Multiple independent sampling within medical school admission interviewing: an “intermediate approach”
Bibliographic record
Abstract
INTRODUCTION: Balancing reliability and resource limitations as well as recruitment activities during admission interviews is a challenge for many medical schools. The Modified Personal Interview (MPI) has been shown to have good psychometric properties while being resource efficient for specialized admission interviews. We describe implementation of an MPI adaptation integrating psychometric rigour alongside resourcing and recruitment goals for larger-scale medical school admission interviewing at the University of Toronto. METHODS: The MPI was implemented during the 2013-2014 admission cycle. The MPI uses multiple independent sampling by having applicants interviewed in a circuit of four brief semi-structured interviews. Recruitment is reflected in a longer MPI interviewing time to foster a 'human touch'. Psychometric evaluation includes generalizability studies to examine inter-interview reliability and other major sources of error variance. We evaluated MPI impact upon applicant recruitment yield and resourcing. RESULTS: MPI reliability is 0.56. MPI implementation maintained recruitment compared with previous year. MPI implementation required 160 interviewers for 600 applicants whereas for pre-MPI implementation 290 interviewers were required to interview 587 applicants. MPI score correlated with first year OSCE performance at 0.30 (p < 0.05). DISCUSSION: MPI reliability is measured at 0.56 alongside enhanced resource utilization and maintenance of recruitment yield. This 'intermediate approach' may enable broader institutional uptake of integrated multiple independent sampling-based admission interviewing within institution-specific resourcing and recruitment goals.
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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 | 0.129 | 0.143 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".