The multiple mini-interview for selecting medical residents: First experience in the Middle East region
Bibliographic record
Abstract
INTRODUCTION: Numerous studies have shown that multiple mini-interviews (MMI) provides a standard, fair, and more reliable method for assessing applicants. This article presents the first MMI experience for selection of medical residents in the Middle East culture and an Arab country. METHODS: In 2012, we started using the MMI in interviewing applicants to the residency program of Dubai Health Authority. This interview process consisted of eight, eight-minute structured interview scenarios. Applicants rotated through the stations, each with its own interviewer and scenario. They read the scenario and were requested to discuss the issues with the interviewers. Sociodemographic and station assessment data provided for each applicant were analyzed to determine whether the MMI was a reliable assessment of the non-clinical attributes in the present setting of an Arab country. RESULTS: One hundred and eighty-seven candidates from 27 different countries were interviewed for Dubai Residency Training Program using MMI. They were graduates of 5 medical universities within United Arab Emirates (UAE) and 60 different universities outside UAE. With this applicant's pool, a MMI with eight stations, produced absolute and relative reliability of 0.8 and 0.81, respectively. The person × station interaction contributed 63% of the variance components, the person contributed 34% of the variance components, and the station contributed 2% of the variance components. DISCUSSION: The MMI has been used in numerous universities in English speaking countries. The MMI evaluates non-clinical attributes and this study provides further evidence for its reliability but in a different country and culture. The MMI offers a fair and more reliable assessment of applicants to medical residency programs. The present data show that this assessment technique applied in a non-western country and Arab culture still produced reliable results.
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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 | 0.004 | 0.117 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".