Annual gatherings as an integrated knowledge translation strategy to support local and traditional food systems within and across Indigenous community contexts: a qualitative study
Bibliographic record
Abstract
Integrated knowledge translation (IKT) and community-based participatory research (CBPR) are recognized as effective approaches when Indigenous and non-Indigenous partners work together to focus on a common goal. The "Learning Circles: Local Healthy Food to School" (LC:LHF2S) study supported the development and implementation of Learning Circles (LC) in 4 Canadian Indigenous communities with the goal of improving local, community-based healthy food systems. Critical to the research process were annual gatherings (AG) where diverse stakeholders (researchers, Indigenous community members, and partners) visited each community to share knowledge, experiences, and provide support in the research process. Using a qualitative, descriptive method, this paper explores how the AG supported IKT across partners. Yearly interviews involving 19 total participants (with some participating multiple times across the 4 gatherings) elicited their AG experiences in supporting local LC:LHF2S. Three themes with multiple sub-themes were identified: (a) setting the stage for IKT (importance of in-person gatherings for building relationships across partners, learning from each other), (b) enabling meaningful engagement (aligning research with Indigenous values, addressing tensions and building trust over time, ensuring flexibility, and Indigenous involvement and leadership), and (c) supporting food system action at the local level (building local community engagement and understanding, and integrating support for implementation and scale-up of LC). This paper provides useful and practical examples of the principles of Indigenous-engaged IKT and CBPR in action in healthy, local, and traditional food initiatives. AG are a valuable IKT strategy to contribute to positive, transformative change and ethical research practice within Indigenous communities.
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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 | 0.021 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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; 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".