Understanding how and under what circumstances integrated knowledge translation works for people engaged in collaborative research: metasynthesis of IKTRN casebooks
Bibliographic record
Abstract
INTRODUCTION AND AIMS: Interaction and collaboration between researchers, patients/public, clinicians, managers and policy-makers are necessary to enhance the relevance and use of research, improve planning, and optimize healthcare delivery and outcomes. The Integrated Knowledge Translation Research Network (IKTRN) published four casebooks from 2019 to 2021, describing varied approaches to research co-production. Our aim was to examine the case studies to extend existing theoretical and empirical perspectives about how co-production works. METHODS: We used metasynthesis, a qualitative research design that includes seven iterative steps (clarify the purpose, delineate the case studies included, extract and code the data, derive themes from the coded data, determine the relationships of the themes to research co-production, synthesize the concepts, and build theory). RESULTS: A total of 35 cases was reviewed. The aggregate findings of this metasynthesis identified multiple contextual and process factors, barriers, and facilitators that influence integrated knowledge translation (IKT), and a range of IKT activities that increased the likelihood of success of co-production during research. In comparing the findings from the metasynthesis with existing literature, we found a number of consistencies, but also new information about barriers, facilitators, IKT activities and outcomes, thereby adding to our understanding about factors that influence co-production. CONCLUSIONS: This metasynthesis provided concrete examples to optimize co-produced clinical and health system research. More research is needed to fully understand how to overcome some challenging modifiable barriers, establish relationships, facilitate communication, overcome power differentials and create processes for knowledge-users working across boundaries (clinical practice and research) to stay engaged and participate fully in research endeavours.
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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 | 0.208 | 0.297 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.042 | 0.028 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.018 | 0.023 |
| Open science | 0.007 | 0.014 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".