Social Capital and Mental Health: Results from a Cross-Sectional Study in Bangladesh
Bibliographic record
Abstract
This paper examines the relationship between social capital and mental health of the aging people in Bangladesh. A cross-sectional study was conducted in Madhabdi municipality and data were collected through face to face interview among the aging people. Mental health was measured by using General Health Questionnaire (GHQ-12). Bivariate analysis such as cross tabulation was applied to presentation of the data and chi-square test was applied to test the association between social capital dimensions and mental health. The chi-square test showed that all dimensions of social capital were related to mental health. Binary logistic regression model was applied to measure the effects of social capital on mental health. The results showed that the aging people who had low neighborhood cohesion ,low social networks ,low norms of reciprocity and low social trust were 1.967 (0.999-3.874), 1.909 (1.015-3.587), 2.302 (1.288-4.113) and 1.705 (.928-3.132) times more likely to say that they have poor mental health status. So, this study reveals that social capital was associated with mental health of the aging people in Bangladesh.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".