Global reporting of waterpipe tobacco policy in online news articles in 2015: a cross-sectional analysis
Bibliographic record
Abstract
Background Policy discussions are often articulated through the news media. The aim of this study is to understand how waterpipe tobacco policy is narrated and developed over time in online news media. Methods We conducted a cross-sectional analysis of online news articles published in the year 2015. We used the online news content retriever service 'Google Alerts' to receive weekly emails on any news article that contained 'waterpipe' or its synonyms in the title or body of English news articles. We coded text into one of the following themes: taxation, smoke-free law, regulation of content and emissions, health warning labels, bans on advertising, promotion and sponsorship, cessation programs, restriction of sale to minors, zoning or licensing, prohibition, and other waterpipe-specific policy. Results We included 567 news articles from 33 countries. We elicited 14 themes, which we counted 1309 times (mean 2.3 themes per article). The five most common themes were licensing or zoning (24.7%), Smoke-free law (20.9%), prohibition (13.4%), waterpipe-specific policy (9.4%), and sales to minors (7.0%). We report on the dominant policy narratives in six countries: Canada, India, Pakistan, the UAE, UK, and the US. Conclusions Geographical variation in policy narratives can be a useful tool for future policy dialogue around waterpipe tobacco control and prevention.
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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 | 0.008 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".