Developing the Virtual Resus Room: Fidelity, Usability, Acceptability, and Applicability of a Virtual Simulation for Teaching and Learning
Bibliographic record
Abstract
PROBLEM: Physical distancing restrictions during the COVID-19 pandemic led to the transition from in-person to online teaching for many medical educators. This report describes the Virtual Resus Room (VRR)-a free, novel, open-access resource for running collaborative online simulations. APPROACH: The lead author created the VRR in May 2020 to give learners the opportunity to rehearse their crisis resource management skills by working as a team to complete virtual tasks. The VRR uses Google Slides to link participants to the virtual environment and Zoom to link participants to each other. Students and facilitators in the emergency medicine clerkship at McMaster University used the VRR to run 2 cases between June and August 2020. Students and facilitators completed a postsession survey to assess usability and acceptability, applicability for learning or teaching, and fidelity. In addition, students took a knowledge test pre- and postsession. OUTCOMES: Forty-six students and 11 facilitators completed the postsession surveys. Facilitators and students rated the VRR's usability and acceptability, applicability for learning and teaching, and fidelity highly. Students showed a significant improvement in their postsession (mean = 89.06, standard deviation [SD] = 9.56) compared with their presession knowledge scores (mean = 71.17, SD = 15.77; t(34) = 7.28, P < .001, with a large effect size Cohen's d = 1.23). Two perceived learning outcomes were identified: content learning and communication skills development. The total time spent (in minutes) facilitating VRR simulations (mean = 119, SD = 36) was significantly lower than time spent leading in-person simulations (mean = 181, SD = 58; U = 20.50, P < .008). NEXT STEPS: Next steps will include expanding the evaluation of the VRR to include participants from additional learner levels, from varying sites, and from other health professions.
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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 | 0.015 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".