Dietary Patterns of Off-Reserve Indigenous Peoples in Canada and Their Association with Chronic Conditions
Bibliographic record
Abstract
Nationally representative nutrition surveys (Canadian Community Health Survey (CCHS) Cycle 2.2, Nutrition 2004 and 2015) were used to examine dietary patterns and their association with socioeconomic/sociodemographic factors and chronic conditions in off-reserve Indigenous population in Canada. A cluster analysis was used to identify dietary patterns (DPs), and the Nutrient Rich Food Index (NRF 9.3) was used as the diet quality score and stratified by age/gender groups. In 2004 (n = 1528), the dominant DPs among Indigenous adults (age = 41 ± 2.3) were “Mixed” (mean NRF = 450 ± 12) and “Unhealthy” among men (mean NRF = 426 ± 18), “Fruits” among women (mean NRF = 526 ± 29), and “High-Fat/High-Sugar” among children (age = 10.2 ± 0.5) (mean NRF = 457 ± 12). In 2015 (n = 950), the dominant DPs were “Unhealthy” (mean NRF = 466 ± 6), “Mixed” (mean NRF = 485 ± 21), Healthy-Like (mean NRF = 568 ± 37), and “Mixed” (mean NRF = 510 ± 9) among adults (age = 45.6 ± 2.2), men, women, and children (age = 10.9 ± 0.3) respectively. The majority of Indigenous peoples had the “Unhealthy” DP with a low diet quality, which may contribute to a high prevalence of obesity and chronic diseases. The income level and smoking status among adults and physical inactivity among children were recognized as important factors that may be associated with the dietary intake of off-reserve Indigenous population.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".