Back

Virtual Delivery of Simulation Education to Undergraduate Medical Students During the COVID-19 Pandemic

Wu, K.; Chan, A.; Pandey, A.; Panchal, P.; Khalid, M.; Bala, S.; Shah, S.; Miller, M. C.

2021-08-22 medical education
10.1101/2021.08.20.21262347 medRxiv
Show abstract

BackgroundThe 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. MethodsA 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. ResultsPost-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. ConclusionSimulation-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.

Matching journals

The top 1 journal accounts for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.