Impact of tobacco control policies on smoking prevalence and quit ratios in 27 European Union countries from 2006 to 2014
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<h3>Background</h3> Tobacco use is still highly prevalent in Europe, despite the tobacco control efforts made by the governments. The development of tobacco control policies varies substantially across countries. The Tobacco Control Scale (TCS) was introduced to quantify the implementation of tobacco control policies across European countries <h3>Objective</h3> To assess the midterm association of tobacco control policies on smoking prevalence and quit ratios among 27 European Union (EU) Member States (EU27). <h3>Methods</h3> Ecological study. We used the TCS in EU27 in 2007 and the prevalence of tobacco and quit ratios data from the Eurobarometer survey (2006 (n=27 585) and 2014 (n=26 793)). We analysed the relationship between the TCS scores and smoking prevalence and quit ratios and their relative changes (between 2006 and 2014) by means of scatter plots and multiple linear regression models. <h3>Results</h3> In EU27, countries with higher scores in the TCS, which indicates higher tobacco control efforts, have lower prevalence of smokers, higher quit ratios and higher relative decreases in their prevalence rates of smokers over the last decade. The correlation between TCS scores and smoking prevalence (r<sub>sp</sub>=–0.444; P=0.02) and between the relative changes in smoking prevalence (r<sub>sp</sub>=–0.415; P=0.03) was negative. A positive correlation was observed between TCS scores and quit ratios (r<sub>sp</sub>=0.373; P=0.06). The percentage of smoking prevalence explained by all TCS components was 28.9%. <h3>Conclusion</h3> EU27 should continue implementing comprehensive tobacco control policies as they are key for reducing the prevalence of smoking and an increase tobacco cessation rates in their population.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it