Cyberbullying Prevention for Adolescents: Iterative Qualitative Methods for Mobile Intervention Design
Notice bibliographique
Résumé
BACKGROUND: Cybervictimization among adolescents is associated with multiple negative mental health consequences. Although pediatricians often screen for cyberbullying, validated and acceptable programs to reduce the frequency and impact of adolescent cybervictimization are lacking. OBJECTIVE: This study uses agile qualitative methods to refine and evaluate the acceptability of a mixed-modality intervention, initiated within the context of usual pediatric care, for adolescents with a history of cyberharassment and cyberbullying victimization. METHODS: Three groups of adolescents were successively recruited from an urban primary care clinic to participate in three consecutive iterations (1, 2, and 3) of the program, which consisted of a brief in-clinic intervention followed by 8 weeks of daily, automated SMS text messaging. After 2 weeks of messaging, iteration 1 (I1) participants completed semistructured interviews regarding intervention experiences. Participant feedback was evaluated via framework matrix analysis to guide changes to the program for iteration 2 (I2). Feedback from 2-week interviews of I2 participants was similarly used to improve the program before initiating iteration 3 (I3). Participants in all 3 iterations completed the interviews after completing the program (8 weeks). Daily response rates assessed participant engagement, and satisfaction questionnaires assessed acceptability. RESULTS: A total of 19 adolescents (aged 13-17 years) reporting past-year cybervictimization were enrolled: 7 in I1, 4 in I2, and 8 in I3. Demographic variables included the following: a mean age of 15 (SD 1.5) years; 58% (11/19) female, 42% (8/19) male, 63% (12/19) Hispanic, 37% (7/19) non-Hispanic, 79% (15/19) people of color, and 21% (4/19) White. A total of 73% (14/19) self-identified as having a low socioeconomic status, and 37% (7/19) self-identified as lesbian, gay, or bisexual. The average past 12-month cybervictimization score at baseline was 8.2 (SD 6.58; range 2-26). Participant feedback was used to iteratively refine intervention content and design. For example, participants in I1 recommended that the scope of the intervention be expanded to include web-based conflicts and drama, rather than narrowly focusing on cyberbullying prevention. On the basis of this feedback, the I2 content was shifted toward more general de-escalation skills and bystander empowerment. Overall, 88.34% (940/1064) of the daily queries sent to participants across all 3 iterations received a reply. Participant satisfaction improved considerably with each iteration; 0% (0/7) of I1 participants rated the overall quality of Intervention to Prevent Adolescent Cybervictimization with Text message as excellent, compared to 50% (2/4) of I2 participants and 86% (6/7) of I3 participants. Engagement also improved between the first and third iterations, with participants replying to 59.9% (235/392) of messages in I1, compared to 79.9% (358/488) of messages in I3. CONCLUSIONS: This study shows the value of structured participant feedback gathered in an agile intervention refinement methodology for the development of a technology-based intervention targeting adolescents.
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 | 0,007 | 0,001 |
| 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 | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».