E-cigarette: Threat of new dimension of tobacco - marketing, distribution and availability in Dhaka city
Bibliographic record
Abstract
Objectives Currently 37.8 million peoples are using tobacco in Bangladesh. Current user of electronic cigarettes in Bangladesh is 0.2 %. Overall 66.2 % adults both men and women current use tobacco but thinking about quitting. The objectives of the study were to identify the market, import, distribution, promotion strategy and pricing policy of e-cigarette in Bangladesh. Methods Cross sectional study design, qualitative and quantitative approaches, purposive sampling techniques were used to conduct the study. Semi-structured Questioner was used collect the primary data and Observational methods and in-depth interview were used to collect qualitative information. Results The study found that most of the E-cigarette shops are situated nearby university area. The team found 15 big e-cigarette shops in 4 market area most of the seller started their business within 1-2 years. They sell their product mostly in two ways - one is direct sales and in the other they use online shopping system. Average device was found between 6 to10 types and the price between 550 to 35,000 BDT and the liquid price is 350 to 24,000 BDT. Their market has been rapidly expanding using aggressive marketing tactics. Conclusions Bangladesh is the first signatory country of FCTC but still now e-cigarette is un-regulated product and Tobacco Control Law has no specific definition regarding e-cigarette that’s why the company aggressively marketing their products. So, Government should ban importing and amendment the law for protecting peoples from danger of tobacco.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".