Gender Differences in Medication Use and Cigarette Smoking Cessation: Results From the International Tobacco Control Four Country Survey
Bibliographic record
Abstract
INTRODUCTION: There is conflicting evidence for gender differences in smoking cessation, and there has been little research on gender differences in smoking cessation medication (SCM) use and effectiveness. Using longitudinal data from the International Tobacco Control Four Country Surveys (ITC-4) conducted in the United Kingdom, the United States, Canada, and Australia, we examined gender differences in the incidence of quit attempts, reasons for quitting, use of SCMs, reasons for discontinuing use of SCMs, and rates of smoking cessation. METHODS: Data were analyzed from adult smokers participating in the ITC-4, annual waves 2006-2011 (n = 7,825), as well as a subsample of smokers (n = 1,079) who made quit attempts within 2 months of survey. Adjusted modeling utilized generalized estimating equations. RESULTS: There were no gender differences in the likelihood of desire to quit, plans to quit, or quit attempts between survey waves. Among quit attempters, women had 31% lower odds of successfully quitting (OR = 0.69; 95% CI = 0.51, 0.94). Stratified by medication use, quit success was lower among women who did not use any SCMs (OR = 0.59; 95% CI = 0.39, 0.90), and it was no different from men when medications were used (OR = 0.73; 95% CI = 0.46, 1.16). In particular, self-selected use of nicotine patch and varenicline contributed to successful quitting among women. CONCLUSIONS: Women may have more difficulty quitting than men, and SCMs use may help attenuate this difference.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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".