Impact of a Student-Driven, Virtual Patient Application on Objective Structured Clinical Examination Performance: Observational Study
Bibliographic record
Abstract
BACKGROUND: Peer-assisted learning (PAL) refers to a learning activity whereby students of similar academic level teach and learn from one another. Groupe de perfectionnement des habiletés cliniques (Clinical Skills Improvement Group), a student organization at Université Laval, Canada, propelled PAL into the digital era by creating a collaborative virtual patient platform. Medical interviews can be completed in pairs (a student-patient and a student-doctor) through an interactive Web-based application, which generates a score (weighted for key questions) and automated feedback. OBJECTIVES: The aim of the study was to measure the pedagogical impact of the application on the score at medical interview stations at the summative preclerkship Objective Structured Clinical Examination (OSCE). METHODS: We measured the use of the application (cases completed, mean score) in the 2 months preceding the OSCE. We also accessed the results of medical interview stations at the preclerkship summative OSCE. We analyzed whether using the application was associated with higher scores and/or better passing grades (≥60%) at the OSCE. Finally, we produced an online form where students could comment on their appreciation of the application. RESULTS: Of the 206 students completing the preclerkship summative OSCE, 170 (82.5%) were registered users on the application, completing a total of 3133 cases (18 by active user in average, 7 minutes by case in average). The appreciation questionnaire was answered online by 45 students who mentioned appreciating the intuitive, easy-to-use, and interactive design, the diversity of cases, and the automated feedback. Using the application was associated with reduced reported stress, improved scores (P=.04), and improved passing rates (P=.11) at the preclerkship summative OSCE. CONCLUSIONS: This study suggests that PAL can go far beyond small-group teaching, showing students' potential to create helpful pedagogical tools for their peers.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".