Cross-national time trends in bullying victimization in 33 countries among children aged 11, 13 and 15 from 2002 to 2010
Bibliographic record
Abstract
BACKGROUND: Bullying among children and adolescents is a public health concern; victimization is associated with psychological and physical health problems. The purpose of this study is to examine temporal trends in bullying victimization among school-aged children in Europe and North America. METHODS: Data were obtained from cross-sectional self-report surveys collected as part of the Health Behaviour in School-aged Children (HBSC) study from nationally representative samples of 11-, 13- and 15-year-olds, from 33 countries and regions which participated in the 2001-02, 2005-06 and 2009-10 surveys. Responses from 581 838 children were included in the analyses. Binary logistic regression was used for the data analyses. RESULTS: The binary logistic regression models showed significant decreasing trends in occasional and chronic victimization between 2001-02 and 2009-10 across both genders in a third of participating countries. One country reported significant increasing trends for both occasional and chronic victimization. Gender differences in trends were evident across many countries. CONCLUSION: Overall, while still common in many countries, bullying victimization is decreasing. The differences between countries highlight the need to further investigate measures undertaken in countries demonstrating a downward trend.
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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 | 0.001 | 0.002 |
| 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".