Waterpipe Tobacco Smoking and Associated Risk Factors among Bangladeshi University Students: An Exploratory Pilot Study
Bibliographic record
Abstract
Abstract Over the past two decades, there has been a global rise in the prevalence of waterpipe tobacco smoking. Waterpipe tobacco smoking involves the inhalation of heated tobacco smoke after passing through water, and it has been associated with an identified dependence effect similar to that found with cigarette smoking. Despite the popularity of waterpipe tobacco among youth (and in particular, university students) in many countries, detailed data of its usage are lacking in Bangladesh. Therefore, the present study was conducted to explore waterpipe tobacco smoking behavior and normative beliefs among university students in Bangladesh and to assess the factors associated with waterpipe tobacco use. A quantitative cross-sectional survey was carried out among 340 Bangladeshi university students (64.4% male; mean age 21.6 years). Among participants, 13.5% reported they had ever smoked tobacco from a waterpipe and 9.4% had it in past 30 days. Among past 30-day users, 72% were categorized as having waterpipe smoking dependence (n = 23). No females in the sample had ever smoked using a waterpipe. Maternal occupation, monthly expenditure, and regular smoking status were major predominant factors associated with waterpipe smoking behavior of the students. The study is of existential value given that there are no prior studies ever carried out in Bangladesh previously. Recommendations are provided based on the study’s findings, particularly in relation to what action is needed from universities in Bangladesh.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.004 | 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".