Receiving support to quit smoking and quit attempts amongsmokers with and without smoking related diseases: Findingsfrom the EUREST-PLUS ITC Europe Surveys
Bibliographic record
Abstract
INTRODUCTION: Having a chronic disease either caused or worsened by tobacco smoking does not always translate into quitting smoking. Although smoking cessation is one of the most cost-effective medical interventions, it remains poorly implemented in healthcare settings. The aim was to examine whether smokers with chronic and respiratory diseases were more likely to receive support to quit smoking by a healthcare provider or make a quit attempt than smokers without these diseases. METHODS: This population-based study included a sample of 6011 adult smokers in six European countries. The participants were interviewed face-to-face and asked questions on sociodemographic characteristics, current diagnoses for chronic diseases, healthcare visits in the last 12 months and, if so, whether they had received any support to quit smoking. Questions on smoking behavior included nicotine dependence, motivation to quit smoking and quit attempts in the last 12 months. The results are presented as weighted percentages with 95% confidence intervals (CI) and as adjusted odds ratios with 95% CI based on logistic regression analyses. RESULTS: Smokers with chronic respiratory disease, those aged 55 years and older, as well as those with one or more chronic diseases were more likely to receive smoking cessation advice from a healthcare professional. Making a quit attempt in the last year was related to younger age, high educational level, higher motivation to quit, lower nicotine dependence and having received advice to quit from a healthcare professional but not with having chronic diseases. There were significant differences between countries with smokers in Romania consistently reporting more support to quit as well as quit attempts. CONCLUSIONS: Although smokers with respiratory disease did indeed receive smoking cessation support more often than smokers without disease, many smokers did not receive any advice or support to quit during a healthcare visit.
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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 | 0.000 |
| 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".