Predictors of household food insecurity and relationship with obesity in First Nations communities in British Columbia, Manitoba, Alberta and Ontario
Bibliographic record
Abstract
OBJECTIVE: To further understandings of household food insecurity in First Nations communities in Canada and its relationship with obesity. DESIGN: Analysis of a cross-sectional dataset from the First Nations Food, Nutrition and Environment Study representative of First Nations communities south of the 60th parallel. Multivariate logistic regression was used to assess associations between food insecurity and sociodemographic factors, as well as the odds of obesity among food-insecure households adjusting for sociodemographic characteristics. SETTING: Western and Central Canada. PARTICIPANTS: First Nations peoples aged ≥19 years. RESULTS: Forty-six percent of First Nations households experienced food insecurity. Food insecurity was highest for respondents who received social assistance; had ≤10 years of education; were female; had children in the household; were 19-30 years old; resided in Alberta; and had no year-round road access into the community. Rates of obesity were highest for respondents residing in marginally food-insecure households (female 56·6 %; male 54·6 %). In gender-specific analyses, the odds of obesity were highest among marginally food-insecure households in comparison with food-secure households, for both female (OR 1·57) and male (OR 1·57) respondents, adjusting for sociodemographic variables. For males only, those in severely food-insecure (compared with food-secure) households had lower odds of obesity after adjusting for confounding (OR 0·56). CONCLUSIONS: The interrelated challenges of food insecurity and obesity in First Nations communities emphasise the need for Indigenous-led, culturally appropriate and food sovereign approaches to food security and nutrition in support of holistic wellness and prevention of chronic disease.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".