The lower effectiveness of text-only health warnings in China compared to pictorial warnings in Malaysia: findings from the ITC project
Bibliographic record
Abstract
BACKGROUND: In 2009, China changed its health warnings on cigarette packs from side-only text warnings to two text-only warnings on 30% of the bottom of the front and back of the pack. Also in 2009, Malaysia changed from similar text warnings to pictorial health warnings consistent with Framework Convention on Tobacco Control (FCTC) Article 11 Guidelines. OBJECTIVE: To measure the impact of the change in health warnings in China and to compare the text-only health warnings to the impact of the pictorial health warnings introduced in Malaysia. METHODS: We measured changes in key indicators of warning effectiveness among a longitudinal cohort sample of smokers from Waves 1 to 3 (2006-2009) of the International Tobacco Control (ITC) China Survey and from Waves 3 to 4 (2008-2009) of the ITC Malaysia Survey. Each cohort consisted of representative samples of adult (≥18 years) smokers from six cities in China (n=6575) and from a national sample in Malaysia (n=2883). Generalised Estimating Equations (GEE) were used to examine the impact of the health warnings on subsequent changes in salience of warnings, cognitive and behavioural outcomes. FINDINGS: Compared to Malaysia, the weak text-only warning labels in China led to a significant change in only two of six key indicators of health warning effectiveness: forgoing cigarettes and reading the warning labels. The change to pictorial health warnings in Malaysia led to significant and substantial increases in five of six indicators (noticing, reading, forgoing, avoiding, thinking about quitting). CONCLUSIONS: The delay in implementing pictorial health warnings in China constitutes a lost opportunity for increasing knowledge and awareness of the harms of cigarettes, and for motivating smokers to quit.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".