Knowledge of the health risks of smoking and impact of cigarette warning labels among tobacco users in six European countries: Findings from the EUREST-PLUS ITC Europe Surveys
Bibliographic record
Abstract
INTRODUCTION: The aim of this study was to examine knowledge of health effects of smoking and the impact of cigarette package warnings among tobacco users from six European Union (EU) Member States (MS) immediately prior to the introduction of the EU Tobacco Products Directive (TPD) in 2016 and to explore the interrelationship between these two factors. METHODS: Cross-sectional data were collected via face-to-face interviews with adult smokers (n=6011) from six EU MS (Germany, Greece, Hungary, Poland, Romania, Spain) between June-September 2016. Sociodemographic variables and knowledge of health risks of smoking (KHR) were assessed. Warning salience, thoughts of harm, thoughts of quitting and foregoing of cigarettes as a result of health warnings were assessed. The Label Impact Index (LII) was used as a composite measure of warning effects. Linear and logistic regression analyses were used to examine sociodemographic predictors of KHR and LII and the inter-relationship between knowledge and LII scores. RESULTS: The KHR index was highest in Romania and Greece and lowest in Hungary and Germany. While the majority of smokers knew that smoking increases the risk for heart diseases, lung and throat cancer, there was lower awareness that tobacco use caused mouth cancer, pulmonary diseases, stroke, and there were very low levels of knowledge that it was also associated with impotence and blindness, in all six countries. Knowledge regarding the health risks of passive smoking was moderate in most countries. The LII was highest in Romania and Poland, followed by Spain and Greece, and lowest in Germany and Hungary. In almost all countries, there was a positive association between LII scores and higher KHR scores after controlling for sociodemographic variables. Several sociodemographic factors were associated with KHR and LII, with differences in these associations documented across countries. CONCLUSIONS: These data provide evidence to support the need for stronger educational efforts and policies that can enhance the effectiveness of health warnings in communicating health risks and promoting quit attempts. Data will serve as a baseline for examining the impact of the TPD.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".