An analysis of e-cigarette and polysubstance use patterns of adolescents in Bangkok, Thailand
Bibliographic record
Abstract
INTRODUCTION: The prevalence of adolescent e-cigarette use has increased markedly in recent years. Specifically, the prevalence of e-cigarette use over the past 30 days was higher than the prevalence of use of other tobacco products. However, there is no definitive data on e-cigarette use among adolescents, including a description of how e-cigarette use is part of a more widespread pattern of substance abuse. The objective of this study was to assess the prevalence of e-cigarette use in combination with tobacco, alcohol, or marijuana, and the risk of polysubstance use among a sample of Thai adolescents, analyzed by sociodemographic characteristics. METHODS: Data were extracted from the Bangkok Behavioral Surveillance Survey (BBSS) cross-sectional survey conducted in 2019. The survey used self-reports from a sample of adolescents aged 14-17 years in Bangkok (n=6167). Multinomial logistic regression was used to determine the status of poly drug use in combination with e-cigarettes. RESULTS: In all, 6.8% of adolescents in this sample reported having used e-cigarettes in the last 30 days. Among the students who used e-cigarettes, the majority (72.0%) reported using other substances along with e-cigarettes, and alcohol was the most common addictive substance used in combination with e-cigarette use. The use of e-cigarettes only and e-cigarettes in combination with other addictive substances (compared to the non-e-cigarette group) tended to be higher among male students, having low academic achievement, having a friend who smokes, being persuaded by a close friend, having ever had sex (OR: 1.48-3.70), and having close friends who drink alcohol (vs none) (OR=3.26). CONCLUSIONS: Polysubstance use is highly prevalent among adolescents who use e-cigarettes. There should be extensive screening for e-cigarette consumption, including use of other addictive substances, especially alcohol. Early and comprehensive prevention efforts to reduce the use of e-cigarettes and other addictive substances can have a huge impact on the health of the adolescent population.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".