Implementation Considerations for Family-Based Telehealth Interventions for Youth in Foster Care: Focus Group Study With Child Welfare System Professionals
Notice bibliographique
Résumé
BACKGROUND: Between 2016 and 2020, over 600,000 youth were served annually by the foster care system. Despite approximately half of foster youth struggling with emotional or behavioral challenges, few receive much-needed services to address their mental health concerns. Family-based interventions are efficacious in addressing both youth and caregiver mental health needs; however, foster youth participation in these family-based interventions is limited by many barriers, including out-of-home placement far from their family of origin. Telehealth is a promising tool for mitigating barriers to access to treatment interventions for foster youth and their families. OBJECTIVE: This study aims to understand child welfare system professionals' perspectives on enabling factors and barriers to providing family-based interventions via telehealth to youth in out-of-county foster care placement. METHODS: This qualitative study derived themes from 3 semistructured focus groups with child welfare system professionals. Participants were asked to discuss how family-based interventions are delivered to foster youth and their caregivers in their jurisdictions, as well as to share their thoughts about how to use telehealth to improve access to family-based interventions for families with youth in out-of-home placement. Data were analyzed using constant comparative analysis and inductive thematic analysis, with the Behavioral Model for Vulnerable Populations as the theoretical framework. RESULTS: Participants were 19 child welfare system professionals (eg, social workers, residential treatment staff, and supervisors) who participated in 1 of the 3 focus groups (6-7/group). Most participants were women (n=13, 68%), White individuals (n=10, 53%), and social workers (n=8, 42%). On average, participants worked in the child welfare system for 16.6 (SD 8.3) years. Participants identified multilevel factors impacting family-based intervention delivery including environmental factors (eg, Medicare billing and presumptive transfer), predisposing characteristics (eg, psychological resources), enabling factors (eg, transportation and team-based youth-centered care), and need factors (eg, motivation to engage). Participants expressed optimism that telehealth could increase access to needed mental health care, diverse providers, and longevity of care while also expressing some concerns regarding telehealth access and literacy. CONCLUSIONS: Child welfare system professionals highlight the need to develop policies and telehealth interventions that are youth versus placement centered, include resources that limit barriers and bolster motivation for engagement, and follow a team-based care model. Findings from this study inform how telehealth can be used to increase access to and engagement with family-based interventions for youth in out-of-home placements and their caregivers of origin.
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Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,015 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,011 | 0,003 |
| Communication savante | 0,003 | 0,002 |
| 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 ».