Social norms towards smoking and electronic cigarettesamong adult smokers in seven European Countries: Findingsfrom the EUREST-PLUS ITC Europe Surveys
Bibliographic record
Abstract
INTRODUCTION: This study explores whether current smokers' social norms towards smoking and electronic cigarettes (e-cigarettes) vary across seven European countries alongside smoking and e-cigarette prevalence rates. At the time of surveying, England had the lowest current smoking prevalence and Greece the highest. Hungary, Romania and Spain had the lowest prevalence of any e-cigarette use and England the highest. METHODS: Respondents were adult (≥18 years) current smokers from the 2016 EUREST-PLUS ITC (Romania, Spain, Hungary, Poland, Greece, Germany) and ITC 4CV England Surveys (N=7779). Using logistic regression, associations between country and (a) smoking norms and (b) e-cigarette norms were assessed, adjusting for age, sex, income, education, smoking status, heaviness of smoking, and e-cigarette status. RESULTS: Compared with England, smoking norms were higher in all countries: reporting that at least three of five closest friends smoke (19% vs 65-84% [AOR=6.9-24.0; Hungary-Greece]), perceiving that people important to them approve of smoking (8% vs 14-57% [1.9-51.1; Spain-Hungary]), perceiving that the public approves of smoking (5% vs 6-37% [1.7-15.8; Spain-Hungary]), disagreeing that smokers are marginalised (9% vs 16-50% [2.3-12.3; Poland-Greece]) except in Hungary. Compared with England: reporting that at least one of five closest friends uses e-cigarettes was higher in Poland (28% vs 36% [2.7]) but lower in Spain and Romania (28% vs 6-14% [0.3-0.6]), perceiving that the public approves of e-cigarettes was higher in Poland, Hungary and Greece (32% vs 36-40% [1.5-1.6]) but lower in Spain and Romania in unadjusted analyses only (32% vs 24-26%), reporting seeing e-cigarette use in public at least some days was lower in all countries (81% vs 12-55% [0.1-0.4]; Spain-Greece). CONCLUSIONS: Smokers from England had the least pro-smoking norms. Smokers from Spain had the least pro-e-cigarette norms. Friend smoking and disagreeing that smokers are marginalised broadly aligned with country-level current smoking rates. Seeing e-cigarette use in public broadly aligned with country-level any e-cigarette use. Generally, no other norms aligned with product prevalence.
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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 | 0.003 | 0.003 |
| 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 | 0.000 | 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".