Adverse childhood experiences and early adolescent cyberbullying in the United States
Bibliographic record
Abstract
INTRODUCTION: With the increasing use of social media and online platforms among adolescents, the relationship between traumatic life events and cyberbullying remains unclear. This study aimed to determine the associations between adverse childhood experiences (ACEs) and cyberbullying victimization among a racially/ethnically and socioeconomically diverse sample of early adolescents. METHODS: We analyzed longitudinal data from 10,317 participants in the Adolescent Brain Cognitive Development (ABCD) study, baseline (2016-2018, ages 9-10 years) to Year 2. Logistic regression analyses were used to estimate associations between ACEs and cyberbullying victimization, adjusting for sex, race/ethnicity, country of birth, household income, parental education, and study site. RESULTS: In the sample (48.7% female, 46.0% racial/ethnic minority), 81.3% of early adolescents reported at least one ACE, and 9.6% reported cyberbullying victimization. In general, there was a dose-response relationship between the number of ACEs and cyberbullying victimization, as two (adjusted odds ratio [AOR]: 1.45, 95% confidence interval [CI]: 1.13-1.85), three (AOR: 2.08, 95% CI: 1.57-2.74), and four or more (AOR: 2.37, 95% CI: 1.61-3.49) ACEs were associated with cyberbullying victimization in adjusted models. In models examining the specific type of ACE, sexual abuse (AOR: 2.27, 95% CI: 1.26-4.11), physical neglect (AOR: 1.61, 95% CI: 1.24-2.09), and household mental health problems (AOR: 1.39, 95% CI: 1.18-1.65) had the strongest associations with cyberbullying victimization. CONCLUSION: Adolescents who have experienced ACEs are at greater risk for experiencing cyberbullying. Interventions to prevent cyberbullying could use a trauma-informed framework, including inter-peer interventions to break this cycle of trauma.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".