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Exploring the Credibility of Large Language Models for Mental Health Support: Protocol for a Scoping Review.

JMIR Res Protoc

January 2025

Data and Web Science Group, School of Business Informatics and Mathematics, University of Manneim, Mannheim, Germany.

Background: The rapid evolution of large language models (LLMs), such as Bidirectional Encoder Representations from Transformers (BERT; Google) and GPT (OpenAI), has introduced significant advancements in natural language processing. These models are increasingly integrated into various applications, including mental health support. However, the credibility of LLMs in providing reliable and explainable mental health information and support remains underexplored.

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Background: Evaluating digital health service delivery in primary health care requires a validated questionnaire to comprehensively assess users' ability to implement tasks customized to the program's needs.

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Methods: A cross-sectional study was conducted in 2 phases.

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This study provides preliminary evidence for real-time functional magnetic resonance imaging neurofeedback (rt-fMRI NF) as a potential intervention approach for internet gaming disorder (IGD). In a preregistered, randomized, single-blind trial, young individuals with elevated IGD risk were trained to downregulate gaming addiction-related brain activity. We show that, after 2 sessions of neurofeedback training, participants successfully downregulated their brain responses to gaming cues, suggesting the therapeutic potential of rt-fMRI NF for IGD (Trial Registration: ClinicalTrials.

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Background: Mental health concerns have become increasingly prevalent; however, care remains inaccessible to many. While digital mental health interventions offer a promising solution, self-help and even coached apps have not fully addressed the challenge. There is now a growing interest in hybrid, or blended, care approaches that use apps as tools to augment, rather than to entirely guide, care.

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