Cross-national patterns of traditional and cyberbullying: The role of individual and social support indicators
Bibliographic record
Abstract
This study examines victimization at multiple levels. At the individual level, we investigate the consistency of the relationship between individual-level factors and both traditional and cyberbullying victimization. At the country level, we explore ranking patterns in the link between risk or protective factors and victimization outcomes and compare the prevalence of bullying and cyberbullying victimization and offending across countries. We also analyzed the associations between social support indicators gender inequality index (GII), inequality-adjusted human development indices (IHDIs), and victimization rates. We analyzed the 2017–2018 Health Behavior in School-aged Children (HBSC) survey data from the World Health Organization (WHO) across 38 countries. The survey inquired about traditional and cyberbullying, nutrition, physical activity, and family and friend relationships. We employed multiple imputation by chained equations (MICE) to impute missing data and used conditional marginal effects to assess the patterns of relationships. The model fit indices were used to compare the associations between social support indicators and country-level outcomes. Our research indicates that bullying/cyberbullying victimization is associated with offending, poor health, and relational quality, and these associations vary at the country level. Country-level data confirm the overlap between online and offline bullying behaviors, and the gender inequality index (GII) is a stronger correlate of country-level victimization than IHDI. Our findings suggest that student-level bullying prevention programs should incorporate strategies that focus on both risk and protective factors. In addition, programs or policies from countries with more favorable outcomes can serve as models for other countries to follow. In addition, findings imply that reducing gender inequality may be more effective in lowering bullying and cyberbullying rates than focusing on the IHDI. It is important that we continue exploring individual, community, and national indicators of risk to mitigate both traditional and cyber forms of victimization and offending worldwide.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".