Integrated knowledge translation: digging deeper, moving forward
Bibliographic record
Abstract
BACKGROUND: Integrated knowledge translation has risen in popularity as a solution to the underuse of research in policy and practice settings. It engages knowledge users-policymakers, practitioners, patients/consumers or their advocates, and members of the wider public-in mutually beneficial research that can involve the joint development of research questions, data collection, analysis and dissemination of findings. Knowledge that is co-produced has a better chance of being implemented. DISCUSSION: The purpose of this paper is to update developments in the field of integrated knowledge translation through a deeper analysis of the approach in practice-oriented and policy-oriented health research. We present collaborative models that fall outside the scope of integrated knowledge translation, but then explore consensus-based approaches and networks as alternate sites of knowledge co-production. We discuss the need to advance the field through the development, or use, of data collection and interpretation tools that creatively engage knowledge users in the research process. Most importantly, conceptually relevant outcomes need to be identified, including ones that focus on team transformation through the co-production of knowledge. CONCLUSIONS: We explore some of these challenges and benefits in detail to help researchers understand what integrated knowledge translation means, and whether the approach's potential added value is worth the investment of time, energy and other resources.
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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 | 0.082 | 0.100 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.015 | 0.038 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.011 | 0.016 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".