Effectiveness of Text-Only Cigarette Health Warnings in Japan: Findings from the 2018 International Tobacco Control (ITC) Japan Survey
Bibliographic record
Abstract
Health warnings are an effective strategy for communicating the health harms of smoking, encouraging quitting, and preventing smoking initiation. This study examines the effectiveness of existing text-only health warnings, identifies key predictors of warning effectiveness, and assesses support for pictorial warnings in Japan. Data are from the 2018 International Tobacco Control (ITC) Japan Survey, a cohort survey of adult cigarette smokers (n = 3306), dual users of cigarettes and heated tobacco products (n = 555), and non-cigarette smokers (n = 823). Weighted multivariable logistic regression models were used to assess predictors of warning effectiveness and support for pictorial warnings. Overall, 15.6% of respondents noticed warnings, and 7.9% read or looked closely at warnings. Overall, 10.3% of smokers and dual users said the warnings stopped them from having a cigarette, and 7.2% avoided warnings. Overall, 27.5% of respondents said the warnings made them think about health risks of smoking, but only 2.7% of smokers and dual users said the warnings made them more likely to quit. Overall, 57.6% of respondents supported pictorial warnings. The weak effectiveness of Japan's text-only warnings is consistent with that in other countries with similar warnings. There is majority support for pictorial warnings in Japan, although the level of support is lower than in other countries.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".