Trends in Indicators of Violence Among Adolescents in Europe and North America 1994–2022
Bibliographic record
Abstract
Objectives: To describe age and gender specific time trends in adolescent violence across 19 countries over 28 years. Methods: The paper presents analysis of eight cycles of the Health Behaviour in School-aged Children (HBSC) Study from 1994-2022, involving 789,531 children aged 11, 13, and 15. Indicators of violence included physical fighting, school bullying and cyberbullying (from 2018). Log-binomial regression models were used to test for linear temporal trends, with Generalized Estimating Equations used to account for clustering by country. Results: School bullying perpetration and victimization declined over time in each age/gender group in most countries. Similar declines were reported for frequent physical fighting among boys (all ages) and girls (age 15 only). The prevalence of violent behaviour was almost universally higher in boys in the early cycles than in girls, but this gender difference attenuated over time. For cyberbullying, significant increases were observed since 2018 in all groups except age 15 girls in most countries. Conclusion: This analysis of a large cross-national dataset suggests a decline in traditional forms of adolescent violence. However, the increases in cyberbullying warrant further monitoring.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 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".