Does social capital interact with economic hardships in influencing older adults’ health? A study from China
Bibliographic record
Abstract
BACKGROUND: The importance of social and economic capital as predictors of health is widely documented, yet the complexity of interactions between them and effects on older people's health is still unclear. Combining the material and psychosocial explanations of health, this study explores the potential interactions between social and economic capital in influencing older adults' health in urban and rural China. METHODS: Using data from the China Family Panel Survey, physical and mental health in 2018 were regressed on social and economic capital indicators in 2016, controlling for sociodemographic characteristics of 3535 respondents aged 65 and older. Rothman's synergy index was calculated to investigate potential interaction effects. RESULTS: Economic hardships were significantly related to both self-reported health and mental health. Neighborhood cohesion and social participation were significantly associated with mental health for all, bonding trust was significantly associated with mental health for urban older people. We found no significant associations between social capital components and self-reported health. There was an interaction effect between low neighborhood cohesion and economic hardships, and between low social participation and economic hardships, creating an increased burden of poor mental health. The interaction effect between low bonding trust and economic hardships on mental health was apparent only among urban older people. CONCLUSIONS: Geographical settings are important factors in the complexity between social and economic capital in affecting older health. Intervention efforts directed towards reducing simultaneously multiple dimensions of deprivation, such as poverty, social exclusion, social isolation, could be helpful in improving older people's health. In materially deprived places, policies to promote health equity by improving social capital but without eliminating poverty may be less effective.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".