Social capital and self-rated health: A cross-sectional study of the general social survey data comparing rural and urban adults in Ontario
Bibliographic record
Abstract
The concept of social capital shows great promise for its potential to influence individual and population health. Yet challenges persist in defining and measuring social capital, and little is known about the mechanisms that link social capital and health. This paper reports on the quantitative phase of a sequential explanatory mixed methods study using data from Canada's 2013 General Social Survey (data collected 2013-14). An exploratory factor analysis revealed six underlying dimensions of social capital for 7,187 adults living in Ontario, Canada. These factors included trust in people, neighbourhood social capital, trust in institutions, sense of belonging, civic engagement, and social network size. A logistic regression indicated that having high Trust in People and Trust in Institutions were associated with better mental health while high Trust in Institutions, Sense of Belonging, and Civic Engagement were associated with better physical health. When comparing rural and urban residents, there were no differences in their self-reported health, nor did social capital influence their health any differently, despite rural residents having higher social capital scores. The study findings are important for understanding the nature of social capital and how it influences health, and provide direction for targeted health promotion strategies.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".