Knowledge management tools and mechanisms for evidence-informed decision-making in the WHO European Region: a scoping review
Bibliographic record
Abstract
Abstract Background Knowledge management (KM) emerged as a strategy to promote evidence-informed decision-making. This scoping review aims to map existing KM tools and mechanisms used to promote evidence-informed health decision-making in the WHO European Region and identify knowledge gaps. Methods Following the Joanna Briggs Institute (JBI) guidance for conducting scoping reviews, we searched Medline, PubMed, EMBASE, the Cochrane library, and Open Grey. We conducted a descriptive analysis of the general characteristics of the included papers and conducted narrative analysis of the included studies and categorized studies according to KM type and phase. Results Out of 9541 citations identified, we included 141 studies. The KM tools mostly assessed are evidence networks, surveillance tools, observatories, data platforms and registries, with most examining KM tools in high-income countries of the WHO European region. Findings suggest that KM tools can identify health problems, inform health planning and resource allocation, increase the use of evidence by policymakers and stimulate policy discussion. Conclusion Policymakers and funding agencies are called to support capacity-building activities, and future studies to strengthen KM in the WHO European region particularly in Eastern Europe and Central Asia. An updated over-arching strategy to coordinate KM activities in the WHO European region will be useful in these efforts.
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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 | 0.093 | 0.257 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.048 | 0.042 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".