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
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».