Prevalence and correlates of different smoking bans in homes and cars among smokers in 6 Countries of the EUREST-PLUS ITC Europe Surveys
Bibliographic record
Abstract
INTRODUCTION: Second-hand smoke exposure has decreased in a number of countries due to widespread smoke-free legislation in public places, but exposure is still present in private settings like homes and cars. Our objective was to describe to what extent smokers implement smoking rules in these settings in six European Union (EU) Member States (MS). METHODS: A cross-sectional survey was conducted with a nationally representative sample of adult smokers from Germany, Greece, Hungary, Poland, Romania and Spain (ITC six European countries survey, part of the EUREST-PLUS Project). We analysed data from 6011 smokers regarding smoking rules in their homes and in cars with children (no rules, partial ban, total ban). We described the prevalence of smoking rules by EU MS and several sociodemographic and smoking characteristics using prevalence ratios (PR) and 95% confidence intervals (CI) derived from Poisson regression models. \. RESULTS: In homes, 26.5% had a total smoking ban (from 13.1% in Spain to 35.5% in Hungary), 44.7% had a partial ban (from 41.3% in Spain to 49.9% in Greece), and 28.8% had no-smoking rules (from 20.2% in Romania to 45.6% in Spain). Prevalence of no-smoking rules in cars with children was 16.2% (from 11.2% in Germany to 20.4% in Spain). The correlates of not restricting smoking in homes and cars included: low education (PR=1.51; 95%CI: 1.20-1.90 and PR=1.55; 95%CI: 1.09-2.20), smoking >30 cigarettes daily (PR=1.53; 95%CI: 1.10-2.14 and PR=2.66; 95%CI: 1.40-5.05) and no attempts to quit ever (PR=1.18; 95%CI: 1.06-1.31 and PR=1.28; 95%CI: 1.06-1.54). CONCLUSIONS: Among smokers in six EU MS, no-smoking rules were more prevalent in homes than in cars with children. Whilst awareness about the health effects of exposure to tobacco smoke on children seemed to be high, more research is needed to better understand the factors that promote private smoke-free environments.
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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 | 0.002 | 0.002 |
| 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 | 0.000 | 0.000 |
| 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".