A Scoping Review of Obesity among Indigenous Peoples in Canada
Bibliographic record
Abstract
Indigenous populations in Canada are heavily affected by the burden of obesity, and certain communities, such as First Nations on reserve, are not included in the sampling framework of large national health surveys. A scoping review of ever published original research reporting obesity rates (body mass index ≥ 30), among adult Indigenous peoples in Canada, was conducted to identify studies that help close the Canadian Community Health Survey (CCHS) data gap for obesity prevalence in Indigenous populations in Canada and to make comparisons based on ethnicity, sex, time, and geography. First Nations on reserve with self-reported height and weight had higher rates of obesity (30%-51%) than First Nations off reserve (21%-42%) and non-Indigenous populations (12%-31%) in their respective province or territory, with the exception of Alberta, where rates in First Nations on reserve (30% and 36%) were lower or similar to those reported in First Nations off reserve (38%). First Nations on reserve with predominantly measured height and weight (42%-66%) had higher rates of obesity compared to Inuit in Quebec (28%), Nunavut (33%), and Newfoundland and Labrador (41%), while the rates were similar to those in Inuit in Northwest Territories (49%). Obesity in these large studies conducted among Inuit was based solely on measured height and weight. Studies in First Nations and Inuit alike showed higher prevalence of obesity in women, as well as an increase with time. No recent studies measured the obesity rates for First Nations in Yukon and Northwest Territories and for Métis living in settlements of Northern Alberta. Researchers are encouraged to conduct total diet studies in these regions, and to use existing data to analyze the associations between obesity, road access, latitude, food environment, and traditional food intake, to further inform community planning and development.
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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 | 0.010 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.026 | 0.035 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".