Associations of health status and diabetes among First Nations Peoples living on-reserve in Canada
Bibliographic record
Abstract
OBJECTIVE: Our objective is to describe self-reported health status, prevalence of diabetes and obesity and their associations in participants from the First Nations Food, Nutrition and Environment Study (FNFNES) in order to identify possible correlates of health in First Nations adults. METHODS: parallel. Health and diabetes were self-reported, and prevalence of obesity was evaluated. Socio-demographic and lifestyle factors and traditional food (TF) activities were investigated for associations with health parameters. RESULTS: High prevalence rates of overweight/obesity (78-91%) and diabetes (19% age-standardized prevalence) were found. Smoking rates were high and physical activity was low. In multivariable analyses, obesity was associated with region, income source, age, gender, smoking and self-reported health; diabetes and lesser self-reported health were associated with obesity and lower education. Diabetes was strongly associated with lesser self-reported health and weakly associated with being a smoker. CONCLUSION: We have identified possible correlates of health in this population that can help to better understand the underlying concerns and identify solutions for First Nations and their partners. We urge governments and First Nations to address the systemic problems identified with a holistic ecosystem approach that takes into consideration the financial and physical access to food, particularly TF, and the facilitation of improved health behaviour. New mechanisms co-developed with First Nations leadership should focus on supporting sustainable, culturally safe and healthy lifestyles and closing the gaps in nutrition and food insecurity.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".