Moving knowledge into action for more effective practice, programmes and policy: protocol for a research programme on integrated knowledge translation
Bibliographic record
Abstract
BACKGROUND: Health research is conducted with the expectation that it advances knowledge and eventually translates into improved health systems and population health. However, research findings are often caught in the know-do gap: they are not acted upon in a timely way or not applied at all. Integrated knowledge translation (IKT) is advanced as a way to increase the relevance, applicability and impact of research. With IKT, knowledge users work with researchers throughout the research process, starting with identification of the research question. Knowledge users represent those who would be able to use research results to inform their decisions (e.g. clinicians, managers, policy makers, patients/families and others). Stakeholders are increasingly interested in the idea that IKT generates greater and faster societal impact. Stakeholders are all those who are interested in the use of research results but may not necessarily use them for their own decision-making (e.g. governments, funders, researchers, health system managers and policy makers, patients and clinicians). Although IKT is broadly accepted, the actual research supporting it is limited and there is uncertainty about how best to conduct and support IKT. This paper presents a protocol for a programme of research testing the assumption that engaging the users of research in phases of its production leads to (a) greater appreciation of and capacity to use research; (b) the production of more relevant, useful and applicable research that results in greater impact; and (c) conditions under which it is more likely that research results will influence policy, managerial and clinical decision-making. METHODS: The research programme will adopt an interdisciplinary, international, cross-sector approach, using multiple and mixed methods to reflect the complex and social nature of research partnerships. We will use ongoing and future natural IKT experiments as multiple cases to study IKT in depth, and we will take advantage of the team's existing relationships with provincial, national and international organizations. Case studies will be retrospective and prospective, and the 7-year grant period will enable longitudinal studies. The initiation of partnerships, funding processes, the research lifecycle and then outcomes/impacts post project will be studied in real time. These living laboratories will also allow testing of strategies to improve the efficiency and effectiveness of the IKT approach. DISCUSSION: This is the first interdisciplinary, systematic and programmatic research study on IKT. The research will provide scientific evidence on how to reliably and validly measure collaborative research partnerships and their impacts. The proposed research will build the science base for IKT, assess its relationship with research use and identify best practices and appropriate conditions for conducting IKT to achieve the greatest impact. It will also train and mentor the next generation of IKT researchers.
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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 | 0.246 | 0.278 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.017 | 0.023 |
| Insufficient payload (model declined to judge) | 0.123 | 0.047 |
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".