Bonding versus bridging social capital and their associations with self rated health: a multilevel analysis of 40 US communities
Bibliographic record
Abstract
STUDY OBJECTIVE: Few studies have distinguished between the effects of different forms of social capital on health. This study distinguished between the health effects of summary measures tapping into the constructs of community bonding and community bridging social capital. DESIGN: A multilevel logistic regression analysis of community bonding and community bridging social capital in relation to individual self rated fair/poor health. SETTING: 40 US communities. PARTICIPANTS: Within community samples of adults (n = 24 835), surveyed by telephone in 2000-2001. MAIN RESULTS: Adjusting for community sociodemographic and socioeconomic composition and community level income and age, the odds ratio of reporting fair or poor health was lower for each 1-standard deviation (SD) higher community bonding social capital (OR = 0.86; 95% = 0.80 to 0.92) and each 1-SD higher community bridging social capital (OR = 0.95; 95% CI = 0.88 to 1.02). The addition of indicators for individual level bonding and bridging social capital and social trust slightly attenuated the associations for community bonding social capital (OR = 0.90, 95% CI = 0.84 to 0.97) and community bridging social capital (OR = 0.96, 95% CI = 0.89 to 1.03). Individual level high formal bonding social capital, trust in members of one's race/ethnicity, and generalised social trust were each significantly and inversely related to fair/poor health. Furthermore, significant cross level interactions of community social capital with individual race/ethnicity were seen, including weaker inverse associations between community bonding social capital and fair/poor health among black persons compared with white persons. CONCLUSIONS: These results suggest modest protective effects of community bonding and community bridging social capital on health. Interventions and policies that leverage community bonding and bridging social capital might serve as means of population health improvement.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".