Social Capital as a Determinant of Self-Rated Health in Women of Reproductive Age: A Population-Based Study
Bibliographic record
Abstract
INTRODUCTION: Recognition of the factors related to women's health is necessary. Evidence is available that the social structure including social capital plays an important role in the shaping people's health. The aim of the current study was to investigate the association between self-rated health and social capital in women of reproductive age. METHODS: This study is a population-based cross-sectional survey on 770 women of reproductive age, residing in any one of the 22 municipality areas across Tehran (capital of Iran) with the multi stage sampling technique. Self-rated health (Dependent variable), social capital (Independent variable) and covariates were studied. Analysis of data was done by one-way ANOVA test and multiple linear regressions. RESULTS: Depending on logistic regression analyses, the significant associations were found between self-rated health and age, educational level, crowding index, sufficiency of income for expenses and social cohesion. Data show that women with higher score in social cohesion as an outcome dimension of social capital have better self-rated health (PV = 0.001). CONCLUSION: Given the findings of this study, the dimensions of social capital manifestations (groups and networks, trust and solidarity, collective action and cooperation) can potentially lead to the dimensions of social capital outcomes (social cohesion and inclusion, and empowerment and political action). Following that, social cohesion as a dimension of social capital outcomes has positively relationship with self- rated health after controlling covariates. Therefore, it is required to focus on the social capital role on health promotion and health policies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".