Experience of Establishment of Multiple Mini structure Interview as part of student admission policy at Faculty of Medicine, King Abdulaziz University, 2011–2012
Bibliographic record
Abstract
Faculty of Medicine (FOM), King Abdulaziz University (KAU), requested for international recognition by the Laison Committee of Canadian Medical Education (LCME) during the period 2008-2010. Selection of medical students was a must standard in LCME. After obtaining a written permission from higher administration at KAU, a committee for the establishment of multiple-mini-interview (MMI) was formed and they conducted workshops to train faculty members at FOM on such process. The interviews were set up in a manner similar to that of an objective-structured clinical evaluation (OSCE), with the applicant moving from one station to another. The applicant was either asked to discuss a scenario or respond to direct questions. The interviewers used a standardized scoring form to rate candidates. When the data were analyzed, it was found that the performance of men students was insignificantly higher than that of women students in stations concerned with personnel character and professionalism. The performance of women students was significantly higher in all other stations (those considered motivation, morals and bioethics, team work and communication skills and behaviors). The women's overall performance was significantly higher than men.
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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 | 0.023 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".