Use of Virtually Facilitated Simulation to Improve COVID-19 Preparedness in Rural and Remote Canada
Bibliographic record
Abstract
Background The Alberta Health Services' Provincial Simulation Program (eSIM) is Canada's largest simulation program. The eSIM mobile simulation program specializes in delivering simulation-based education (SBE) to rural and remote communities (RRC). During the COVID-19 pandemic, a quality improvement project involving rapid cycle in situ virtually facilitated simulation (VFS) for COVID-19 airway management and health systems preparedness in RRC was successfully implemented. Methods Between April 24 and July 31, 2020, a team of six rural simulationists (four nurses and two physicians) provided 24 VFS sessions with virtual debriefing to 200 health care providers distributed across 11 RRC in Alberta and the Northwest Territories, covering a geographic area of approximately 169,028 km 2 . Results Video analysis of sequential VFS rapid cycle sessions using a standardized observational tool indicated decreased personal protective equipment (PPE) breaches by 36.6% between the first and third cycles. Teams demonstrated increased competency with airway management such as correct use of bag-valve-mask ventilation, and implementation of health system process improvements, such as incorporation of an intubation checklist. Improvements occurred on average over 2.2 rapid cycles completed within 1.3 weeks per RRC. Postsession self-reported participant electronic surveys indicated self-reported improvement in clinical management, teamwork behavior, and health systems issues outcome measures which were categorized based on the Crisis Resource Management and Systems Engineering Initiative for Patient Safety (SEIPS) frameworks. Of the 48 survey respondents, 86.1% reported that VFS was equivalent or superior to in-person simulation. The cost of VFS was 62.9% lower than comparable in-person SBE. Conclusion VFS provides a rapidly mobilizable and cost-effective way of delivering high-quality SBE to geographically isolated communities.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".