Beyond the European Union Tobacco Products Directive: smokers’ and recent quitters’ support for further tobacco control measures (2016–2018)
Bibliographic record
Abstract
BACKGROUND: Several measures recommended by the WHO Framework Convention on Tobacco Control have not been implemented in the European Union, despite changes in the legislation such as the Tobacco Products Directive (TPD). This study aims to understand smokers' and recent quitters' levels of support for tobacco control measures that go beyond the TPD during and after its implementation. METHODS: Data from wave 1 (2016, n=6011) and wave 2 (2018, n=6027) of the EUREST-PLUS International Tobacco Control Policy Evaluation Project Six European Countries Survey, a cohort of adult smokers in Germany, Greece, Hungary, Poland, Romania, Spain were used to estimate the level of support for seven different tobacco control measures, overall and by country. RESULTS: In 2018, the highest support was for implementing measures to further regulate tobacco products (50.5%) and for holding tobacco companies accountable for the harm caused by smoking (48.8%). Additionally, in 2018, 40% of smokers and recent quitters supported a total ban on cigarettes and other tobacco products within ten years, if assistance to quit smoking is provided. Overall, support for tobacco control measures among smokers and recent quitters after the implementation of the TPD remained stable over time. CONCLUSION: There is considerable support among smokers and recent quitters for tobacco control measures that go beyond the current measures implemented. A significant percentage of smokers would support a ban on tobacco products in the future if the government provided assistance to quit smoking. This highlights the importance of implementing measures to increase smoking cessation in conjunction with other policies.
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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 | 0.004 | 0.008 |
| 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 | 0.002 |
| 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".