Trend over time on knowledge of the health effects of cigarette smoking and smokeless tobacco use in Bangladesh: Findings from the International Tobacco Control Policy Evaluation Bangladesh Surveys
Bibliographic record
Abstract
INTRODUCTION: Cigarette smoking and smokeless tobacco (ST) use are prevalent in Bangladesh. This longitudinal study examined how knowledge of the health effects of smoking and ST use in Bangladesh has changed overtime with the country's acceleration of tobacco control efforts. METHODS: Data were analysed from the International Tobacco Control Survey, a nationally representative longitudinal study of users and non-users of tobacco (aged 15 and older) in Bangladesh, across four waves conducted in 2009 (n = 4378), 2010 (n = 4359), 2012 (n = 4223) and 2015 (n = 4242). Generalised estimating equations assessed the level of knowledge about harms of tobacco use across four waves. Multivariable logistic regressions assessed whether knowledge of health effects from cigarette smoking and ST use in 2015 differed by user group. RESULTS: In 2015 survey, most tobacco users were aware that cigarette smoking causes stroke (92%), lung cancer (97%), pulmonary tuberculosis (97%) and ST use causes mouth cancer (97%) and difficulty in opening mouth (80%). There were significant increases in the total knowledge score of smoking related health harm from 2010 to 2012 (mean difference = 0.640; 95% confidence interval [CI] 0.537, 0.742) and 2012 to 2015 (mean difference = 0.555; 95% CI 0.465, 0.645). Participants had greater odds of awareness for ST health effects from 2010 to 2015. DISCUSSION AND CONCLUSIONS: The results suggest that increasing efforts of awareness policy interventions is having a positive effect on tobacco-related knowledge in Bangladesh. These policy initiatives should be continued to identify optimal methods to facilitate behaviour change and improve cessation of smoking and ST use.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".