Text Messaging to Enhance Behavioral Health Treatment Engagement Among Justice-Involved Youth: Qualitative and User Testing Study
Notice bibliographique
Résumé
BACKGROUND: Mental health and substance use disorders are highly prevalent in justice-involved youth, yet only 8% of court-involved, nonincarcerated (CINI) youth in need of treatment receive it. Dual diagnosis (co-occurring psychiatric and substance use disorders) in justice-involved youth is highly predictive of recidivism. Identifying novel approaches, such as the use of mobile health (mHealth) technologies, to close this gap between need and receipt of behavioral health treatment for the CINI population could potentially offset rates of reoffending into adulthood. Text-messaging (short message service, SMS) interventions have demonstrated efficacy in improving treatment adherence and other associated outcomes in other vulnerable youth populations, but development and testing of mHealth interventions to improve behavioral health treatment rates and outcomes for CINI youth are lacking. OBJECTIVE: This study aimed to collect qualitative data from key stakeholders to inform the development of a theoretically grounded, family-based text-messaging (SMS) intervention targeting CINI youth's behavioral health treatment engagement; additionally, the aim was to conduct end-user testing over 6 months with CINI youth and caregivers to determine intervention feasibility and acceptability. METHODS: CINI youth and caregivers were referred from a California-based Juvenile Probation Department and community-based provider organizations providing services for justice-involved youth. Eligibility criteria included the following: being a justice-involved youth or a caregiver of a justice-involved youth, English speaking, youth aged 13 to 17 years old and either referred to or currently attending mental health or substance use treatment, and youth and caregiver have access to a cell phone with text-messaging capability. RESULTS: Overall, 28 individuals participated in focus groups and interviews-8 youth, 5 caregivers, and 15 juvenile justice (JJ) personnel. Three major themes emerged: (1) texting among JJ personnel and CINI youth and caregivers in their caseload is common but not systematic, (2) stigma and privacy are perceived as barriers to texting youth about behavioral health treatment appointments, and (3) messages should be short, simple, relatable, positive, and personalized. In total, 9 participants (7 youth and 2 caregivers) participated in end-user testing and rated the intervention as useful, helpful, and supportive. CONCLUSIONS: Text messaging (SMS) is an acceptable and feasible means of reminding CINI youth to attend behavioral health treatment appointments. Future implementation challenges include making text messaging (SMS) personalized and tailored but not resource intensive (eg, requiring one-to-one, 24/7 human contact) and identifying which systems will deliver and sustain the intervention. Text messaging (SMS) among justice personnel, youth, and their caregivers is already widespread, but lack of clear guidelines about privacy, confidentiality, and information sharing poses ethical conundrums. Future hybrid-type research designs that explore the efficacy of the intervention while also studying ethical, system, and policy-level factors associated with using digital health interventions to improve CINI youth outcomes is a key next step.
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 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 | 0,013 | 0,020 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,002 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| 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 ».