Exposure to car smoking among youth in seven countries of the European Union
Bibliographic record
Abstract
Abstract Background In the USA and Canada, cars are a major source of secondhand smoke (SHS) exposure among youth. Little is known about the magnitude of this public health problem in European countries. In this report, we study SHS exposure in vehicles among adolescents across 7 member states of the European Union (EU), with a particular focus on socio-economic characteristics and adolescents’ smoking environment. Methods Data on self-reported SHS exposure in cars during the past seven days was obtained from the 2016/17 cross-sectional SILNE-R study from 14-17-year old adolescents in EU seven countries (N = 10,481). We applied two multivariate logistic regression models with sociodemographic characteristics and mediating smoking-related factors. Results SHS car exposure varied widely across the 7 EU countries: 6% in Finland, 12% in Ireland, 15% in the Netherlands, 19% in Germany, 23% in Portugal, 36% in Belgium and 43% in Italy. Low paternal educational levels were strong correlates of SHS exposure in cars as well as migration background. Other correlates were one’s own smoking status and the relation to the family and peer smoking environment, such as parental smoking, permissive smoking rules at home, and best friends smoking. Conclusions In most of these seven countries, a considerable proportion of youth, particularly those from disadvantaged backgrounds, is exposed to SHS in cars. There is a need to ensure adoption and sustained enforcement of smoke-free car legislation. Given the long-term effectiveness of smoke-free car policies, our finding suggests that such policies can contribute towards reducing smoking inequalities. Key messages We assessed adolescent secondhand smoke (SHS) exposure in cars in 7 EU countries, which varies widely, ranging from 6% in Finland to 43% in Italy. The findings point to a social gradient, environmental factors in SHS car exposure and call for the rapid implementation of smoke-free car legislation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".