Knowledge to action for solving complex problems: insights from a review of nine international cases
Bibliographic record
Abstract
INTRODUCTION: Solving complex problems such as preventing chronic diseases introduces unique challenges for the creation and application of knowledge, or knowledge to action (KTA). KTA approaches that apply principles of systems thinking are thought to hold promise, but practical strategies for their application are not well understood. In this paper we report the results of a scan of systems approaches to KTA with a goal to identify how to optimize their implementation and impact. METHODS: A 5-person advisory group purposefully selected 9 initiatives to achieve diversity on issues addressed and organizational forms. Information on each case was gathered from documents and through telephone interviews with primary contacts within each organization. Following verification of case descriptions, an inductive analysis was conducted within and across cases. RESULTS: The cases revealed 5 guidelines for moving from conceiving KTA systems to implementing them: (1) establish and nurture relationships, (2) co-produce and curate knowledge, (3) create feedback loops, (4) frame as systems interventions rather than projects, and (5) consider variations across time and place. CONCLUSION: Results from the environmental scan are a modest start to translating systems concepts for KTA into practice. Use of the strategies revealed in the scan may improve KTA for solving complex public health problems. The strategies themselves will benefit from the development of a science that aims to understand adaptation and ongoing learning from policy and practice interventions, strengthens enduring relationships, and fills system gaps in addition to evidence gaps. Systems approaches to KTA will also benefit from robust evaluations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".