Graphic health warnings and their best position on waterpipes: A cross-sectional survey of expert and public opinion
Bibliographic record
Abstract
INTRODUCTION: Our aim was to assess the visibility and efficiency of graphic health warnings (GHWs) on waterpipe tobacco packs (WTPs) and to explore other more effective places to display them for better impact. We also evaluated the visibility of GHWs when placed on the waterpipe device. METHODS: We conducted 3 cross-sectional study phases using face-to-face survey questionnaires in 2014-2015. Phase I surveyed 31 tobacco control experts, while Phase II surveyed 700 participants and Phase III surveyed 348 from the public in Cairo, Egypt. RESULTS: Approximately half of the experts and participants in Phases II and III thought that GHWs on WTPs are not adequately visible, and 68.9% and 79.6% in Phases II and III, respectively, suggested posting warnings also in other places. About one-third of experts and 69.1% of Phase II participants suggested posting GHWs inside cafés or in public places, while 46.9% of Phase III participants favored placing them on waterpipes. After viewing our suggested positions on a waterpipe, all experts, 80.6% of participants in Phase II, and 81.6% in Phase III acknowledged that GHWs would be more visible there. The mouthpiece was the location selected most often across all phases (31.1% in Phase I, 35.6% in Phase II and 36.3% in Phase III). Lung and throat cancers were similarly effective in raising participants' concern about waterpipe smoking health risks (24.7%). CONCLUSIONS: This is the first population-based study to explore the best location to place GHWs on waterpipes. Policymakers should consider enacting a regulatory framework for placing GHWs on waterpipe devices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".