The relation between number of smoking friends, and quit intentions, attempts, and success: Findings from the International Tobacco Control (ITC) Four Country Survey.
Bibliographic record
Abstract
Smokers who inhabit social contexts with a greater number of smokers may be exposed to more positive norms toward smoking and more cues to smoke. This study examines the relation between number of smoking friends and changes in number of smoking friends, and smoking cessation outcomes. Data were drawn from Wave 1 (2002) and Wave 2 (2003) of the International Tobacco Control (ITC) Project Four Country Survey, a longitudinal cohort survey of nationally representative samples of adult smokers in Australia, Canada, United Kingdom, and United States (N = 6,321). Smokers with fewer smoking friends at Wave 1 were more likely to intend to quit at Wave 1 and were more likely to succeed in their attempts to quit at Wave 2. Compared with smokers who experienced no change in their number of smoking friends, smokers who lost smoking friends were more likely to intend to quit at Wave 2, attempt to quit between Wave 1 and Wave 2, and succeed in their quit attempts at Wave 2. Smokers who inhabit social contexts with a greater number of smokers may be less likely to successfully quit. Quitting may be particularly unlikely among smokers who do not experience a loss in the number of smokers in their social context.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".