Bibliographic record
Abstract
Medical school interviews are critical for screening candidates for admission. Traditionally, the panel format is used for this process, although its drastically low reliabilities sparked the creation of the highly reliable multiple mini-interview (MMI). However, the multiple mini-interview's feasibility issues made it unappealing to some institutions, like the University of Toronto, who created the modified personal interview (MPI) as a more feasible alternative. The lack of literature about the MPI, however, prevents the medical community from determining whether this interview format achieves this goal. Therefore, evidence was compiled and critically appraised for the MPI using Kane's validity framework, which enables analysis of four levels of inference (Scoring, Generalization, Extrapolation, Implication). Upon examining each level, it was concluded that assumptions made at the 'Scoring' and 'Generalization' levels had the least support. Based on these findings, it was recommended that in-person rater training become mandatory and the number of stations increase twofold from four to eight. Moreover, the following research initiatives were suggested to improve understanding of and evidence for the modified personal interview: (1) formally blueprint each station; (2) conduct predictive validity studies for the modified personal interview, and (3) relate admission to medical school on the basis of the MPI with medical error rates. By making these changes and studying these initiatives, the MPI can become a more feasible and equally effective alternative to the MMI with more evidence to justify its implementation at other medical schools.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.096 | 0.269 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.033 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.011 | 0.028 |
| 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".