A Technology-Enhanced Intervention for Violence and Substance Use Prevention Among Young Black Men: Protocol for Adaptation and Pilot Testing
Notice bibliographique
Résumé
BACKGROUND: Black boys and men from disinvested communities are disproportionately survivors and perpetrators of youth violence. Those presenting to emergency departments with firearm-related injuries also report recent substance use. However, young Black men face several critical individual and systemic barriers to accessing trauma-focused prevention programs. These barriers contribute to service avoidance, the exacerbation of violence recidivism, substance use relapse, and a revolving-door approach to prevention. In addition, young Black men are known to be digital natives. Therefore, technology-enhanced interventions offer a pragmatic and promising opportunity to mitigate these barriers, provide vital life skills for self-led behavior change, and boost service engagement with vital community resources. OBJECTIVE: The study aims to systematically adapt and pilot-test Boosting Violence-Related Outcomes Using Technology for Empowerment, Risk Reduction, and Life Skills Preparation in Youth Based on Acceptance and Commitment Therapy (BrotherlyACT), a culturally congruent, trauma-focused digital psychoeducational and service-engagement tool tailored to young Black men aged 15-24 years. BrotherlyACT will incorporate microlearning modules, interactive safety planning tools for risk assessment, goal-setting, mindfulness practice, and a service-engagement conversational agent or chatbot to connect young Black men to relevant services. METHODS: The development of BrotherlyACT will occur in 3 phases. In phase 1, we will qualitatively investigate barriers and facilitators influencing young Black men's willingness to use violence and substance use prevention services with 15-30 young Black men (aged 15-24 years) who report perpetrating violence and substance use in the past year and 10 service providers (aged >18 years; any gender; including health care providers, street outreach workers, social workers, violence interrupters, community advocates, and school staff). Both groups will be recruited from community and pediatric emergency settings. In phase 2, a steering group of topic experts (n=3-5) and a youth and community advisory board comprising young Black men (n=8-12) and service providers (n=5-10) will be involved in participatory design, alpha testing, and beta testing sessions to develop, refine, and adapt BrotherlyACT based on an existing skills-based program (Achieving Change Through Values-Based Behavior). We will use user-centered design principles and the Assessment, Decision, Administration, Production, Topical, Experts, Integration, Training, and Testing framework to guide this adaptation process (phase 2). In phase 3, a total of 60 young Black men will pilot-test the adapted BrotherlyACT over 10 weeks in a single-group, pretest-posttest design to determine its feasibility and implementation outcomes. RESULTS: Phase 1 data collection began in September 2021. Phases 2 and 3 are scheduled to start in June 2023 and end in September 2024. CONCLUSIONS: The development and testing of BrotherlyACT is a crucial first step in expanding an evidence-based psychoeducational and service-mediating intervention for young Black men involved in violence. This colocation of services shifts the current prevention strategy from telling them why to change to teaching them how. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/43842.
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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 | 0,018 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,003 |
| Méta-épidémiologie (sens large) | 0,004 | 0,004 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,004 | 0,002 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,004 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,072 | 0,010 |
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 ».