P4417Evaluating compliance of labelling on tobacco packets with WHO recommendations and national legislation in 12 middle eastern countries across levels of economic development
Bibliographic record
Abstract
Introduction: Over one billion people smoke worldwide, half of whom will die of tobacco-related illnesses. Despite knowledge of harms, smoking rates remain high and continue to rise in Middle Eastern Countries. Health warnings have been shown to make cigarettes appear less attractive, increase the smoker's awareness of harms, increase quit attempts and lead to a reduction in cigarette smoking. Deceptive promotional labels, such as “light” or “mild”, have been shown to have the opposite effects by misleading consumers into minimizing the harms of smoking. Initiatives such as the Framework Convention on Tobacco Control (FCTC) were developed by the WHO to address the tobacco epidemic through health policy. Despite its positive impact, implementation remains a challenge. In this study, we assess compliance of labelling on tobacco packets from twelve Middle Eastern countries with national legislation and WHO recommendations. Methods: Investigators from twelve Middle Eastern countries collected at least 10 unique packets of the most commonly consumed and cheapest brands of cigarettes between January 2015 and November 2016. The countries included Bahrain, Israel, Kuwait, Oman, Qatar, Saudi Arabia, United Arab Emirates (High-Income Countries – HIC), Jordan, Lebanon, Turkey (Upper-Middle Income Countries – UMIC), Egypt, and Palestine (Low-Middle Income Countries – LMIC). A total of 140 packets were inspected using a structured data collection tool; all labels were assessed for content, size, and location.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".