Associations of Cannabis Use, High-Risk Alcohol Use, and Depressive Symptomology with Motivation and Attempts to Quit Cigarette Smoking Among Adults: Findings from the 2020 ITC Four Country Smoking and Vaping Survey
Bibliographic record
Abstract
Abstract This study assessed independent and interaction effects of the frequency of cannabis use, high-risk alcohol use, and depressive symptomology on motivation and attempts to quit cigarette smoking among adults who regularly smoked. Cross-sectional data are from the 2020 International Tobacco Control Four Country Smoking and Vaping Survey and included 7044 adults (ages 18 + years) who smoked cigarettes daily in Australia (n = 1113), Canada (n = 2069), England (n = 2444), and the United States (USA) (n = 1418). Among all respondents, 33.1% of adults reported wanting to quit smoking “a lot,” and 29.1% made a past-year quit attempt. Cannabis use was not significantly associated with either outcome (both p ≥ 0.05). High-risk alcohol use was significantly associated with decreased odds of motivation to quit (p = 0.02) and making a quit attempt (p = 0.004). Depressive symptomology was associated with increased odds for both outcomes (both p < 0.001). There were no significant 2- or 3-way interactions between cannabis use, alcohol consumption, and depressive symptomatology. Overall, just over a quarter of adults who smoked daily reported making a recent quit attempt, and most were not highly motivated to quit. Longitudinal research should investigate whether there are linkages between cannabis use, risky alcohol consumption, and/or depression on successful long-term smoking cessation.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".