Considering context: Adaptive elements of a simulation program to improve primary care safety during the COVID-19 pandemic in Alberta, Canada
Bibliographic record
Abstract
BACKGROUND: Globally, primary care (PC) has been central to the COVID-19 response. The pandemic has strained PC systems and introduced novel infection prevention and control (IPC) risks to the provision of safe, accessible in-person care. Specifically, the implementation of IPC guidance developed outside of PC into its operational context has proved challenging. METHODS: Our team of "action researchers" developed an innovative virtual tabletop simulations (TTS) intervention which assisted PC teams as they adapted, implemented, and integrated IPC guidance into their specific clinical contexts. While we have detailed the "technical" elements of the TTS program elsewhere, this paper examines the specific "adaptive" elements that made this intervention successful in the high-income country context of Alberta, Canada. RESULTS: Multiple factors influenced the uptake of this program in our Albertan setting, including: cultural geography; approach to financing and delivering PC; and policies and cultural norms supporting PC integration, medical education and research, and egalitarian teamwork. CONCLUSIONS: Virtual TTS may provide substantial benefits to IPC and safety improvements in PC settings globally. However, the specific technical and adaptive elements of our Albertan TTS program might, or might not, make these a viable IPC intervention for adapting, spreading, and scaling to other 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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".