Bibliographic record
Abstract
Purpose: The multiple mini-interview (MMI) is a validated technique used in the admissions process in some undergraduate and postgraduate schools and is reported to reduce subjectivity in selecting postgraduate applicants. No studies have been conducted in Saudi Arabia concerning the MMI. The authors report their experience of transitioning from traditional interviews to the MMI and the results of a post-MMI survey undertaken by participating applicants and interviewers. Materials and Methods: The authors did retrospective analysis after MMI had been conducted at the College of Medicine, King Saud University, Riyadh, Saudi Arabia, in 2019, in coordination with an internal medicine residency program. They implemented MMIs totaling four stations of 10 min each that focused on the domains of communication, attitude, knowledge, and a mini-interview personalized for each candidate. Ten questioners interviewed 99 applicants, of whom 68 undertook a post-MMI survey. Results: In terms of their perceptions and experience, the applicants and interviewers responded positively to the transition from traditional interviews to the MMI. MMI was seen to be more objective, 75% of applicants felt it was associated with less anxiety, and 79% believed it provided a better portrayal of their abilities. Conclusion: The use of the MMI in selecting postgraduate applicants in Saudi Arabia is feasible and acceptable. Furthermore, it may give an improved objective portrayal of applicants' abilities and reduce their interview-associated anxiety.
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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 | 0.021 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.007 |
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".