The acceptability of the multiple mini interview for resident selection.
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: This study describes and assesses the acceptability of the multiple mini interview (MMI) to both international medical graduate (IMG) applicants to family medicine residency training in Alberta, Canada, and also interviewers for Alberta's International Medical Graduate Program (AIMGP), an Alberta Health and Wellness government initiative designed to help integrate IMGs into Canadian residency training. IMGs are physicians who completed undergraduate medical education outside of Canada and the United States. IMGs who live in the Canadian province of Alberta may obtain a limited number of government-funded positions for residency training by applying to AIMGP. METHODS: A literature review and faculty and medical community consultation informed the development of a 12-station MMI designed to identify non-cognitive characteristics associated with professionalism potential. Clinical scenarios were developed by family physicians and medical educators. Applicant and interviewer posttest acceptability was assessed using surveys. Quantitative data were analyzed using descriptive statistics, and qualitative data were analyzed using content analysis and thematic description. RESULTS: Our research demonstrates evidence for applicant and interviewer acceptability of the MMI. Interviewers reported high levels of satisfaction with the time-restricted process that addressed multiple situations pertinent to the Canadian family medicine context. Applicants and interviewers were each satisfied that 8 minutes was enough time at each station. Applicants reported that they felt the process was free from gender and cultural bias. Interviewers agreed that this MMI was a fair assessment of potential for family medicine. CONCLUSIONS: Standardized residency selection interviews can be adapted to measure professionalism potential characteristics important to family medicine in ways that are acceptable to IMG applicants and interviewers.
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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 | 0.172 | 0.295 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".