Indigenous Identity and Household Food Insecurity are Associated with Poor Health Outcomes in Canada
Bibliographic record
Abstract
Purpose: To examine whether Indigenous identity and food insecurity combined were associated with self-reported poor health. Methods: Data from the 2015–2016 Canadian Community Health Survey and multiple logistic regression were employed to evaluate the association between Indigenous identity, household food insecurity, and health outcomes, adjusted for individual and household covariates. The Alexander Research Committee in Alexander First Nation (Treaty 6) reviewed the manuscript and commented on the interpretation of study findings. Results: Data were from 59082 adults (3756 Indigenous). The prevalence of household food insecurity was 26.3% for Indigenous adults and 9.8% for non-Indigenous adults (weighted to the Canadian population). Food-secure Indigenous adults, food-insecure non-Indigenous adults, and food-insecure Indigenous adults had significantly (p < 0.001) greater odds of poor health outcomes than food-secure non-Indigenous adults (referent group). Food-insecure Indigenous adults had 1.96 [95% CI:1.53,2.52], 3.73 [95% CI: 2.95,4.72], 3.00 [95% CI:2.37,3.79], and 3.94 [95% CI:3.02,5.14] greater odds of a chronic health condition, a chronic mental health disorder, poor general health, and poor mental health, respectively, compared to food-secure non-Indigenous adults. Conclusions: Health policy decisions and programs should focus on food security initiatives for all Canadians, including addressing the unique challenges of Indigenous communities, irrespective of their food security status.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".