Perceptions and reasons for quitting and transitioningbetween smoking and smokeless tobacco products: Findingsfrom four waves of the ITC Bangladesh survey
Bibliographic record
Abstract
INTRODUCTION: Transitions between different tobacco products are frequent among tobacco users in Bangladesh; however, the reasons leading to such transitions and why they quit are not well researched. The aim of the study is to examine perceptions and reasons reported by tobacco users in Bangladesh to transition to other products or quit. METHODS: Data from four waves (2009-2015) of the International Tobacco Control (ITC) Bangladesh Survey were used. Repeated data on perceptions and reasons for exclusive cigarette (n=520), bidi (n=130), and SLT users (n=308) to either start using other products or quit were analyzed with sampling weights. The percentages of responses across waves were used to calculate the pooled proportion data using a meta-analysis approach. RESULTS: Common reasonsig for respondents switching to other tobacco products were influence of friends/family (73.8-86.0%), and curiosity (44.4-71.3%). The perceived calming effect of smoking cigarettes and bidis (43.2-56.9%), and the impression that bidis were less harmful (52.3%) and taste better (71.2%) were major reasons for exclusive SLT users to switch products. Health concerns (16.5-62.7%) and disapproval from friends/family (29.8-56.4%) were generally the main reasons for quitting. For smoked tobacco users, doctor's advice (41.6%), package warning labels (32.3%), and price (32.4%) seemed to be the major driving factors to quit. CONCLUSIONS: Results highlight that the reasons for switching between tobacco products and quitting include social factors (e.g. friends/family) and (mis) perceptions regarding the products. Tobacco control policy could emphasize cessation support, increased price and education campaigns as key policies to reduce overall tobacco use in Bangladesh. Data from four waves (2009-2015) of the International Tobacco Control (ITC) Bangladesh Survey were used. Repeated data on perceptions and reasons for exclusive cigarette (n=520), bidi (n=130), and SLT users (n=308) to either start using other products or quit were analyzed with sampling weights. The percentages of responses across waves were used to calculate the pooled proportion data using a meta-analysis approach.
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 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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".