Social capital, SES and health: an individual-level analysis . Soc Sci Med
Bibliographic record
Abstract
Stimulated by the finding (Kawachi et al., 1997) that social capital in communities may mediate the relationship between income inequality and health status, this article describes relationships between individual-level elements of social capital — trust, commitment and identity in the social-psychological dimension; participation in clubs and associations and civic participation in the action dimension — and self-rated health status, before and after controlling for human capital (socioeconomic status measured by income and education), using survey data collected in Saskatchewan, Canada (n = 534, 40 % response rate). Income (P = 0.001) and education (P< 0.001) were related to health in the expected directions. Both income (P = 0.002) and education (P = 0.004) were related to health among the elderly; education (P = 0.035) to health among the middle-aged; and neither among the youthful respondents. Frequency of socialization with work-mates (P = 0.019) and attendance at religious services (P = 0.034) had the strongest (and positive) relationships with health of the social engagement questions, even after controlling for human capital, and participation in clubs and associations was positively related to health among the elderly (P = 0.009). But for commitment to one’s own personal happiness (P = 0.039), trust, commitment and identification of various kinds were not significantly related to health. Civic participation was also unrelated to health. The main conclusion is that little evidence was found for compositional eects of social capital on health. Secondary findings are that the relationship between SES and health was the same for men and women and strongest among the elderly; that socialization with colleagues from work is relevant and that attendance at religious
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".