Peas in a Pod: The Process of Mutual Learning in Knowledge Exchange on Health Promotion Interventions Research
Bibliographic record
Abstract
INTRODUCTION: Knowledge exchange (KE) in health promotion research encourages the outcome of mutual learning between researchers and knowledge users. Researcher-practitioner partnerships (RPP) are an approach in health promotion intended to cultivate mutually beneficial research between different disciplines and sectors. RPPs have been found to improve intervention success and sustainability, including the bidirectional sharing of sector-specific knowledge, but how mutual learning occurs remains unclear. This paper analyses two examples of mutual learning in health promotion RPPs, as told by the practitioners and researchers involved. METHODS: During the Peas in a Pod virtual practice exchange, researchers and practitioners working in retail food environments came together to discuss KE. The event included a fireside chat with two retailers sharing their experiences collaborating with researchers. The primary author took notes from the discussion and led the writing of the case, which each practitioner reviewed for accuracy. After the fireside chat, researchers and knowledge users held small group discussions which were analysed into three themes to explore mutual learning within retail food environment research. RESULTS: Example one was a retailer-led hospital retail merchandising intervention study at an urban public tertiary hospital in Nova Scotia, Canada. Example two was a healthy merchandising strategy trial with an Australian Aboriginal-owned and governed not-for-profit store corporation in the Northern Territory and Queensland, Australia. Mutual learning involved (1) partnerships with both near and far-sighted vision, (2) negotiation and meeting in the middle and (3) leveraging policies and strategies to support interventions. Overall, KE bridged both knowledge and action. CONCLUSION: This paper provides insight into how mutual learning occurs in health promotion research. Mutual learning within an RPP influenced research design and implementation. Our findings showed that knowledge exchange emerging through the intervention research also contributed to further changes in partnerships and policies. However, further study is required to understand how knowledge exchange and policies intersect. SO WHAT?: Mutual learning in knowledge exchange can contribute to health promotion intervention evidence and create pathways to new partnerships with retailers who are sceptical or hesitant about implementing retail interventions.
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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 | 0.097 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.015 | 0.057 |
| Scholarly communication | 0.026 | 0.036 |
| Open science | 0.004 | 0.055 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".