Gender differences in cigarette smoking cessation attempts among adults who smoke and drink alcohol at high levels: Findings from the 2018–2020 International Tobacco Control Four Country Smoking and Vaping Surveys
Bibliographic record
Abstract
BACKGROUND: This study examined the association between alcohol consumption and smoking cessation behaviour of adults who smoke in four countries. METHODS: Data came from 4275 adults (≥18 years) who smoked tobacco ≥ monthly and participated in the 2018 and 2020 International Tobacco Control Four Country Smoking and Vaping Surveys (Australia: n = 720; Canada: n = 1250; US: n = 1011; England: n = 1294). The 2018 Alcohol Use Disorders Identification Test-Consumption (AUDIT-C) survey data coded into three levels ('never/low', 'moderate' or 'heavy' consumption) were analysed using multivariable logistic regression models to predict any smoking cessation attempts and successful cessation by 2020 survey, and whether this differed by gender and country. RESULTS: Compared to never/low alcohol consumers, only those who drink heavily were less likely to have made a quit smoking attempt (40.4 % vs. 43.8 %; AOR = 0.69, 95 % CI = 0.57-0.83, p < .001). The association differed by gender and country (3-way interaction, p < .001), with females who drink heavily being less likely to attempt to quit smoking in England (AOR = 0.27, 95 % CI = 0.15-0.49, p < .001) and Australia (AOR = 0.38, 95 % CI = 0.19-0.77, p = .008), but for males, those who drink moderately (AOR = 2.18, 95 % CI = 1.17-4.06, p = .014) or heavily (AOR = 2.61, 95 % CI = 1.45-4.68, p = .001) were more likely to make a quit attempt in England only. Alcohol consumption did not predict quit success. CONCLUSION: Heavy alcohol use among adults who smoke appears to only undermine the likelihood of trying to quit smoking with some variation by gender and country of residence, but not their chances of succeeding if they tried.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".