Heritability of Bullying and Victimization in Children and Adolescents: Moderation by the KiVa Antibullying Program
Bibliographic record
Abstract
OBJECTIVE: Bullying affects approximately a quarter of schoolchildren and is associated with numerous adverse outcomes. Although distinct risk factors for bullying and victimization have been identified, few studies have investigated the genetic and environmental underpinnings of bullying and victimization. The aims of this study were twofold: first, to examine the contributions of genetic and environmental factors to bullying and victimization, and second, to analyze whether the KiVa antibullying program moderated the magnitude of these contributions by comparing estimates derived from the KiVa versus control groups. METHOD: The sample comprised students from schools that participated in the evaluation of the KiVa antibullying program in Finland during 2007-2009. Bullying and victimization were measured using peer nominations by classmates. The sample for the twin analyses comprised of 447 twins (107 monozygotic and 340 dizygotic twins) aged 7-15. RESULTS: Genetic contributions accounted for 62% and 77% of the variance in bullying and in victimization at pre-intervention, respectively. There was a post-intervention difference in the overall role of genetic and environmental contributions between the intervention and the control group for bullying and victimization, with non-shared environmental effects playing a lesser role (and genes a larger role) in the intervention than in the control group context. CONCLUSIONS: This study replicates previous findings on the genetic underpinnings of both bullying and victimization, and indicates that a school-based antibullying program reduces the role of non-shared environmental factors in bullying and victimization. The results indicate that prevention and intervention efforts need to target both environmental and (heritable) individual level factors to maximize effectiveness.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".