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Record W3192647138 · doi:10.1097/acm.0000000000004364

Developing the Virtual Resus Room: Fidelity, Usability, Acceptability, and Applicability of a Virtual Simulation for Teaching and Learning

2021· article· en· W3192647138 on OpenAlexaff
Sarah Foohey, Alim Nagji, Yusuf Yılmaz, Matthew Sibbald, Sandra Monteiro, Teresa M. Chan

Bibliographic record

VenueAcademic Medicine · 2021
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsRoyal College of Physicians and Surgeons of CanadaMcMaster University Medical CentreSt. Joseph’s Healthcare HamiltonMcMaster UniversityJoseph Brant HospitalTrillium Health Centre
Fundersnot available
KeywordsUsabilityDistance educationPsychologyMedical educationFidelityDistancingCoronavirus disease 2019 (COVID-19)Applied psychologyComputer scienceMedicineMathematics education

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.042
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.042
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.076
GPT teacher head0.431
Teacher spread0.355 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations25
Published2021
Admission routes1
Has abstractyes

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