Community engagement in Indigenous food systems contamination studies: A systematic scoping review
Bibliographic record
Abstract
INTRODUCTION: Indigenous food systems are vital for maintaining cultural practices, physical and mental well-being, and community health. However, these systems are increasingly threatened by environmental contamination, exacerbating health disparities. Despite growing recognition of the importance of Indigenous knowledge in environmental health research, there is limited systematic evidence on how well community engagement is incorporated into studies investigating contamination of Indigenous food systems. This scoping review aims to assess reported practices for engaging Indigenous Peoples and the use of study results to support community-driven initiatives. METHODS: A systematic scoping review was conducted on peer-reviewed articles published between January 2010 and July 2024 that assessed contamination in Indigenous food systems with a human health dimension. The search included three databases: Web of Science, Scopus, and CAB Direct, yielding 2,203 articles. After applying inclusion and exclusion criteria, 202 studies were retained for final analysis. Data were extracted on study characteristics, community engagement strategies, Indigenous knowledge integration, and reported use of study results by Indigenous Peoples. The analysis was conducted using the PRISMA framework. RESULTS: Most studies (97%) employed quantitative methods, with fewer incorporating qualitative or mixed-method approaches. While community engagement was mentioned in about two-thirds of the studies, the depth of engagement varied significantly. A quarter of studies included Indigenous authors and only a small proportion reported meaningful collaboration with Indigenous Peoples throughout the research process. Studies with Indigenous authorship were more likely to report community engagement activities and utilization of results for broader community initiatives. CONCLUSION: The increasing recognition of Indigenous and traditional knowledge within academia must extend beyond intellectual discourse to address health disparities. Indigenous Peoples have long advocated for self-determination and engagement in research conducted in their communities. As part of broader reconciliation efforts with Indigenous Peoples the environmental health scientific community must reciprocate these efforts by integrating discussions into scientific literature about community participation and implementation of study results. This review highlights the need for robust and meaningful community engagement in environmental health research related to Indigenous food systems.
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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 | 0.064 | 0.200 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.028 | 0.027 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".