Multiple Mini Interview as an admission tool in higher education: Insights from a systematic review
Bibliographic record
Abstract
OBJECTIVES: Multiple Mini Interviews (MMI) have been conducted across the globe in the student selection process, particularly in health profession education. This paper reported the validity evidence of MMI in various educational settings. METHODS: A literature search was carried out through Scopus, Science Direct, Google Scholar, PubMed, and EBSCOhost databases based on specific search terms. Each article was appraised based on title, abstract, and full text. The selected articles were critically appraised, and relevant information to support the validity of MMI in various educational settings was synthesized. This paper followed the PRISMA guideline to ensure consistency in reporting systematic review results. RESULTS: A majority of the studies were from Canada, with 41.54%, followed by the United Kingdom (25.39%), the United States (13.85%), and Australia (9.23%). The rest (9.24%) were from Germany, Ireland, the United Arab Emirates, Japan, Pakistan, Taiwan, and Malaysia. Moreover, most MMI stations ranged from seven to 12 with a duration of 10 min per station (including a 2-min gap between stations). CONCLUSION: The results suggest that the content, response process, and internal structure of MMI were well supported by evidence; however, the relation and consequences of MMI to important outcome variables were inconsistently supported. The evidence shows that MMI is a non-biased, practical, feasible, reliable, and content-valid admission tool. However, further research on its impact on non-cognitive outcomes is required.
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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 | 0.136 | 0.299 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.017 | 0.020 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 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".