Prevalence, Motivations, and Social, Mental Health and Health Consequences of Cyberbullying Among School-Aged Children and Youth: Protocol of a Longitudinal and Multi-Perspective Mixed Method Study
Notice bibliographique
Résumé
BACKGROUND: While the online environment may promote important developmental and social benefits, it also enables the serious and rapidly growing issue of cyberbullying. Cyberbullying constitutes an increasing public health problem - victimized children and youth experience a range of health and mental health concerns, including emotional and psychosomatic problems, maladaptive behaviors, and increased suicidality. Perpetrators demonstrate a lack of empathy, and may also struggle with health and mental health issues. OBJECTIVE: This paper describes the protocols applied in a longitudinal and multi-perspective mixed-methods study with five objectives: (1) to explore children/youth's experiences, and children/youth's, parents', and teachers' conceptions, definitions, and understanding of cyberbullying; (2) to explore how children/youth view the underlying motivations for cyberbullying; (3) to document the shifting prevalence rates of cyberbullying victimization, witnessing, and perpetration; (4) to identify risk and protective factors for cyberbullying involvement; and (5) to explore social, mental health, and health consequences of cyberbullying. METHODS: Quantitative survey data were collected over three years (2012-2014) from a stratified random baseline sample of fourth (n=160), seventh (n=243), and tenth (n=267) grade children/youth, their parents (n=246), and their teachers (n=103). Quantitative data were collected from students and teachers during in-person school visits, and from parents via mail-in surveys. Student, parent, and teacher surveys included questions regarding: student experiences with bullying/cyberbullying; student health, mental health, and social and behavioral issues; socio-demographics; and information and communication technology use. In-depth semi-structured qualitative interviews were conducted twice with a sub-sample of students (n=57), purposively selected based on socio-demographics and cyberbullying experience, twice with their parents (n=50), and once with their teachers (n=30). RESULTS: Data collection for this study is complete. Planned analyses include transition probabilities and repeated measures analyses to determine involvement in cyberbullying. Repeated measures analyses, including between-subject factors (eg, socio-demographics), will be utilized to determine factors that protect or increase risk of involvement in cyberbullying. Qualitative analysis utilizing grounded theory is planned, to permit rich understanding of participant experiences and perspectives. Results will be reported in 2016 and 2017. CONCLUSIONS: This study will offer insight into the contemporary phenomenon of cyberbullying while also informing interventions to curb cyberbullying and address its pervasive social, mental health, and health consequences. Knowledge mobilization strategies and implications for research and practice are discussed.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,004 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».