Structured evaluation of gender integration in tobacco control research
Bibliographic record
Abstract
BACKGROUND: Gender norms are known to influence smoking behaviours, but studies evaluating tobacco control policies frequently overlook these factors by relying on gender-blind methodologies. The degree to which gender-sensitive methodological approaches are used in tobacco control research since the adoption of the Framework Convention on Tobacco Control has not yet been evaluated. METHODS: We applied the European Institute for Gender Equality's Gender Impact Assessment (EIGE-GIA) toolkit to 43 peer-reviewed studies to assess the integration of gender-sensitive approaches in tobacco control research. Original tobacco control research studies published after 2005 based on nationally representative data were identified from PubMed using targeted search terms and a reverse snowball strategy. Each study was coded against the EIGE-GIA's core criteria of specifying the gendered target group and assessing the gendered impact of policy interventions. RESULTS: Among the 43 studies analysed, 40 identified specific target groups and outlined key challenges related to tobacco use and MPOWER policies, meeting the first EIGE-GIA criterion. However, only 16 studies assessed specific tobacco control policies, and only 5 evaluated the gender-specific impacts of these policies. Many studies failed to meet the second criterion, often relying on binary comparisons that ignore complex gender dynamics. CONCLUSIONS: Our findings reveal persistent gaps in tobacco control research methodologies. Reliance on binary sex-disaggregated data that fail to explore gender-based determinants of health limits our understanding of the effectiveness of tobacco control policy interventions and fails to address gendered smoking behaviours. Researchers should use comprehensive frameworks to guide the assessment of gendered impacts of tobacco control interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".