Virtual tabletop simulations for primary care pandemic preparedness and response
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it