Effect of Network Social Capital on the Chances of Smoking Relapse: A Two-Year Follow-up Study of Urban-Dwelling Adults
Bibliographic record
Abstract
OBJECTIVES: We sought to examine the prospective influence of social capital and social network ties on smoking relapse among adults. METHODS: In 2010, a 2-year follow-up study was conducted with the 2008 Montreal Neighborhood Networks and Healthy Aging Study (MoNNET-HA) participants. We asked participants in 2008 and 2010 whether they had smoked in the past 30 days. Position and name generators were used to collect data on social capital and social connections. We used multilevel logistic analysis adjusting for demographic and socioeconomic factors to predict smoking relapse in 2010. RESULTS: Of the 1400 MoNNET-HA follow-up participants, 1087 were nonsmokers in 2008. Among nonsmokers, 42 were smokers in 2010. Results revealed that participants with higher network social capital were less likely (odds ratio [OR] = 0.68; 95% confidence interval [CI] = 0.47, 0.96), whereas socially isolated participants (OR = 3.69; 95% CI = 1.36, 10.01) or those who had ties to smokers within the household (OR = 4.22; 95% CI = 1.52, 11.73) were more likely to report smoking in 2010. CONCLUSIONS: Social network capital reduced the chances of smoking relapse. Smoking cessation programs might aim to increase network diversity so as to prevent relapse.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".