Defining Integrated Knowledge Translation and Moving Forward: A Response to Recent Commentaries
Bibliographic record
Abstract
ntegrated knowledge translation (IKT) is a model of collaborative research, where researchers work with knowledge users who identify a problem and have the authority to implement the research recommendations.Knowledge users have unique expertise pertaining to the research topic, including knowledge of the context and the potential for implementation.Researchers bring methodological and content expertise to the collaboration.Implicit in this approach is the sharing of power between researchers and knowledge users.Sometimes referred to as the co-production of knowledge, this new way of working suggests that the synergies derived from the collaboration will result in better science; more relevant and actionable research findings; increased use of the findings in policy or practice; and mutual learning.An evaluation of knowledge translation funding programs at the Canadian Institutes of Health Research demonstrated that researchers and knowledge users co-producing research were more likely to report improving the health of Canadians, creating more effective health services or products and strengthening the Canadian healthcare system than researchers who do not work with knowledge users. 1 Interest in IKT as a strategy for accelerating the uptake and impact of research is growing as demonstrated by the recent publication of articles and commentaries in this and other journals.[2][3][4][5] Rycroft-Malone et al 6 note that, while promising, the IKT approach is not without its challenges.The authors highlight issues of power, politics, and perceptions that require careful attention if an IKT approach is to be successful.Collaborations, they argue, require prompting and support.The authors also suggest that certain personable qualities, such as being tolerant of less structure whilst maintaining methodological standards, are required of individuals participating in IKT partnerships.A related commentary was offered by Cooke et al 7 in which they address the issue of power.In particular, these authors argue that the co-members
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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 | 0.180 | 0.430 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.016 | 0.030 |
| Scholarly communication | 0.029 | 0.048 |
| Open science | 0.011 | 0.029 |
| Research integrity | 0.089 | 0.120 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".