Local Brand Smoking Among Adult Smokers: Findings from the Wave 5 International Tobacco Control China Survey — China, 2015
Bibliographic record
Abstract
What is already known about this topic?: Branding of cigarettes may play a role in shaping the smoking behaviors of Chinese smokers, and local brand (LB) cigarettes may reflect this influence because of greater tax and non-tax incentives compared to non-LB. Some of these brands are regional flagships that market to smokers using local landmarks or icons. What is added by this report?: LB brands were significantly more likely to be the usual brand of smokers residing in provincial-level administrative divisions (PLADs) that produced their own LB cigarettes [adjusted odds ratio (AOR): 30.95; 95% confidence interval (CI): 26.36-36.49] compared to those residing in PLADs that had non-local ventures with non-LB cigarettes. Further, smokers residing in urban areas were found to be less likely to smoke LB cigarettes (AOR: 0.79; 95% CI: 0.67-0.93) compared to those in rural areas. What are the implications for public health practice?: These findings suggest that LB smoking may be a result of industry-driven incentives to boost LB sales, fueled by such as supply-side strategies to boost LB sales or targeted cultural/social marketing that appeals to certain demographic groups. Although addressing these incentives to support LBs would be challenging given the nature of China's tobacco industry, doing so would have potential to reduce cigarette smoking and ultimately the health burden of smoking in China.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".