Medical student selection: choice of a semi-structured panel interview or an unstructured one-on-one interview
Bibliographic record
Abstract
Reliability has been shown to be higher in structured medical admissions interviews as compared to unstructured interviews. This study reports the comparison of a proposed semi-structured panel interview with a current individual unstructured medical admissions interview. Inter-rater reliability coefficients were calculated, and correlations were estimated between panel, individual and academic scores. Admission status in 2003 was related to these scores by means of logistic regression. Both individual and panel interviews were significantly correlated with admissions status. The inter rater reliability coefficient (from individual interviews) was 0.12 whereas the interpanel reliability coefficient was 0.52. Panel interview: good across panel and within panel consistency of scoring. No effect of who asked the questions, question order, or interview duration on scoring. No correlation between panel interview scores and academic variables (MCAT, GPA). We found good inter-panel reliability, a high consistency within and between interview panels, and uniformly positive questionnaire responses. The panel interview measures something different from academic variables. These data, in conjunction with a strong sense from the medical and psychological literature supporting the reliability and validity of a semi-structured panel interview, support our decision to replace our individual interview with the panel interview.
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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 | 0.062 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".