Extending collaborations for knowledge translation: lessons from the community-based participatory research literature
Bibliographic record
Abstract
The purpose of this paper is to expand the current focus on researcher–decision maker knowledge translation (KT) partnerships to include community partners. Lessons were drawn from the community-based participatory research literature. An inductive thematic analysis was conducted, using 42 eligible articles, and resulted in the identification of four themes (principles, structure, process and relationships) and associated factors that could contribute to KT collaborations among the three groups of actors. These findings are presented in a KT Triad framework. Thus, the framework provides specific lessons to facilitate researcher– decision maker–community collaborations based on an established body of literature. Including community partners in the KT process is important for integrating community context and needs into research-to-policy deliberations.
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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 | 0.313 | 0.274 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.021 | 0.046 |
| Scholarly communication | 0.025 | 0.042 |
| Open science | 0.009 | 0.041 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".