Tobacco policies and changes in the tendency of smoking cessation in cigarette users in Chile: a longitudinal cross-sectional study
Bibliographic record
Abstract
OBJECTIVE: To assess the impact of tobacco control regulations and policy implementation on smoking cessation tendencies in cigarette users born between 1982 and 1991 in Chile. DESIGN: Longitudinal cross-sectional study. SETTING: National level. PARTICIPANTS: Data from the National Survey of Drug Consumption (Service of Prevention and Rehabilitation for Drug and Alcohol Consumption). A pseudo-cohort of smokers born between 1982 and 1991 (N=17 905) was tracked from 2002 to 2016. PRIMARY AND SECONDARY OUTCOMES MEASURES: Primary outcome was the tendency to cease smoking conceptualised as the report of using cigarettes 1 month or more ago relative to using cigarettes in the last 30 days. The main exposure variable was the Tobacco Policy Index-tracking tobacco policy changes over time. Logistic regression, controlling for various factors, was applied. RESULTS: Models suggested a 14% increase in the smoking cessation tendency of individuals using cigarettes 1 month or more ago relative to those using cigarettes in the last 30 days (OR 1.14, CI 95% CI 1.10 to 1.19) for each point increment in the Tobacco Policy index. CONCLUSIONS: Our study contributes to documenting a positive impact of the implementation of interventions considered in the MPOWER strategy in the progression of smoking cessation tendencies in smokers born between 1982 and 1991 in Chile.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".