Virtual Delivery of Simulation Education to Undergraduate Medical Students During the COVID-19 Pandemic
Bibliographic record
Abstract
Abstract Background The COVID-19 pandemic has restricted in-person clinical training for medical students. Simulation-based teaching is a promising tool to introduce learners to the clinical environment. MacSim is a student-led simulation workshop for learners to develop clinical competencies. The objective of this study was to assess the impacts of MacSim and participants’ perspectives regarding simulation-based teaching. Methods A comprehensive simulation, representative of a virtual care scenario, was delivered to 42 pre-clerkship medical students via video conferencing. In pairs, participants obtained histories and carried out management plans for simulated patients. Participants were surveyed and interviewed. Survey data were analyzed using the Wilcoxon signed-ranks test. Interview transcript data were thematically analyzed. Results Post-simulation, participants (n=24) felt more prepared to make clinical decisions, collaborate, and communicate in a virtual setting. 92% of respondents agreed MacSim was a valuable learning experience and 96% agreed more simulation-based learning should be integrated into curricula. Emergent themes from interviews (n=12) included: 1) value of simulation fidelity, 2) value of physician feedback, and 3) effectiveness of MacSim in improving virtual clinical skills. Conclusion Simulation-based teaching is of importance and educational value to medical students. It may play an increasingly prevalent role in education as virtual care is likely to become more prevalent.
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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 | 0.008 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".