Sex Differences in Use of Smoking Cessation Services and Resources: A Real-World Study
Bibliographic record
Abstract
OBJECTIVES: Smoking cessation interventions with sex considerations have been found to effectively increase cessation rates. However, evidence is limited and weak. This study examined sex differences in the use of smoking cessation services or resources among Ontario adults. METHODS: Data are from the Smokers' Panel, an ongoing online survey of Ontario adult smokers and recent quitters. The analysis included 1009 male and 1765 female participants. Bivariate analysis was used to examine differences in sociodemographic characteristics and smoking-related variables by use of cessation services/resources. Logistic regression was then used to identify sociodemographic characteristics and smoking-related variables associated with the use of cessation services/resources. RESULTS: The analysis shows that there were significant sex differences in the use of individual interventions. Female participants were more likely to use nicotine patch (63% vs 58%; adjusted odds ratio, AOR: 1.39, 95% confidence interval [CI]: 1.16-1.67), varenicline (29% vs 24%; AOR: 1.37, 95% CI: 1.13-1.66), Smokers' Helpline phone (14% vs 10%; AOR: 1.39, 95% CI: 1.07-1.79), Smokers' Helpline online (27% vs 21%; AOR 1.43, 95% CI: 1.18-1.74), self-help materials (23% vs 16%; AOR: 1.81 95% CI: 1.46-2.26), and alternative methods (23% vs 19%; AOR: 1.40, 95% CI: 1.14-1.73) compared with male participants, after adjusting for covariates. CONCLUSION: Consistent with other findings, the study shows sex differences in the use of smoking cessation services or resources among adult smokers. Women are more likely to use recommended cessation resources such as nicotine patch, varenicline, and Smokers' Helpline than men. Health professionals should use this increased willingness to help female smokers quit. However, men may be underserved and more men-specific interventions need to be developed and evaluated.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".