Usability of a Technology-Based Bystander Bullying Intervention for Middle School Students in Rural, Low-Income Communities: Mixed Methods Study
Notice bibliographique
Résumé
BACKGROUND: Students who are targets of bullying and who witness bullying are at high risk for negative mental health outcomes. Bystander training is essential to reduce bullying and the negative associated consequences for targets and bystanders. Resources necessary for program delivery, however, pose significant barriers for schools, particularly those in rural, low-income communities. Technology-based programs can reduce health disparities for students in these communities through cost-effective, easy-to-disseminate programming. OBJECTIVE: The aim of this study is to conduct usability testing of a bystander bullying web app prototype, STAC-T (technology-based STAC, which stands for the 4 bystander strategies Stealing the Show, Turning it Over, Accompanying Others, and Coaching Compassion) as an initial step in the development of a full-scale STAC-T intervention. Objectives include assessing usability and acceptability of the STAC-T prototype, understanding school needs and barriers to program implementation, and assessing differences in usability between school personnel and students. METHODS: A sample of 16 participants, including school personnel and students recruited from 3 middle schools in rural, low-income communities, completed usability testing followed by a qualitative interview. Descriptive statistics, 2-tailed independent sample t tests, and consensual qualitative research were used to assess usability and program satisfaction and to extract themes related to acceptability, feasibility, needs, barriers, and feedback for intervention development. RESULTS: Usability testing indicated that the app was easy to use, acceptable, and feasible. Both school personnel (mean rating 89.6, SD 5.1) and students (mean rating 91.8, SD 7.0) rated the app well above the standard cutoff score for above-average usability (ie, 68), and both school personnel (mean rating 5.83, SD 0.41) and students (mean rating 6.10, SD 0.57) gave the app high user-friendliness ratings (0-7 scale, with 7 as high user-friendliness). The overall ratings also suggested that school personnel and students were satisfied with the program. Of the 6 school personnel who said they would recommend the program, 1 (17%), 4 (66%), and 1 (17%) rated the program as 3, 4, and 5 stars, respectively; 80% (8/10) of students said they would recommend the program; and 60% (6/10) and 40% (4/10) rated the program as 4 stars and 5 stars, respectively. Qualitative data revealed that school personnel and students found the STAC-T app to be useful, user-friendly, and relevant, while providing feedback related to the importance of digital learning activities that engage the user. Data from school personnel also indicated positive perceptions regarding program feasibility and probability of program adoption, with the most significant barrier being cost, suggesting the importance of considering the financial resources available to schools in rural, low-income communities when setting the price point for the full-scale STAC-T intervention. CONCLUSIONS: This study provides support for the full-scale development of the STAC-T app and provides key information for revision to enhance used engagement. TRIAL REGISTRATION: ClinicalTrials.gov NCT04681495; https://clinicaltrials.gov/ct2/show/NCT04681495.
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 | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».