Do smokers want to protect non-smokers from the harms of second-hand smoke in cars? Findings from the EUREST-PLUS ITC Europe Surveys
Bibliographic record
Abstract
BACKGROUND: There is currently no comprehensive legislation protecting non-smokers and children from second-hand smoke (SHS) exposure in private cars at the European Union (EU) level. This study aims to assess smokers' support for smoke-free cars legislation in six EU countries. METHODS: Data come from the EUREST-PLUS ITC Europe Surveys: Wave 1 (2016, n = 6011) and Wave 2 (2018, n = 6027) conducted in Germany, Greece, Hungary, Poland, Romania and Spain. Support for smoke-free cars carrying pre-school children and non-smokers and voluntary implementation of smoke-free cars were assessed among adult smokers. Generalized estimating equations models were used to assess changes in support between waves. RESULTS: In 2018, 96.3% [95% confidence interval (CI) 95.4-97.0%] of the overall sample supported smoke-free legislation for cars carrying pre-school children, representing an increase of 2.4 percentage points in comparison to 2016. Smoke-free legislation for cars transporting non-smokers was supported by 85.2% (95% CI 83.1-87.1%) of smokers' in 2016 and 90.2% (95% CI 88.6-91.7%) in 2018. Among smokers who owned cars, there was a significant 7.2 percentage points increase in voluntary implementation of smoke-free cars carrying children from 2016 (60.7%, 95% CI 57.2-64.0%) to 2018 (67.9%, 95% CI 65.1-70.5%). All sociodemographic groups of smokers reported support higher than 80% in 2018. CONCLUSION: The vast majority of smokers in all six EU countries support smoke-free legislation for cars carrying pre-school children and non-smokers. This almost universal support across countries and sociodemographic groups is a clear indicator of a window of opportunity for the introduction of comprehensive legislation to protect non-smokers and children from SHS exposure in cars.
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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 | 0.005 | 0.011 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".