Building knowledge integration systems for evidence‐informed decisions
Bibliographic record
Abstract
PURPOSE: This paper aims to describe methods and models designed to build a comprehensive, integrative framework to guide the research to policy and practice cycle in health care. DESIGN/METHODOLOGY/APPROACH: Current models of science are summarised, identifying specific challenges they create for knowledge to action (KTA). Alternative models for KTA are outlined to illustrate how researchers and decision makers can work together to fit the KTA model to specific problems and contexts. The Canadian experience with the evolving paradigm shift is described, along with recent initiatives to develop platforms and tools that support the new thinking. Recent projects to develop and refine methods for embedded research are described. The paper concludes with a summary of lessons learned and recommendations that will move the KTA field towards an integrated science. FINDINGS: Conceptual models for KTA are advancing, benefiting from advances in team science, development of logic models that address the realities of complex adaptive systems, and new methods to more rapidly deliver knowledge syntheses more useful to decision and policy makers. PRACTICAL IMPLICATIONS: KTA is more likely when co-produced by researchers, practitioners, and policy makers. Closer collaboration requires shifts in thinking about the ways we work, capacity development, and greater learning from practice. ORIGINALITY/VALUE: More powerful ways of thinking about the complexities of knowledge to action are provided, along with examples of tools and priorities drawn from systems thinking.
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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 | 0.102 | 0.134 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.018 | 0.013 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.026 | 0.038 |
| Open science | 0.007 | 0.021 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".