A student-initiated objective structured clinical examination as a sustainable cost-effective learning experience
Bibliographic record
Abstract
BACKGROUND: The objective structured clinical examination (OSCE) has gained widespread use as a form of performance assessment. However, opportunities for students to participate in practice OSCEs are limited by the financial, faculty and administrative investments required. OBJECTIVES: To determine the feasibility and acceptability of a student-run mock OSCE (MOSCE) as a learning experience for medical students of all 4 years. DESIGN: We conducted a five-station MOSCE for third-year students. This involved fourth-year students as examiners and first-/second-year students as standardized patients (SPs). Each examiner scored examinees using a checklist and global rating scale while providing written and verbal feedback. MOSCE stations and checklists were designed by students and reviewed by a faculty supervisor. Following the MOSCE, participants completed surveys which elucidated their perceptions on the roles they took during the MOSCE. RESULTS: Fifty examinees participated in the MOSCE. Of these, 42 (84%) consented to participate in the study and submitted completed questionnaires. Twenty-four examiners participated in the OSCE and consented to participate in the study, with 22 (92%) submitting completed questionnaires. Fifty-three of 60 SPs (88%) agreed to take part in this study, and 51 (85%) completed questionnaires. The internal consistency of the five-station OSCE was calculated as a Cronbach's alpha of 0.443. Students commented positively on having the opportunity to network and engage in mentorship activities and reinforce clinical concepts. CONCLUSIONS: Examinees, examiners, and SPs all perceived the MOSCE to be a beneficial learning experience. We found the MOSCE to be a feasible and acceptable means of providing additional OSCE practice to students prior to higher-stakes evaluations.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".