Dispelling myths about gender differences in smoking cessation: population data from the USA, Canada and Britain
Bibliographic record
Abstract
OBJECTIVES: Based mainly on findings from clinical settings, it has been claimed that women are less likely than men to quit smoking successfully. If true, this would have important implications for tobacco control interventions. The authors aimed to test this possibility using data from general population surveys. METHODS: The authors used data from major national surveys conducted in 2006-2007 in the USA (Tobacco Use Supplement to the Current Population Survey), Canada (Canadian Tobacco Use Monitoring Survey) and the UK (General Household Survey) to estimate rates of smoking cessation by age in men and women. RESULTS: The authors found a pattern of gender differences in smoking cessation which was consistent across countries. Below age 50, women were more likely to have given up smoking completely than men, while among older age groups, men were more likely to have quit than women. Across all age groups, there was relatively little difference in cessation between the sexes. CONCLUSIONS: Conclusions about gender differences in smoking cessation should be based on evidence from the general population rather than from atypical clinical samples. This study has found convincing evidence that men in general are not more likely to quit smoking successfully than women.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".