Applying knowledge to generate action: A community-based knowledge translation framework
Bibliographic record
Abstract
INTRODUCTION: Practical strategies are needed to translate research knowledge between researchers and users into action. For effective translation to occur, researchers and users should partner during the research process, recognizing the impact that knowledge, when translated into practice, will have on those most affected by that research. METHOD: Participatory action research (PAR) was used to generate a rural community's knowledge of their children's health. The Ottawa Model of Research Use (OMRU), a knowledge translation framework, was used to guide the translation of that generative knowledge into action, and the more current knowledge-to-action (KTA) conceptual framework provided the rationale for the graphical depiction of engagement of a rural community in knowledge translation. RESULTS: The definitions, perspectives, best practices, and existing frameworks of knowledge translation are outlined. The foundational underpinnings and elements of PAR, the OMRU, and KTA are linked to form a conceptual framework for knowledge translation in a rural community context. Select strategies noted in OMRU to translate existing knowledge informed aspects of PAR to generate an action. DISCUSSION: Diverse yet complementary approaches could be used by health professionals to advance the theory, method, and research of knowledge translation and exchange, regardless of context. Knowledge needs to be relevant, appropriate, applicable, timely, and reasonable to influence change.
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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 | 0.064 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.008 | 0.038 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".