Integration of Digital Tools Into Community Mental Health Care Settings That Serve Young People: Focus Group Study
Notice bibliographique
Résumé
BACKGROUND: Digital mental health tools have substantial potential to be easily integrated into people's lives and fundamentally impact public health. Such tools can extend the reach and maximize the impact of mental health interventions. Before implementing digital tools in new settings, it is critical to understand what is important to organizations and individuals who will implement and use these tools. Given that young people are highly familiar with technology and many mental health concerns emerge in childhood and adolescence, it is especially crucial to understand how digital tools can be integrated into settings that serve young people. OBJECTIVE: This study aims to learn about considerations and perspectives of community behavioral health care providers on incorporating digital tools into their clinical care for children and adolescents. METHODS: Data were analyzed from 5 focus groups conducted with clinicians (n=37) who work with young people at a large community service organization in the United States. This organization provides care to more than 27,000 people annually, most of whom are of low socioeconomic status. The transcripts were coded using thematic analysis. RESULTS: Clinicians first provided insight into the digital tools they were currently using in their treatment sessions with young people, such as web-based videos and mood-tracking apps. They explained that their main goals in using these tools were to help young people build skills, facilitate learning, and monitor symptoms. Benefits were expressed, such as engagement of adolescents in treatment, along with potential challenges (eg, accessibility and limited content) and developmental considerations (eg, digital devices getting taken away as punishment). Clinicians discussed their desire for a centralized digital platform that securely connects the clinician, young person, and caregivers. Finally, they offered several considerations for integrating digital tools into mental health care, such as setting up expectations with clients and the importance of human support. CONCLUSIONS: Young people have unique considerations related to complex accessibility patterns and technology expectations that may not be observed when adults are the intended users of mental health technologies. Therefore, these findings provide critical insights to inform the development of future tools, specifically regarding connectivity, conditional restraints (eg, devices taken away as punishment and school restrictions), expectations of users from different generations, and the blended nature in which digital tools can support young people.
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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 | 0,012 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,007 | 0,003 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 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 ».