Perceptions, Predictors of and Motivation for Quitting among Smokers from Six European Countries from 2016 to 2018: Findings from EUREST-PLUS ITC Europe Surveys
Bibliographic record
Abstract
The European Tobacco Products Directive (TPD) was introduced in 2016 in an effort to decrease prevalence of smoking and increase cessation in the European Union (EU). This study aimed to explore quitting behaviours, motivation, reasons and perceptions about quitting, as well as predictors (reported before the TPD implementation) associated with post-TPD quit status. A cohort study was conducted involving adult smokers from six EU countries (n = 3195). Data collection occurred pre-(Wave 1; 2016) and post-(Wave 2; 2018) TPD implementation. Bivariate and logistic regression analyses of weighted data were conducted. Within this cohort sample, 415 (13.0%) respondents reported quitting at Wave 2. Predictors of quitting were moderate or high education, fewer cigarettes smoked per day at baseline, a past quit attempt, lower level of perceived addiction, plans for quitting and the presence of a smoking-related comorbidity. Health concerns, price of cigarettes and being a good example for children were among the most important reasons that predicted being a quitter at Wave 2. Our findings show that the factors influencing decisions about quitting may be shared among European countries. European policy and the revised version of TPD could emphasise these factors through health warnings and/or campaigns and other policies.
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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 | 0.002 | 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 | 0.001 | 0.001 |
| 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".