The relationship between social capital and self-rated health: a multilevel analysis based on a poverty alleviation program in the Philippines
Bibliographic record
Abstract
BACKGROUND: Poor health is both a cause and consequence of poverty, and there is a growing body of evidence suggesting that social capital is an important factor for improving health in resource-poor settings. International Care Ministries (ICM) is a non-governmental organization in the Philippines that provides a poverty alleviation program called Transform. A core aim of the program is to foster social connectedness and to create a network of support within each community, primarily through consistent community-led small group discussions. The purpose of this research was to investigate the relationship between social capital and self-rated health and how ICM's Transform program may have facilitated changes in those relationships. METHODS: Three types of social capital were explored: bonding-structural, bridging-structural and cognitive. Using cross-sectional data collected before and after Transform, multilevel modelling was used to examine their effects on self-rated health between the two time points. RESULTS: The analyses showed that while social capital had minimal effects on self-rated health before Transform, a series of associations were identified after the program. Evidence of interdependence between the different types of social capital was also observed: bonding social capital only had a beneficial effect on self-rated health in the presence of bridging social capital, but we found that there was a 17 percentage point increase in self-rated health when individuals possessed all possible bridging and bonding relationships. At the same time, our estimates showed that maximising all forms of social capital is not necessarily constructive, as the positive effect of cognitive social capital on self-rated health was weaker at higher levels of bridging social capital. CONCLUSIONS: The results from this study has shown that building social capital can influence the way people perceive their own health, which can be facilitated by intervention programs which seek to create bonding and bridging relationships. Transform's intentional design to learn in community could be relevant to program planners as they develop and evaluate community-based programs, making adaptations as necessary to achieve organisation-specific goals while acknowledging the potential for varied effects when applied in different contexts or circumstances.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".