Strengthening population health interventions: developing the CollaboraKTion Framework for Community-Based Knowledge Translation
Bibliographic record
Abstract
BACKGROUND: Much of the research and theorising in the knowledge translation (KT) field has focused on clinical settings, providing little guidance to those working in community settings. In this study, we build on previous research in community-based KT by detailing the theory driven and empirically-informed CollaboraKTion framework. METHODS: A case study design and ethnographic methods were utilised to gain an in-depth understanding of the processes for conducting a community-based KT study as a means to distilling the CollaboraKTion framework. Drawing on extensive field notes describing fieldwork observations and interactions as well as evidence from the participatory research and KT literature, we detail the processes and steps undertaken in this community-based KT study as well as their rationale and the challenges encountered. In an effort to build upon existing knowledge, Kitson and colleagues' co-KT framework, which provides guidance for conducting KT aimed at addressing population-level health, was applied as a coding structure to inform the current analysis. This approach was selected because it (1) supported the application of an existing community-based KT framework to empirical data and (2) provided an opportunity to contribute to the theory and practice gaps in the community-based KT literature through an inductively derived empirical example. RESULTS: Analysis revealed that community-based KT is an iterative process that can be viewed as comprising five overarching processes: (1) contacting and connecting; (2) deepening understandings; (3) adapting and applying the knowledge base; (4) supporting and evaluating continued action; and (5) transitioning and embedding as well as several key elements within each of these processes (e.g. building on existing knowledge, establishing partnerships). These empirically informed theory advancements in KT and participatory research traditions are summarised in the CollaboraKTion framework. We suggest that community-based KT researchers place less emphasis on enhancing uptake of specific interventions and focus on collaboratively identifying and creating changes to the contextual factors that influence health outcomes. CONCLUSIONS: The CollaboraKTion framework can be used to guide the development, implementation and evaluation of contextually relevant, evidence-informed initiatives aimed at improving population health, amid providing a foundation to leverage future research and practice in this emergent KT area.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.058 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads 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".