The Impact of Heated Tobacco Products on Smoking Cessation, Tobacco Use, and Tobacco Sales in South Korea
Bibliographic record
Abstract
Heated tobacco products (HTPs), a hybrid between conventional and electronic cigarettes, were first launched in South Korea in June 2017. Owing to advertisements stating that HTPs are odorless, tar-free, and less harmful to health, the sales of HTPs have grown quickly enough to account for about 10% of the total tobacco market in a year. HTP use by young, highly educated, and high-income groups had a significant impact on both the overall tobacco market over the past 3 years and the smoking and quitting behaviors of smokers. Based on national smoking rate statistics, tobacco sales trends, and the number of visitors to smoking cessation clinics, the following changes have been identified: (1) The decline in current smoking rates has slowed or rose in some groups. (2) The decline in total cigarette sales has slowed but rose again in the first quarter of 2020. (3) The number of visitors to smoking cessation clinics decreased just after the advent of HTPs. These results may be due to the insufficient support of tobacco regulation policies but also coincide chronologically with the appearance of HTPs in South Korea. It is necessary to investigate the usage rate of various tobacco products, including HTPs and e-cigarettes, to examine the health risks of novel tobacco products and provide accurate information to users and policymakers. Finally, tobacco companies are continuously developing new product concepts to escape the regulation of existing cigarettes; thus, comprehensive management measures for all nicotine-containing products are needed.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".