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 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 | 0.014 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".