Social Media Use for Public Health Campaigning in a Low Resource Setting: The Case of Waterpipe Tobacco Smoking
Bibliographic record
Abstract
INTRODUCTION: Waterpipe tobacco smoking prevalence is increasing worldwide despite its documented health effects. A general belief that it is less harmful than cigarettes may be fuelled by the lack of media campaigns highlighting its health effects. We aimed to create and assess the impact of a social media campaign about dangers of waterpipe smoking. METHODS: The "ShishAware" campaign included three social media (Facebook, Twitter, and YouTube) and a website. Nine months after launch we collected data to assess use of, and reaction to, our media accounts. RESULTS: Requiring limited maintenance resources, Facebook attracted campaign supporters but YouTube attracted opposers. Twitter enabled the most organisation-based contact but Facebook was the most interactive medium. Facebook users were more likely to "like" weekday than weekend statuses and more likely to comment on "shisha fact" than "current affairs" statuses. Follower subscription increased as our posting rate increased. Our YouTube video gained 19,428 views (from all world continents) and 218 comments (86% from pro-waterpipe smokers). CONCLUSIONS: Social media campaigns can be created and maintained relatively easily. They are innovative and have the potential for wide and rapid diffusion, especially towards target audiences. There is a need for more rigorous evaluation of their effects, particularly among the youth.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".