Factors and reasons for planning to quit smoking among a nationally representative sample of adults who smoke: Findings from the 2021 ITC EUREST-PLUS Spain Survey
Bibliographic record
Abstract
INTRODUCTION: Intentions to quit are the strongest predictor of successful smoking cessation and future quit attempts. This study assesses factors associated with quit intentions among adults who smoke in Spain. METHODS: Data are from the 2021 International Tobacco Control (ITC) EUREST-PLUS Spain Wave 3 Survey, a nationally representative survey of adults aged ≥18 years who smoke (n=1006). Analysis was restricted to 867 adults who provided information about quit intentions. Multivariable Poisson regression was used to examine several correlates of quit intentions. Adjusted prevalence ratios (APR) were estimated. RESULTS: Less than half (45.6%) of adults who smoke reported intending to quit, with only 13.0% intending to quit in the next 6 months; 11.3% reported at least one quit attempt in the past year. Factors associated with quit intentions were having a high income (APR=1.39; 95% CI: 1.01-1.92), having at least one quit attempt in the previous year (APR=1.41; 95% CI: 1.16-1.71), worrying that smoking will damage one's health (APR=1.52; 95% CI: 1.05-2.20), regretting starting to smoke (agree, APR=1.25; 95% CI: 1.03-1.52; disagree, APR=0.66; 95% CI: 0.46-0.95), health concerns (APR=1.46; 95% CI: 1.17-1.82), and smoking restrictions in public places (APR=1.28; 95% CI: 1.06-1.54). CONCLUSIONS: Only13% of adults from Spain who smoke intend to quit in the next 6 months. Factors associated with quitting were high income, at least one quit attempt in the past year, worrying about health damage from smoking, regretting starting to smoke, having health concerns, and smoking restrictions in public places. There is a need for comprehensive measures that encourage and support people to quit.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".