A Cyberbullying Media-Based Prevention Intervention for Adolescents on Instagram: Pilot Randomized Controlled Trial
Notice bibliographique
Résumé
BACKGROUND: Between 15% and 70% of adolescents report experiencing cybervictimization. Cybervictimization is associated with multiple negative consequences, including depressed mood. Few validated, easily disseminated interventions exist to prevent cybervictimization and its consequences. With over 97% of adolescents using social media (such as YouTube, Facebook, Instagram, or Snapchat), recruiting and delivering a prevention intervention through social media and apps may improve accessibility of prevention tools for at-risk youth. OBJECTIVE: This study aims to evaluate the feasibility and acceptability of and obtain preliminary outcome data on IMPACT (Intervention Media to Prevent Adolescent Cyber-Conflict Through Technology), a brief, remote app-based intervention to prevent and reduce the effect of cyberbullying. METHODS: From January 30, 2020, to May 3, 2020, a national sample of 80 adolescents with a history of past-year cybervictimization was recruited through Instagram for a randomized control trial of IMPACT, a brief, remote research assistant-led intervention and a fully automated app-based program, versus enhanced web-based resources (control). Feasibility and acceptability were measured by consent, daily use, and validated surveys. Although not powered for efficacy, outcomes (victimization, bystander self-efficacy, and well-being) were measured using validated measures at 8 and 16 weeks and evaluated using a series of longitudinal mixed models. RESULTS: Regarding feasibility, 24.5% (121/494) of eligible participants provided contact information; of these, 69.4% (84/121) completed full enrollment procedures. Of the participants enrolled, 45% (36/80) were randomized into the IMPACT intervention and 55% (44/80) into the enhanced web-based resources groups. All participants randomized to the intervention condition completed the remote intervention session, and 89% (77/80) of the daily prompts were answered. The retention rate was 99% (79/80) at 8 weeks and 96% (77/80) at 16 weeks for all participants. Regarding acceptability, 100% (36/36) of the intervention participants were at least moderately satisfied with IMPACT overall, and 92% (33/36) of the participants were at least moderately satisfied with the app. At both 8 and 16 weeks, well-being was significantly higher (β=1.17, SE 0.87, P=.02 at 8 weeks and β=3.24, SE 0.95, P<.001 at 16 weeks) and psychological stress was lower (β=-.66, SE 0.08, P=.04 at 8 weeks and β=-.89, SE 0.09, P<.001 at 16 weeks) among IMPACT users than among control group users. Participants in the intervention group attempted significantly more bystander interventions than those in the control group at 8 weeks (β=.82, SE 0.42; P=.02). CONCLUSIONS: This remote app-based intervention for victims of cyberbullying was feasible and acceptable, increased overall well-being and bystander interventions, and decreased psychological stress. Our findings are especially noteworthy given that the trial took place during the COVID-19 pandemic. The use of Instagram to recruit adolescents can be a successful strategy for identifying and intervening with those at the highest risk of cybervictimization. TRIAL REGISTRATION: ClinicalTrials.gov NCT04259216; http://clinicaltrials.gov/ct2/show/NCT04259216.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,012 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».