Socioeconomic Differences in the Effectiveness of the Removal of the “Light” Descriptor on Cigarette Packs: Findings from the International Tobacco Control (ITC) Thailand Survey
Bibliographic record
Abstract
Many smokers incorrectly believe that "light" cigarettes are less harmful than regular cigarettes. To address this problem, many countries have banned "light" or "mild" brand descriptors on cigarette packs. Our objective was to assess whether beliefs about "light" cigarettes changed following the 2007 removal of these brand descriptors in Thailand and, if a change occurred, the extent to which it differed by socioeconomic status. Data were from waves 2 (2006), 3 (2008), and 4 (2009) of the International Tobacco Control (ITC) Thailand Survey of adult smokers in Thailand. The results showed that, following the introduction of the ban, there was an overall decline in the two beliefs that "light" cigarettes are less harmful and smoother than regular cigarettes. The decline in the "less harmful" belief was considerably steeper in lower income and education groups. However, there was no evidence that the rate of decline in the "smoother" belief varied by income or education. Removing the "light" brand descriptor from cigarette packs should thus be viewed not only as a means to address the problem of smokers' incorrect beliefs about "light" cigarettes, but also as a factor that can potentially reduce socioeconomic disparities in smoking-related misconceptions.
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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 | 0.008 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".