Interviewers' Experiences with Two Multiple Mini-Interview Scoring Methods Used for Admission to a Master of Physical Therapy Programme
Bibliographic record
Abstract
Purpose: To describe participants' attitudes, beliefs, and experiences with the use of two methods of scoring the Multiple Mini-Interview (MMI) for admission to a Master of Physical Therapy program: a rank-based scoring system (RBS; used from 2007 to 2013) and a criterion-based scoring system (CBS; tested in 2014). The MMI uses short independent assessments to obtain an aggregate score of candidates' professionalism and interpersonal skills, based on behavioural questions within scenarios that assess one attribute at a time. Method: This qualitative descriptive inquiry sought to capture the experiences of 18 MMI interviewers primarily through semi-structured interviews. Interviews were transcribed verbatim, and the data were analyzed using thematic analysis. The results were validated by theoretical and investigator triangulation and member checking. Results: One major theme, scoring systems, and two sub-themes, CBS and RBS, emerged across all data. Participants unanimously agreed that CBS is a more fair and objective way to score candidates' interviews. Conclusions: CBS was well accepted by participants, and the majority preferred it over RBS. Participants felt that CBS presented a more accurate depiction of candidates.
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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 | 0.028 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".