Patterns of Adolescent Substance Use: A Comparative Study among the United States, Canada, England, and South Korea from 2014-2021
Bibliographic record
Abstract
Purpose: This study aimed to identify the cross-national estimates of substance use, explore problematic substance use patterns among adolescents across countries and over time, and examine the relationship of individual characteristics on substance use patterns. Methods: This study employed a time-series secondary data analysis spanning from 2014 to 2021 across four countries (United States, Canada, England, and South Korea). We analyzed the usage of five substances (alcohol, binge drinking, cigarettes, electronic cigarettes, and illicit drugs) alongside demographic characteristics. A descriptive analysis was performed to identify estimates of substance use across countries. Latent class analysis was employed to examine adolescents’ substance use patterns across time and countries. A multinomial logistic regression model was fit to assess the relationship between latent class and demographic characteristics. Results: Adolescents in the United States and Canada had the highest substance use, while Korean adolescents had the lowest, particularly concerning drugs. Latent class analysis revealed two classes (light-user and heavy-user), with the addition of a third class (moderate-user) in some instances. The substance use patterns, while differing significantly among nations, exhibited consistency over time. During COVID-19, a decrease in heavy-substance users was observed across countries. Male or older participants were more likely to belong to the heavy-user class. Conclusion: This research offers valuable insights into the variations in substance use patterns between nations and over time. A tailored approach is essential to prevent adolescents from becoming heavy-substance users. This approach should consider country regulations and demographics for a targeted and comprehensive preventive strategy.
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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 | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".