“If you show them respect, you’re going to [get] respect back": a theory for engaging First Nations for knowledge translation within a national nutrition and health survey
Bibliographic record
Abstract
BACKGROUND: Knowledge translation (KT) research aims to bridge the gap between research results and application which is vital to addressing health inequities. Despite the increasing emphasis on engaging Indigenous communities in research, there is limited research examining how to effectively engage communities to achieve Indigenous KT. The Food, Environment, Health, and Nutrition of First Nations Children and Youth (FEHNCY) is a nationally representative survey collaborating with First Nations' (FNs) communities across Canada to inform policies and programs. The FEHNCY Community Engagement and Mobilization (CEM) supports partnerships with participating FNs communities and the application of study findings into action. This formative research aimed to examine how, for whom, and in which circumstances community engagement approaches support KT within FNs communities. METHODS: Data were generated with one rural and one semi-urban community participating in the FEHNCY pilot from the Atlantic and Eastern regions of Canada, respectively. A total of 26 in-depth interviews were conducted, 1 modified Talking Circle with community partners and 2 focus group discussions with the FEHNCY team. We used a realist approach combining inductive and deductive coding stages to develop a middle-range theory examining the connections between community engagement and KT. RESULTS: Our findings highlight the contexts, interventions, mechanisms, and outcomes that create pathways to KT. The participants described the societal, study and community contexts that affected engagement processes. The essential community engagement strategies included supporting Indigenous leadership in the research, supporting community decision-making, promoting project visibility, applying youth-specific engagement strategies, and incorporating FNs knowledges. The participants also described that centering positive relationships between research and community partners and valuing FNs knowledge systems were essential mechanisms for supporting KT. Lastly, participants highlighted KT outcomes namely, community self-determination in research, improved research findings and application of results for FNs benefit. CONCLUSION: This research can inform the strategic use of community engagement in research for KT among FNs. This study is the first to generate a middle-range theory using primary data collection for supporting KT through community engagement approaches in Indigenous health research.
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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 | 0.103 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.019 | 0.107 |
| Scholarly communication | 0.017 | 0.022 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".