Addressing evidence needs during health crises in the province of Quebec (Canada): a proposed action plan for rapid evidence synthesis
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic necessitated the rapid availability of evidence to respond in a timely manner to the needs of practice settings and decision-makers in health and social services. Now that the pandemic is over, it is time to put in place actions to improve the capacity of systems to meet knowledge needs in a situation of crisis. The main objective of this project was thus to develop an action plan for the rapid syntheses of evidence in times of health crisis in Quebec (Canada). METHODS: We conducted a three-phase collaborative research project. First, we carried out a survey with producers and users of rapid evidence syntheses (n = 40) and a group interview with three patient partners to prioritize courses of action. In parallel, we performed a systematic mapping of the literature to identify rapid evidence synthesis initiatives developed during the pandemic. The results of these two phases were used in a third phase, in which we organized a deliberative workshop with 26 producers and users of rapid evidence syntheses to identifying strategies to operationalize priorities. The data collected at each phase were compared to identify common courses of action and integrated to develop an action plan. RESULTS: A total of 14 specific actions structured into four main axes were identified over the three phases. In axis 1, actions on raising awareness of the importance of evidence-informed decision-making among stakeholders in the health and social services network are presented. Axis 2 includes actions to promote optimal collaboration of key stakeholders in the production of rapid evidence synthesis to support decision-making. Actions advocating the use of a variety of rapid evidence synthesis methodologies known to be effective in supporting decision-making are presented in axis 3. Finally, axis 4 is about actions on the use of effective knowledge translation strategies to promote the use of rapid evidence synthesis products to support decision-making. CONCLUSIONS: This project led to the development of a collective action plan aimed at preparing the Quebec ecosystem and other similar jurisdictions to meet knowledge needs more effectively in times of health emergency. The implementation of this plan and its evaluation will enable us to continue to fine-tune it.
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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 | 0.432 | 0.355 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.019 | 0.018 |
| Science and technology studies | 0.022 | 0.010 |
| Scholarly communication | 0.024 | 0.014 |
| Open science | 0.015 | 0.020 |
| Research integrity | 0.021 | 0.016 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".