Virtual tabletop simulations for primary care pandemic preparedness and response
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 pandemic prompted widescale use of clinical simulations to improve procedures and practices. We outline our deployment of a virtual tabletop simulation (TTS) method in primary care (PC) clinics across Alberta, Canada. We summarise the quality and safety improvements from this method and report end users' perspectives on key elements. METHODS: Our virtual TTS used teleconferencing software alongside digital whiteboards to walk clinic stakeholders through patient scenarios. Participants reviewed and rehearsed their workflows and care practices. The goal was for staff to take ownership over gaps and codesigned solutions. After simulation sessions, follow-up interviews were conducted to collect feedback. RESULTS: These sessions helped PC staff identify and codesign solutions for clinical hazards and threats. These included the flow of patients through clinics, communications, redesignation of physical spaces, and adaptation of guidance for cleaning and personal protective equipment use. End users reported sessions provided neutral spaces to discuss practice changes and built confidence in delivering safe care during the pandemic. DISCUSSION: TTS has not been extensively deployed to improve clinical practice in outpatient environments. We show how virtual TTS can bridge gaps between knowledge and practice by offering a guided space to rehearse clinical changes. We show that virtual TTS can be used in multiple contexts to help identify hazards, improve safety and build confidence in professional teams adapting to rapid changes in both policies and practices. While our sessions were conducted in Alberta, our results suggest this method may be deployed in other contexts, including low-resource settings.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".