Social Support and Thriving Health: A New Approach to Understanding the Health of Indigenous Canadians
Bibliographic record
Abstract
OBJECTIVES: We examined the importance of social support in promoting thriving health among indigenous Canadians, a disadvantaged population. METHODS: We categorized the self-reported health status of 31625 adult indigenous Canadians as thriving (excellent, very good) or nonthriving (good, fair, poor). We measured social support with indices of positive interaction, emotional support, tangible support, and affection and intimacy. We used multivariable logistic regression analyses to estimate odds of reporting thriving health, using social support as the key independent variable, and we controlled for educational attainment and labor force status. RESULTS: Compared with women reporting low levels of social support, those reporting high levels of positive interaction (odds ratio [OR]=1.4; 95% confidence interval [CI]=1.2, 1.6), emotional support (OR=2.1; 95% CI=1.8, 2.4), and tangible support (OR = 1.4; 95% CI = 1.2, 1.5) were significantly more likely to report thriving health. Among men, only emotional support was significantly related to thriving health (OR=1.7; 95% CI=1.5, 1.9). Thriving health status was also significantly mediated by age, aboriginal status (First Nations, Métis, or Inuit), educational attainment, and labor force status. CONCLUSIONS: Social support is a strong determinant of thriving health, particularly among women. Research that emphasizes thriving represents a positive and necessary turn in the indigenous health discourse.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".