Family and peer effect on young and adolescent smoking in Bangladesh
Bibliographic record
Abstract
Abstract Background: Cigarette smoking among youth has become a major public health problem in many developing countries like Bangladesh. Many smokers start smoking in their teens; then they become addicted and deepen the dependency during early adulthood, maintaining their smoking behaviour. Current literature from Bangladesh has not examined how peer effect, family influence and smoking-related knowledge impact smoking behaviour of the adolescent and young adults from the urban areas of the country. This study aims to fill this gap in the literature by investigating the smoking patterns of urban male adolescents and young adults in Bangladesh. The study aims to examine the family and peer influence on the behaviour of smoking of this population group. The study also explores how knowledge on harmful effects of smoking affects the participation decision of smoking by young adults and adolescents. Methods: This study is an educational institution-based cross-sectional study within the framework of the Global Youth Tobacco Survey (GYTS). Primary data has been collected from a sample of 995 urban male students aged between 10-24 years living in the seven divisional headquarters of Bangladesh. We use logistic regression and count data regression models to examine how smoking is associated with different factors. Results: Our results suggest that a person is 45% more prone to smoke a cigarette if any of the family members is a smoker. The likelihood of smoking is about six times higher if he is offered a cigarette by his friend, whereas the likelihood of being a smoker is 15-fold if he has a smoker friend, as the findings reveal. However, institutional and family awareness are not significantly related to smoking. Conclusion: Strong evidence of peer and family effect on smoking initiation indicates the need for effective smoking-prevention intervention at the national level, specifically targeted at family and educational institutions. The findings are also relevant for other countries which share the similar characteristics of Bangladesh in terms of adolescent smoking determinants.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".