Cybervictimisation and mental health conditions in young people: findings from a nationally representative longitudinal cohort
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».