Time Trends in Peer Violence and Bullying Across Countries and Regions of Europe, Central Asia, and Canada Among Students Aged 11, 13, and 15 from 2013 to 2022
Bibliographic record
Abstract
BACKGROUND: The impact of the COVID-19 pandemic on temporal trends in peer violence and bullying deserves closer scrutiny. The aim of the present study was to examine temporal trends in peer violence and bullying among school-aged children before and after the COVID-19 pandemic. METHODS: Data from the Health Behaviour in School-aged Children (HBSC) surveys (2013/2014-2021/2022) were analyzed to track changes in peer violence and bullying over time. The sample encompassed over 700,000 students aged 11, 13, and 15 from more than 40 countries across Asia, Europe, and North America. RESULTS: Traditional (school) bullying perpetration and victimization did not change significantly over time. A significant decreasing trend in engagement in physical fighting between the 2013/2014 and 2021/2022 surveys was observed among male participants aged 15. In contrast, a significant increasing trend in engagement in physical fighting was observed among female participants aged 11 and 13 years. Following the pandemic, increases in cyberbullying perpetration and victimization were observed among students aged 11 and 13, a trend not evident among 15-year-olds. CONCLUSION: Except for cyberbullying, the pandemic did not appear to influence trends in peer violence and bullying, which remained largely stable or reflected trajectories that had begun prior to the pandemic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".