Tobacco smoking in urban neighborhoods: Exploring social capital as a protective factor in Santiago, Chile
Bibliographic record
Abstract
INTRODUCTION: Research examining the relationship between social capital and health in Latin America has been limited. The aim of this study is to evaluate the association between social capital and tobacco use in four low-income neighborhoods in Santiago, Chile. METHODS: A multistage probability sample was used to select households in 4 of the 10 poorest neighborhoods in the district of Puente Alto, in Santiago, Chile. A cross-sectional survey of 781 participants (81.2% response rate for households) included sociodemographic variables, questions pertaining to neighborhood social capital, and questions pertaining to tobacco. Main analyses were carried out at the individual level by performing a multiple logistic regression of individual tobacco use on individual perceptions of community social capital. RESULTS: The prevalence of smoking was 43.9% of the surveyed population. A five-factor structure for social capital was identified, including "perceived trust in neighbors," "perceived trust in organizations," "reciprocity within the neighborhood," "neighborhood integration," and "social participation." An inverse relationship between trust in neighbors and tobacco smoking was statistically significantly with an adjusted odds ratio of 0.95 (95% CI: 0.91-0.99). Trust in neighbors was also significantly inversely associated with the number of cigarettes smoked. DISCUSSION: Tobacco control remains a significant challenge in global health, requiring innovative strategies that address changing social contexts as well as the changing epidemiological profile of developing regions.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".