Cyberbullying, mental health, and substance use experimentation among early adolescents: a prospective cohort study
Bibliographic record
Abstract
Background: Although cyberbullying has been linked with adverse health outcomes, most prior studies have been cross-sectional, and there are limited large-scale, prospective analyses examining cyberbullying and mental health and substance use outcomes in early adolescents. Therefore, the aim of this study was to determine prospective associations between cyberbullying, mental health, and substance use experimentation one year later in a US national cohort of early adolescents (11-12 years old). Methods: We analyzed prospective cohort data from the Adolescent Brain Cognitive Development (ABCD) Study (Year 2, N = 9799). Linear and logistic regression analyses were used to determine associations between cyberbullying victimization (exposure variable, Year 2) and mental health (depressive, anxiety, attention, somatic, oppositional defiant, conduct problems, and suicidal behaviours), and substance (alcohol, nicotine, cannabis) use experimentation outcomes (Year 3), adjusting for sociodemographic variables and mental health outcomes, suicidal behaviours, or reported substance use experimentation at Year 2. Findings: The total analysed sample comprised 9799 who were 48.4% female and racially/ethnically diverse (45.1% non-White). 8.7% reported lifetime cyberbullying victimization. Cyberbullying victimization was prospectively associated with higher depressive (β = 0.61, 95% CI 0.02-1.19), somatic (β = 1.00, 95% CI 0.42-1.57), and attention problems (β = 0.52, 95% CI 0.03-1.00), as well as suicidal behaviors (adjusted odds ratio [AOR] 2.62, 95% CI 1.73-3.98) one year later. Cyberbullying victimization was prospectively associated with higher odds of alcohol (AOR 1.98, 95% CI 1.53-2.57), nicotine (AOR 3.37, 95% 2.16-5.26), and cannabis (AOR 4.65, 95% 2.46-8.77) experimentation one year later. While cyberbullying victimization was associated with anxiety, oppositional defiant, and conduct problems in the unadjusted model, this was no longer significant after adjusting for covariates. Interpretation: Given associations with poor mental health and substance use in early adolescents, it is important to develop interventions to prevent and reduce cyberbullying. Pediatricians, parents, and educators can provide mental health support for early adolescent victims of cyberbullying. Funding: This research was supported by the Bill and Melinda Gates Foundation (INV-048897). J.M.N. was funded by the National Institutes of Health (K08HL159350 and R01MH135492) and the Doris Duke Charitable Foundation (2022056).
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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 | 0.001 | 0.000 |
| 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".