Cyberbullying Victimisation Was Associated With Greater Manic Symptoms in Early Adolescence: A Prospective Cohort Study
Bibliographic record
Abstract
AIM: Cyberbullying has been linked to various adverse psychological outcomes, but prospective associations with manic symptoms in early adolescents remain unexplored. We examined the prospective relationship between cyberbullying victimisation and manic symptoms in a diverse cohort of American children and adolescents. METHOD: We analysed data from the Adolescent Brain Cognitive Development study from the period 2 follow-up (2018-2020) to the period 3 follow-up (2019-2021). Linear regression models estimated the associations between cyberbullying victimisation (lifetime and past 12 months) and manic symptoms. We adjusted for age, sex, race and ethnicity, household income, parental education, manic symptoms, anxiety, depression, total screen time, and study site. RESULTS: The average age of our 9095 adolescents (51.3% male) was 12.0 ± 0.7 years. The prevalence of cyberbullying victimisation was 9.2% for lifetime and 6.1% for the past 12 months. Adjusting for the covariates, lifetime cyberbullying victimisation was associated with a 0.41 higher manic symptom sum score (95% CI 0.18-0.65, p = 0.001), and past 12-month cyberbullying victimisation was associated with a 0.38 higher manic symptom sum score (95% CI 0.11-0.66, p = 0.007). CONCLUSION: These findings highlight the need for early identification and intervention for adolescents experiencing cyberbullying to mitigate its adverse effects on mental health.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".