Social capital and health: implication for health promotion by lay citizens in Japan
Bibliographic record
Abstract
A non-profit organization was formed in 2009 by lay citizens of Nagahama, Japan in response to a community-based genome-epidemiologic study, the 'Nagahama Zero(0)-ji Prevention Cohort Project (N0PCP)'. This organization aims to promote health by taking advantage of citizens' social networks. The Ottawa Charter for Health Promotion affirms the importance of creating supportive environments and coordinating social relationships. Supportive environments (infrastructure) and social relationships (resources) work together as aspects of social capital. This study sought to examine the association between self-rated health and social capital, at both individual and neighborhood levels, and to discuss suitable health promotion strategies for local circumstances.A cross-sectional survey was conducted in 2011, using a self-administered postal questionnaire. Social capital indicators included aspects of support in the environment (social support, neighborhood connectedness, informal social controls, neighborhood trust, general trust, and attachment to place) and social relationships (number of activities; participation in neighborhood activities; participation in recreational activities; and social leverage regarding physical health, mental health, and acquisition of health information). Neighborhood-level social capital was calculated as the percentage of individuals in a neighborhood in the 'high social capital' category. At the individual level, participation in recreational activities, high general trust, and discussion regarding mental health problems with family members were associated with self-rated health positively, whereas discussion of mental health problems with acquaintances had a negative correlation. At the neighborhood level, a highly supportive environment did not contribute to good health, whereas aggregated attachment to place had a positive correlation. There were no significant inter-regional health differences.The results of this study suggest that health promotion activities should aim at promoting the formation of empathetic friendships through individual networks, based on bringing individuals who need support to compatible places. Attachment to place should be incorporated into activities as an important and effective tool.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".