Food insecurity and its association with multiple health outcomes among Indigenous peoples in Canada: the buffering role of culture-based resources
Bibliographic record
Abstract
OBJECTIVES: Despite growing interest in the health disparities associated with food insecurity, research focusing on Indigenous peoples has been limited, especially in studies using nationally representative samples. This study investigates the association between food insecurity and various health outcomes - self-rated general and mental health, chronic health conditions, suicidal ideation, and obesity - among Indigenous peoples in Canada. It also explores the potential moderating effects of culture-based resources, which include cultural identity affect, cultural group belonging, cultural engagement, and cultural exploration. DESIGN: The study utilized data from the 2017 Aboriginal Peoples Survey, a nationally representative sample of First Nations individuals living off-reserve, Métis, and Inuit across Canada (N = 15,533). Logistic regression models were used to analyze the data. RESULTS: Food insecurity was negatively associated with all examined health outcomes. Culture-based resources demonstrated a mixture of anticipated and unexpected effects on these relationships. Consistent with the stress process model, cultural group belonging mitigated the negative impact of food insecurity on all health outcomes. A similar pattern was observed for cultural engagement. However, contrary to expectations from the stress-buffering perspective, little evidence was found to support the moderating effects of cultural identity affect and cultural exploration. CONCLUSION: The results underscore the detrimental effects of food insecurity on the health of Indigenous peoples in Canada and suggest that culture-based resources, particularly cultural group belonging, play a crucial role in mitigating health disparities.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".