Culturally Tailored Tele–Mental Health Care Linkage for Indigenous Populations: Protocol for a Mixed Methods Pilot Study
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.041 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.083 | 0.015 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".