Predictors of car smoking rules among smokers in France, Germany and the Netherlands
Bibliographic record
Abstract
BACKGROUND: As exposure to tobacco smoke pollution (TSP) has been identified as a cause of premature death and disease in non-smokers, and studies have demonstrated that smoking in cars produces high levels of TSP, this study will investigate smokers' rules for smoking in their cars, and predictors of car smoking rules, including potentially modifiable correlates. METHODS: Data were drawn from nationally representative samples of current smokers from the International Tobacco Control Policy Evaluation Project surveys in France (2007), Germany (2007), and the Netherlands (2008). Smokers in France and Germany were asked about smoking rules in their cars, and smokers in the Netherlands were asked about smoking rules in cars carrying children. RESULTS: In France and Germany, 59% and 52% of smokers respectively, allowed smoking in their cars. In the Netherlands, 36% of smokers allowed smoking in cars carrying children. Predictors of allowing smoking in cars included: being a daily vs. non-daily smoker, being younger vs. older age, having no (young) children in the home, being a heavier smoker, and allowing smoking in the home. In the Netherlands, smokers who agreed that TSP is dangerous to non-smokers were less likely to allow smoking in cars carrying children. CONCLUSION: Overall, a sizeable proportion of smokers allowed smoking in their cars across the three countries. Media campaigns with information about the dangers of TSP may increase the adoption of smoke-free cars. These media campaigns could target smokers who are most likely to allow smoking 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 | 0.012 | 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 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".