Trends in Adolescent Substance Use: Analysis of HBSC Data for Four Eastern European Countries, 1994–2018
Bibliographic record
Abstract
The aim of the study was to analyze the trends of adolescent substance use in four eastern European countries over the time period from 1994 to 2018. The four countries in focus were selected based on their shared historical backgrounds and major economic and social transformations experienced. METHODS: Two decades (1993/1994-2017/2018) of repeated cross-sectional data from the Estonian, Latvian, Lithuanian, and Polish Health Behaviour in School-aged Children survey were used. Data comprised 42,169 school children 15 years of age (9th grade). The following categories of substance use were included: regular alcohol consumption and drunkenness, tobacco smoking, electronic cigarette smoking, and cannabis use. Trends in substance use over time were tested using Jonckheere's trend test. RESULTS: Prevalence of substance use among adolescents over time revealed that the Baltic states and Poland have faced relatively different temporal trends. In the Baltic states, there was a general increase during the period of 1994-2002, which was followed by a period of peaking or stability between 2002-2010, and then decreasing trends of these risky behaviors from 2010 onwards. In Poland, the same period had less consistent patterns, with decreasing trends starting much earlier on. The prevalence of cannabis use, which had been measured since 2006, had its own unique pattern with many fluctuations within and between countries. CONCLUSIONS: The findings on the prevalence of substance use among adolescents from 1994 to 2018 revealed that the Baltic states and Poland have faced relatively different temporal trends. These countries might be facing new public health challenges in a near future, e.g., use of electronic cigarettes and cannabis use among adolescents.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".