Bibliographic record
Abstract
Introduction Heated tobacco products (HTPs) have been available in the Korean market since June 2017. In this study, we examined the prevalence and predictors of HTP use among Korean adolescents and the association between HTP and electronic cigarette (EC) use and attempts to quit conventional cigarette (CC) smoking. Methods We analysed the data of a representative sample (n=60 040) of 13–18-year-old middle-school and high-school students in Korea who had participated in the 14th Korea Youth Risk Behavior Web-based Survey in 2018. Results The prevalence of ever HTP use among Korean adolescents was 2.9% (men: 4.4%, women: 1.2%), a year after the introduction of HTPs in the Korean market. Furthermore, 81.3% of the 1568 ever HTP users were triple users of HTPs, ECs and CCs. Multivariate analysis revealed that ever HTP use was greater among men, higher-grade students, current CC and/or EC users and risky alcohol drinkers. Among current CC smokers, ever users of ECs (28%–30%) and ever HTP users and current EC users (48%) were more likely to have attempted to quit CC smoking than those who had never used HTPs and ECs. However, there were fewer HTP and/or EC ever users among ever CC smokers who successfully quit smoking. Conclusions Many adolescents, especially CC and EC users, had already used HTPs shortly after the introduction of HTPs in Korea. The use of newer types of tobacco products is associated with lower odds of abstinence from CCs; therefore, it is important to protect adolescents from them.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".