No observed effect of a student-led mock objective structured clinical examination on subsequent performance scores in medical students in Canada
Bibliographic record
Abstract
Student-led peer-assisted mock objective structured clinical examinations (MOSCEs) have been used in different settings to help students prepare for subsequent higher-stakes, faculty-run OSCEs. MOSCE participants generally valued feedback from peers and report benefits to learning. Our study investigated whether participation in a peer-assisted MOSCE affects subsequent OSCE performance. To determine whether mean OSCE scores differed depending on whether medical students participated in the MOSCE, we conducted a between-subjects analysis of variance (ANOVA), with cohort (2016 vs. 2017) and MOSCE participation (MOSCE vs. No MOSCE) as independent variables and mean OSCE score as the dependent variable. Participation in the MOSCE had no influence on mean OSCE scores (P=0.19). There was a significant correlation between mean MOSCE scores and mean OSCE scores (Pearson's r = 0.52, P<0.001). Whereas previous studies report self-reported benefits from participation in student-lead MOSCEs, it was not associated with objective benefits in this study.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".