Nutritional interventions for Indigenous adults in Canada - opportunities to sustain health and cultural practices: a scoping review
Bibliographic record
Abstract
Indigenous People in Canada possess rich cultural traditions, intertwined with a strong connection to nature. However, colonisation and contemporary challenges have given rise to changes in lifestyle and culture, resulting in health and nutrition disparities within these communities. The goal of this review was to explore the available literature of existing Indigenous nutrition programs for adults in Canada. Arksey and O'Malley's scoping review protocol was used to conduct the search between July 2020 and February 2023. Articles were obtained from MEDLINE (Ovid), PsycInfo, Embase (Ovid), CINAHL (EBSCO), Web of Science, Scopus (Elsevier), Canadian Business and Current Affairs (Proquest), and Google Scholar. We identified 24 publications, with 19 being unique interventions. Common themes among programs included integrating traditional foods and cultural values, adapted programming to local needs, empowering community members, using a multidisciplinary collaboration, and leveraging social activities, all of which highlight the need for holistic strategies amid complex historical, social, and environmental factors. Overall, this review emphasises the need for continued support and development of Indigenous-led nutritional initiatives to promote health and well-being among Indigenous adults in Canada. Ensuring culturally relevant and sustainable solutions is crucial for addressing nutritional health disparities and fostering long-term positive outcomes.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".