Electronic cigarette use and smoking cessation behavior among adolescents in China
Bibliographic record
Abstract
OBJECTIVE: China produces the majority of the world's electronic cigarettes (e-cigarettes) and e-cigarettes have become popular in the country, especially among young people. However, little is known about the characteristics of e-cigarette use in China and how it is associated with smoking cessation behavior. This study focuses on the adolescent group in China and examines their perception and use of e-cigarettes and the association with smoking abstinence. METHODS: We use a mobile app-based survey on smoking behavior conducted in November 2015 in China, and focus on a sample of 2042 adolescents aged between 12 and 18. Awareness, perception, use of e-cigarettes are examined as well as the behaviors of promoting e-cigarettes and of smoking cessation. A logistic regression is performed to test the association between e-cigarette use and smoking abstinence behavior. RESULTS: In 2015, nearly 90% of the surveyed adolescents in China were aware of e-cigarettes, while over a quarter of the respondents were ever users. The odds ratio for ever users of e-cigarettes to have tried to quit smoking conventional cigarettes was 1.60 that of never users. For those who tried to quit smoking, 36.02% indicated that they used e-cigarettes to help quit. However, only 13.52% of those who had used e-cigarettes to help quit smoking were successful in quitting. CONCLUSIONS: This study is one of the first empirical research on e-cigarette use among Chinese adolescents. E-cigarettes are widely known and quite popular among Chinese adolescents. As the association between e-cigarette use and smoking cessation behavior is less than clear, more empirical research is called for to help form evidence-based regulatory policy on e-cigarettes in China.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".