Positive impact of Australian ‘blindness ’ tobacco warning labels: findings from the ITC four country survey
Bibliographic record
Abstract
Background: Smokers with greater knowledge of the health effects of smoking are more likely to quit and remain abstinent. Australia has communicated the causal association of smoking and blindness since the late 1990s. In March 2007, Australia became the first country to include a pictorial warning label on cigarette packages with the message that smoking causes blindness. The current study tested the hypothesis that the introduction of this warning label increased smokers ’ knowledge of this important health effect. Methods: Six waves of the International Tobacco Control Four Country Survey were conducted, as a telephone survey of 17,472 adult smokers in Australia, Canada, United Kingdom and the United States, with three waves before and three waves after the blindness health warning was introduced in Australia. The survey measured adult smokers ’ knowledge that smoking causes blindness. Results: Australian smokers were significantly more likely to report that smoking causes blindness, compared to Canadian, UK and US smokers, where there were neither health campaigns nor health warnings labels about blindness. After the introduction of the blindness warning, Australian smokers were more likely than before the blindness warning to report that they know that smoking causes blindness (62 versus 49 per cent; OR = 1.68, 95 % CI: 1.03, 2.76, p = 0.04). In Australia, smokers aged over 55 years were less likely than those aged 18 to 24 to report that smoking causes blindness (OR = 0.43;
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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 | 0.016 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".