Gender empowerment and female-to-male smoking prevalence ratios
Bibliographic record
Abstract
OBJECTIVE: To determine whether in countries with high gender empowerment the female-to-male smoking prevalence ratio is also higher. METHODS: Bivariate and multiple regression analyses were performed to explore the relation between the United Nations Development Programme's gender empowerment measure (GEM) and the female-to-male smoking prevalence ratio (calculated from the 2008 WHO global tobacco control report). Because a country's progression through the various stages of the tobacco epidemic and its gender smoking ratio (GSR) are thought to be influenced by its level of development, we explored this correlation as well, with economic development defined in terms of gross national income (GNI) per capita and income inequality (Gini coefficient). FINDINGS: The GSR was significantly and positively correlated with the GEM (r=0.680; P<0.001). In addition, the GEM was the strongest predictor of the GSR (β, adjusted: 0.47; P<0.0001) after controlling for GNI per capita and for Gini coefficient. CONCLUSION: Whether progress towards gender empowerment can take place without a corresponding increase in smoking among women remains to be seen. Strong tobacco control measures are needed in countries where women are being increasingly empowered.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".