Cybervictimisation and mental health conditions in young people: findings from a nationally representative longitudinal cohort
Bibliographic record
Abstract
BACKGROUND: Cybervictimisation has been linked to poor mental health in young people, but doubts remain about the robustness of this association. We examined mental health outcomes for adolescents who experienced cybervictimisation using a genetically informative longitudinal design to strengthen causal inference by accounting for alternative explanations. METHODS: We used data from the Environmental Risk (E-Risk) Longitudinal Twin Study, a nationally representative cohort of 2232 British twins born in 1994-95. We included participants who completed interviews assessing cybervictimisation and mulitple offline forms of victimisation since age 12 years, and a range of mental health conditions at age 18 years. Confounders were measured prospectively from ages 5 years to 18 years. Unmeasured confounders including genetic and shared environmental factors were controlled for using discordant twin analyses. People with lived experience were not involved in this study. FINDINGS: 2066 participants completed assessments at age 18 years, of whom 2063 (99·9%) had data on cybervictimisation. The mean age of the twins at the time of the assessment was 18·4 years (SD 0·4), and 1870 (90·5%) identified as White, 84 (4·1%) as Asian, 40 (1·9%) as Black, eight (0·4%) as mixed race, and 64 (3·1%) as other ethnicities. 419 (20·3%) of 2063 young people reported being moderately or severely cybervictimised between ages 12 years and 18 years, with ten (2·4%) participants reporting online abuse without having experienced offline victimisation. Cybervictimised adolescents were more likely to report generalised anxiety disorder, major depressive disorder, self-harm or suicide attempt, post-traumatic stress disorder, conduct disorder, and psychotic experiences compared with those not cybervictimised. These associations remained after adjusting for confounders, including individual characteristics (sex assigned at birth, minority ethnicity, socioeconomic status, and childhood intelligence quotient), pre-existing vulnerabilities (previous mental health conditions and online and offline victimisation), and concurrent vulnerabilities (problematic digital technology use and loneliness). Offline victimisation accounted for the associations, with modest to substantial attenuation in odds ratios (17·7-28·0% for generalised anxiety disorder and major depressive disorder; 33·5-52·3% for other outcomes). Cybervictimisation was uniquely associated with generalised anxiety disorder independently of genetic and shared environmental factors and offline victimisation (odds ratio 2·14 [95% CI 1·18-3·88]). INTERPRETATION: Amid ongoing policy debates on digital safety and to support targeted intervention strategies, mental health responses to cybervictimisation should consider the broader context of victimisation experienced by young people. FUNDING: UK Medical Research Council, US National Institute of Child Health and Human Development, and Jacobs Foundation.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".