Secondhand smoke exposure assessment in outdoor hospitality venues across 11 European countries
Bibliographic record
Abstract
Due to partial or poorly enforced restrictions secondhand tobacco smoke (SHS) is still present in outdoor hospitality venues in many European countries. This study aimed to assess SHS concentrations in outdoor hospitality venues across Europe and identify contextual exposure determinants. Cross-sectional study. We measured airborne nicotine and evidence of tobacco use in terraces of bars, cafeterias, and pubs from 11 European countries in 2017–2018. Sites were selected considering area-level socioeconomic indicators and half were visited during nighttime. We noted the smell of smoke, presence of smokers, cigarette butts, ashtrays, and number of physical covers. Contextual determinants included national smoke-free policies for the hospitality sector, the Tobacco Control Scale score (2016), and the national smoking prevalence (2017–2018). We computed medians and interquartile ranges (IQR) of nicotine concentrations and used multivariate analyses to characterize the exposure determinants. Nicotine was present in 93.6% of the 220 sites explored. Overall concentrations were 0.85 (IQR:0.30–3.74) μg/m3 and increased during nighttime (1.45 IQR:0.65–4.79 μg/m3), in enclosed venues (2.97 IQR:0.80–5.80 μg/m3), in venues with more than two smokers (2.79 IQR:1.03–6.30 μg/m3), in venues in countries with total indoor smoking bans (1.20 IQR:0.47–4.85 μg/m3), and in venues in countries with higher smoking prevalence (1.32 IQR:0.49–5.34 μg/m3). In multivariate analyses, nicotine concentrations were also positively associated with the observed number of cigarette butts. In venues with more than two smokers, SHS levels did not significantly vary with the venues’ degree of enclosure. Our results suggest that current restrictions in outdoor hospitality venues across Europe have a limited protective effect and justify the adoption of total smoking bans in outdoor areas of hospitality venues.
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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 | 0.000 |
| 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".