Demystifying knowledge translation: learning from the community
Bibliographic record
Abstract
OBJECTIVES: While there is increasing interest in research related to so-called Knowledge Translation, much of this research is undertaken from the perspective of researchers. The objective of this paper is to explore, through the participatory evaluation of Manitoba's The Need to Know Project, the characteristics of effective knowledge translation initiatives from the perspective of community partners. METHODS: The multi-method evaluation adopted a utilization-focused approach, where stakeholders participated in identifying evaluation questions, and methods were made transparent to participants. Over 100 open-ended, semi-structured interviews were conducted with project stakeholders over the first three years of the project. These interviews explored the perspectives of participants on all aspects of project development. Formal feedback processes allowed further refinement of emerging theory. RESULTS: This research suggests that there has been insufficient emphasis on personal factors in knowledge translation. The themes of 'quality of relationships' and 'trust' connected many different components of knowledge translation, and were essential for collaborative research. Organizational barriers and lack of confidence in researchers present greater challenges to knowledge translation than individual interest or community capacity. The costs of participation in collaborative research for community partners and the benefits for researchers, also require greater attention. CONCLUSIONS: Participation of community partners in The Need to Know Project has provided unique perspectives on knowledge translation theory. It has identified limitations to the common interpretations of knowledge translation principles and highlighted the characteristics of collaborative research initiatives that are of greatest importance to community partners.
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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 | 0.156 | 0.194 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.012 | 0.027 |
| Scholarly communication | 0.021 | 0.028 |
| Open science | 0.005 | 0.029 |
| Research integrity | 0.004 | 0.006 |
| 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; 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".