Exploring the relationships between nutrition and brain health among Indigenous Peoples in North America: a systematic review
Bibliographic record
Abstract
OBJECTIVES: This systematic review synthesizes extant literature that examines relationships between nutrition and brain health in Indigenous populations in North America and further assesses the extent to which Indigenous research paradigms and community engagement processes have been employed. METHODS: We searched five databases for primary research studies that examined indices of diet/nutrients in relation to brain health and focused on Indigenous populations in North America. Quality appraisal was performed using the Aboriginal and Torres Strait Islander Quality Appraisal Tool as well as the Appraisal Tool for Cross-Sectional Studies, the JBI Critical Appraisal Checklist for Case Control Studies, or the Critical Appraisal Skills Programme Cohort Study Checklist. SYNTHESIS: Of the 564 articles identified in the search, 16 met inclusion criteria. Ten studies focused on Inuit populations, 2 focused on the residents of Grassy Narrows First Nation, 2 focused on Cree populations, and 2 focused on Quileute, Makah, and Quinault First Nations populations. Fourteen studies reported deleterious effects of dietary contaminants (e.g., mercury, lead, polychlorinated biphenyls) on brain health outcomes and three studies reported beneficial effects of omega-3 polyunsaturated fatty acids on brain health outcomes. CONCLUSION: Findings of this review highlight the neurotoxic effects of environmental contaminants and the beneficial effects of omega-3 polyunsaturated fatty acids in traditional Indigenous food sources on a range of brain health outcomes. However, given the methodological limitations of the studies reviewed and the lack of community-based research that employs Indigenous research paradigms, results should be interpreted with caution. There is a clear need for strength-based research that examines the positive effects of nutrients within traditional Indigenous foods on brain health.
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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 | 0.008 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".