Health knowledge and smokeless tobacco quit attempts and intentions among married women in rural Bangladesh: cross-sectional survey
Bibliographic record
Abstract
Introduction and Aims: The aim of this study was to investigate health knowledge, attitudes and smokeless tobacco quit attempts and intentions among married women in rural Bangladesh. Design and Methods: A cross-sectional survey was conducted using an interviewer administered, pretested, semistructured questionnaire. All 8082 women living in the Jhaudi and Ghotmajhee local government areas, aged =18 years with at least one pregnancy in their lifetime, were invited to participate. Questions covered smokeless tobacco consumption (STC), knowledge regarding its health effects, users' quit attempts and intentions and sociodemographic characteristics. Results: Eight thousand seventy-four women completed the survey (response rate 99.9%). Almost half (45%) of current consumers thought STC was good for their health and many ascribed medicinal values to it, for example 25% thought STC reduced stomach aches. A quarter had previously tried to quit and 10% intended to quit. After adjusting for potential confounders, inaccurate knowledge of STC health consequences was associated with being older [adjusted odds ratio (aOR) = 2.71, 95% confidence interval (CI) 1.99–3.50], less educated (aOR = 2.18, 95% CI 1.66–2.85), Muslim (aOR = 17.0, 95% CI 12.0–23.9) and unemployed (aOR = 29.7, 95% CI: 25.2–35.1). Having less education (aOR = 2.52, 95% CI 0.98–6.45) and being unemployed (aOR = 1.52, 95% CI 1.03–2.23) were associated with the intention to quit. Discussion and Conclusions: Large gaps exist in rural Bangladeshi women's understanding of the adverse health effects of STC. Health awareness campaigns should highlight the consequences of STC. Routine screening and cessation advice should be provided in primary healthcare and smokeless tobacco control strategies should be implemented.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".