Developing a rapid evidence response to<scp>COVID</scp>‐19: The collaborative approach of Saskatchewan, Canada
Bibliographic record
Abstract
Introduction: The COVID-19 Evidence Support Team (CEST) was a provincial initiative that combined the support of policymakers, researchers, and clinical practitioners to initiate a new learning health cycle (LHS) in response to the pandemic. The primary aim of CEST was to produce and sustain the best available COVID-19 evidence to facilitate decision-making in Saskatchewan, Canada. To achieve this objective, four provincial organizations partnered to establish a single, data-driven system. Methods: The CEST partnership was driven by COVID-19 questions from Emergency Operational Committee (EOC) of the Saskatchewan Health Authority. CEST included three processes: (a) clarifying the nature and priority of COVID-19 policy and clinical questions; (b) providing Rapid Reviews (RRR) and Evidence Search Reports (ESR); and (c) seeking the requestors' evaluation of the product. A web-based repository, including a dashboard and database, was designed to house ESRs and RRRs and offered a common platform for clinicians, academics, leaders, and policymakers to find COVID-19 evidence. Results: In CEST's first year, 114 clinical and policy questions have been posed resulting in 135 ESRs and 108 RRRs. While most questions (41.3%) originated with the EOC, several other teams were assembled to address a myriad of questions related to areas such as long-term care, public health and prevention, infectious diseases, personal protective equipment, vulnerable populations, and Indigenous health. Initial challenges were mobilization of diverse partners and teams, remote work, lack of public access, and quality of emerging COVID-19 literature. Current challenges indicate the need for institutional commitment for CEST sustainability. Despite these challenges, the CEST provided the Saskatchewan LHS with a template for successful collaboration. Conclusions: The urgency of COVID-19 pandemic and the implementation of the CEST served to catalyze collaboration between different levels of a Saskatchewan LHS.
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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 | 0.228 | 0.260 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.016 | 0.011 |
| Scholarly communication | 0.024 | 0.006 |
| Open science | 0.010 | 0.028 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".