Potential Impact of Graphic Health Warnings on Cigarette Packages in Reducing Cigarette Demand and Smoking-Related Deaths in Vietnam
Bibliographic record
Abstract
Two years after implementation of the graphic health warning intervention in Vietnam, it is very important to evaluate the intervention's potential impact. The objective of this paper was to predict effects of graphic health warnings on cigarette packages, particularly in reducing cigarette demand and smoking-associated deaths in Vietnam. In this study, a discrete choice experiment (DCE) method was used to evaluate the potential impact of graphic tobacco health warnings on smoking demand. To predict the impact of GHWs on reducing premature deaths associated with smoking, we constructed different static models. We adapted the method developed by University of Toronto, Canada and found that GHWs had statistically significant impact on reducing cigarette demand (up to 10.1% through images of lung damage), resulting in an overall decrease of smoking prevalence in Vietnam. We also found that between 428,417- 646,098 premature deaths would be prevented as a result of the GHW intervention. The potential impact of the GHW labels on reducing premature smoking-associated deaths in Vietnam were shown to be stronger among lower socio-economic groups.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".