The impact of cigarette warning labels and smoke-free bylaws on smoking cessation: evidence from former smokers.
Bibliographic record
Abstract
BACKGROUND: To effectively address the health burden of tobacco use, tobacco control programs must find ways of motivating smokers to quit. The present study examined the extent to which former smokers' motivation to quit was influenced by two tobacco control policies recently introduced in the Waterloo Region: a local smoke-free bylaw and graphic cigarette warning labels. METHODS: A random digit-dial telephone survey was conducted with 191 former smokers in southwestern Ontario, Canada in October 2001. Former smokers who had quit in the previous three years rated the factors that influenced their decision to quit and helped them to remain abstinent. RESULTS: Thirty-six percent of former smokers cited smoke-free policies as a motivation to quit smoking. Former smokers who quit following the introduction of a total smoke-free bylaw were 3.06 (CI95 = 1.02-9.19) times more likely to cite smoking bylaws as a motivation to quit, compared to former smokers who quit prior to the bylaw. A total of 31% participants also reported that cigarette warning labels had motivated them to quit. Former smokers who quit following the introduction of the new graphic warning labels were 2.78 (CI9 = 1.20-5.94) times more likely to cite the warnings as a quitting influence than former smokers who quit prior to their introduction. Finally, 38% of all former smokers surveyed reported that smoke-free policies helped them remain abstinent and 27% reported that warning labels helped them do so. CONCLUSION: More stringent smoke-free and labelling policies were associated with a greater impact upon motivations to quit.
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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 | 0.008 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".