We developed a MMI to assess resident and specialty intern candidates for Small Animal Internal Medicine over a 3-year period. Our aims were to assess acceptability by obtaining impressions from interviewers and candidates of the MMI process and to evaluate feasibility by quantifying time required. In total, 61 resident candidates completed the survey, a response rate of 70% of total interviewees. Respondents reported the MMI as more stressful (82%, 95% CI: 70%, 91%) and less enjoyable (62%, 95% CI: 49%, 74%). While 54% (95% CI: 41%, 67%) of respondents preferred a traditional interview process, 70% (95% CI: 57%, 81%) perceived that the MMI was fairer and 51% (95% CI: 38%, 64%) felt it allowed them to better demonstrate their strengths. Most (63%, 95% CI: 49%, 75%) reported that their experience with the MMI would lead them to rank the program more highly. Interviewers preferred the MMI process due to improved perceived fairness and time efficiency. MMI required less total time, 29.5 hours versus 112 hours for the traditional interview over 3-years ( p = .002) and lower per applicant time investment (.34 hours/applicant vs. 1.2 hours/applicant, p = .0001). In conclusion, the MMI process was acceptable to interviewees, preferred by interviewers, and was feasible in terms of time savings compared to traditional interviews.
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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 | 0.089 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".