Culturally Tailored Tele–Mental Health Care Linkage for Indigenous Populations: Protocol for a Mixed Methods Pilot Study
Notice bibliographique
Résumé
BACKGROUND: Urban Indigenous populations face disproportionate mental health challenges, including high rates of posttraumatic stress disorder, depression, and substance use disorders, yet they have limited access to health services, especially culturally relevant care. The mechanism for providing care to Indigenous people in the United States, the Indian Health Service, is significantly underfunded and only accessible to certain Indigenous people. With more than 70% of Indigenous individuals in the United States living in urban settings, there is a growing need for innovative health care solutions. A community-based, Indigenous-led health and mental health-focused nonprofit in the northeast United States developed ShockTalk, a tele-mental health linkage-to-care app tailored specifically for Indigenous communities, to fill this gap. OBJECTIVE: This study aims to assess (1) the feasibility, including accessibility, experience, and value, of ShockTalk for practitioners and clients interested in providing or receiving culturally responsive mental health treatment, and (2) changes in client attitudes related to tele-mental health treatment value and trust in ShockTalk technology. METHODS: This study outlines the development and pilot study of ShockTalk. The conceptual framework is based on the behavioral model of health care use. ShockTalk uses artificial intelligence to connect clients with Indigenous or culturally aligned therapists and facilitates access to care via Facebook Messenger. Using a prewaitlist or postwaitlist design, 5 client participants will be admitted to the study at first, and 5 additional participants at 3 months. Data collection includes presurveys and postsurveys on client attitudes toward mental health treatment and trust in the ShockTalk platform at baseline and a 3-month follow-up, followed by in-depth qualitative interviews at 3 months. A preliminary economic evaluation will track direct costs (ie, therapist time, platform fees, and administrative expenses) and compare relative costs across treatment doses. Analyses will assess the feasibility of data collection and inform a future full-scale trial. RESULTS: This study was funded in April 2022. Data collection occurred between May 2022 and October 2024. In total, 4 client participants and 2 therapist participants were enrolled. Data analysis is complete and results are expected to be published in February 2026. CONCLUSIONS: This pilot study will offer insights into optimizing technology-based, culturally relevant mental health care. By examining varying levels of engagement and associated costs, this research seeks to identify the most effective and cost-efficient strategies for improving mental health outcomes in urban Indigenous populations in the United States. ShockTalk has the potential to shape future health care innovations in this field. Findings are expected to contribute significantly to Indigenous mental health care by offering insights into sustainable, accessible, and culturally appropriate telehealth interventions, guiding future policy and practice. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/67757.
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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 | 0,041 | 0,025 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,003 |
| Méta-épidémiologie (sens large) | 0,004 | 0,003 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,006 | 0,003 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,004 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,083 | 0,015 |
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 ».