Navigating Knowledge to Action: A Conceptual Map for Facilitating Translation of Population Health Risk Planning Tools Into Practice
Bibliographic record
Abstract
A population health risk tool was created that estimates future diabetes risk and provides outputs that can inform practical and meaningful diabetes prevention strategies and support local decision making and planning. A project was designed to inform and understand knowledge translation and application of this novel tool in multiple health-related settings. Lacking published studies in this area, the authors conceived a conceptual map to guide the project that integrates and adapts elements from several planned action theories. This paper describes the rationale and basis for constructing the Population Health Planning Knowledge-to-Action Model and elaborates on the 2 connected structures of the framework: the Tool Creation Path and the Action Cycle. Although created for an express purpose, this model has the potential to inform application of other tools. This work demonstrates how a research team can adapt and integrate existing frameworks to better align with novel real-world knowledge translation issues. Furthermore, the integration of a population risk tool to support health decision making highlights the interaction between continuing education and knowledge translation.
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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 | 0.037 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.017 | 0.011 |
| Science and technology studies | 0.009 | 0.027 |
| Scholarly communication | 0.022 | 0.032 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 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".