Who Smokes in Europe? Data From 12 European Countries in the TackSHS Survey (2017–2018)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Population data on tobacco use and its determinants require continuous monitoring and careful inter-country comparison. We aimed to provide the most up-to-date estimates on tobacco smoking from a large cross-sectional survey, conducted in selected European countries. METHODS: Within the TackSHS Project, a face-to-face survey on smoking was conducted in 2017-2018 in 12 countries: Bulgaria, England, France, Germany, Greece, Ireland, Italy, Latvia, Poland, Portugal, Romania, and Spain, representing around 80% of the 432 million European Union (EU) adult population. In each country, a representative sample of around 1,000 subjects aged 15 years and older was interviewed, for a total of 11,902 participants. RESULTS: Overall, 25.9% of participants were current smokers (31.0% of men and 21.2% of women, P < 0.001), while 16.5% were former smokers. Smoking prevalence ranged from 18.9% in Italy to 37.0% in Bulgaria. It decreased with increasing age (compared to <45, multivariable odds ratio [OR] for ≥65 year, 0.31; 95% confidence interval [CI], 0.27-0.36), level of education (OR for low vs high, 1.32; 95% CI, 1.17-1.48) and self-rated household economic level (OR for low vs high, 2.05; 95% CI, 1.74-2.42). The same patterns were found in both sexes. CONCLUSIONS: These smoking prevalence estimates represent the most up-to-date evidence in Europe. From them, it can be derived that there are more than 112 million current smokers in the EU-28. Lower socio-economic status is a major determinant of smoking habit in both sexes.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it