Varieties of gender differences in tobacco smoking behaviours
Bibliographic record
Abstract
Introduction The presence of gender differences is well known in a wide variety of areas, including employment, education, and income. In most European Union countries, size of the gender differences in tobacco smoking decreases with size of place of residence and education and increases with age until late adulthood. Measures aimed at narrowing the gender gap in socio-economic indicators are justified in the Partnership agreements on the European structural and investment funds which have been signed between the European Commission and member states. Methods This study analyses gender gap in tobacco-smoking behaviours according to the results of a two-wave study Awareness of Cancer and Prevention, which was carried out on a stratified random sample of n=2∙8,000 Poles aged 18+. Cramér’s V was used to assess the size of gender differences. The two-way analysis was followed by propensity score matching. Results According to the propensity score matching, being male currently affects tobacco smoking behaviours in terms of average treatment effect, even when controlling for socio-demographic characteristics, health status, and knowledge of lung cancer prevention. In Poland, in terms of Cramér’s V, the size of gender differences in tobacco non-use had not changed considerably between 2006 and 2014 (16.5 pp and 17.2 pp). The pattern of gender gap in tobacco non-use by size of place of residence and age had remained generally similar. However, a large increase in gender gap in tobacco non-use in the population with higher education was observed between 2006 and 2014 (2.5 pp and 11.8 pp). Men with higher education were the only gender-education group, which recorded a decrease in non-smoking. Conclusions Within the conceptualization by Rosenberg and Hovland (1960), high gender differences apply to the behavioural component of lung cancer prevention rather than affective and cognitive domains. Observed decrease in tobacco non-smoking in men with higher education demands additional actions.
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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 | 0.005 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".