Activities used by evidence networks to promote evidence-informed decision-making in the health sector– a rapid evidence review
Bibliographic record
Abstract
BACKGROUND: Evidence networks facilitate the exchange of information and foster international relationships among researchers and stakeholders. These networks are instrumental in enabling the integration of scientific evidence into decision-making processes. While there is a global emphasis on evidence-based decision-making at policy and organisational levels, there exists a significant gap in our understanding of the most effective activities to exchange scientific knowledge and use it in practice. The objective of this rapid review was to explore the strategies employed by evidence networks to facilitate the translation of evidence into decision-making processes. This review makes a contribution to global health policymaking by mapping the landscape of knowledge translation in this context and identifying the evidence translation activities that evidence networks have found effective. METHODS: The review was guided by standardised techniques for conducting rapid evidence reviews. Document searching was based on a phased approach, commencing with a comprehensive initial search strategy and progressively refining it with each subsequent search iterations. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement was followed. RESULTS: The review identified 143 articles, after screening 1135 articles. Out of these, 35 articles were included in the review. The studies encompassed a diverse range of countries, with the majority originating from the United States (n = 14), followed by Canada (n = 5), Sweden (n = 2), and various other single locations (n = 14). These studies presented a varied set of implementation strategies such as research-related activities, the creation of teams/task forces/partnerships, meetings/consultations, mobilising/working with communities, influencing policy, activity evaluation, training, trust-building, and regular meetings, as well as community-academic-policymaker engagement. CONCLUSIONS: Evidence networks play a crucial role in developing, sharing, and implementing high-quality research for policy. These networks face challenges like coordinating diverse stakeholders, international collaboration, language barriers, research consistency, knowledge dissemination, capacity building, evaluation, and funding. To enhance their impact, sharing network efforts with wider audiences, including local, national, and international agencies, is essential for evidence-based decision-making to shape evidence-informed policies and programmes effectively.
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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 | 0.163 | 0.355 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.027 | 0.021 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.016 | 0.025 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".