Bonding social capital and health within four First Nations communities in Canada: A cross-sectional study
Bibliographic record
Abstract
To date, research on social capital in Indigenous contexts has been scarce. In this quantitative study, our objectives were to (1): Describe bonding social capital within four distinct First Nations communities in Canada, and (2) Explore the associations between bonding social capital and self-rated health in these communities. With community permission, cross-sectional data were drawn from the Canadian Alliance for Healthy Hearts and Minds study. Four reserve-based First Nations communities were included in the analysis, totaling 591 participants. Descriptive statistics were computed to examine levels of social capital among communities and logistic regression analyses were performed to identify social capital predictors of good self-rated health. Age, sex, education level, and community were controlled for in all models. Across the four communities in this study, areas of common social capital included frequent socialization among friends and large and interconnected family networks. Positive self-rated health was associated with civic engagement at federal or provincial levels (OR=1.65, p<0.05) and organizational membership (OR=1.60, p<0.05), but overall, sociodemographic variables were more significantly associated with self-rated health than social capital variables. Significant differences in social capital were found across the four communities and community of residence was a significant health outcomes predictor in all logistic regression models. In conclusion, this study represents one of the first efforts to quantitatively study First Nations social capital with respect to health in Canada. The results reflect significant differences in the social capital landscape across different First Nations communities and suggest the need for social capital measurement tools that may be adapted to unique Indigenous contexts. Further, the impact of social capital on health may be better explored and interpreted with more community-specific instruments and with supplementary qualitative inquiry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".