Jul 2026· Information Hiding· 0 citations· 15 references
Computer Science
Abstract
Large language models (LLMs) are rapidly becoming embedded in everyday mental health help-seeking practices, particularly among young people who already turn to digital platforms as gateways to mental health support. While LLMs offer unprecedented immediacy and accessibility, their integration into help-seeking ecosystems raises important questions for digital health research. This opinion paper argues that LLMs fundamentally reshape the developmental processes underpinning online help-seeking. Traditional digital help-seeking requires active exploration, searching, comparing sources and reflecting on lived experience, processes that contribute to mental health literacy and resilience. In contrast, LLMs collapse informational plurality into singular, authoritative-sounding responses, potentially shifting users from active exploration toward passive consumption. We discuss the risks of sycophancy, and over-reliance on immediacy, and consider how these dynamics may alter developmental trajectories of coping and help-seeking agency. We argue that preserving agency, connectedness, and reflective engagement must be central to the design of conversational AI in health contexts.
Despite recent advances in large language models (LLMs), their ability to generate empathetic mental health counseling responses in low-resource languages remains largely unexplored. To address this gap, we curate 625 authentic mental health cases from three complementary sources: (1) publicly available Facebook posts discussing mental health concerns, (2) transcripts from the Bangladeshi television program"Ami Akhon Ki Korbo", and (3) anonymized student questionnaire responses covering diverse emotional and psychological challenges. Based on these cases, we build an evaluation corpus comprising advice written by licensed clinical psychologists and responses generated by three modern proprietary LLMs: GPT-4o Mini, Claude 4.5 Haiku, and Gemini 2.5 Pro. We further propose the Role-Playing Reflective Chain-of-Thought Advisory Framework (RP-RCAF), a task-specific prompting strategy that combines expert-authored few-shot examples with structured self-reflection to produce supportive, culturally aware, and ethically aligned counseling through a compassionate advisor persona. We also introduce the Grok 4-Based Response Evaluation and Scoring Framework (G-REFS), which integrates automated assessment with expert psychologist validation across emotional sensitivity, cultural appropriateness, linguistic clarity, and ethical soundness. Experimental results show that RP-RCAF consistently outperforms conventional prompting across all evaluated models and produces responses that more closely align with professional psychological counseling.
Fatema Tuj Johora Faria, Mukaffi Bin Moin, Md. Mahfuzur Rahman et al.· 0 citations
This editorial updates the research priorities articulated by JMIR Mental Health in 2023, while reaffirming their emphasis on equity, replicability, privacy, efficacy, and engagement.
M. Birk, Shruti Kochhar, Keris Myrick et al.· JMIR Mental Health· 1 citation
Digital platforms have become primary spaces through which Kenyan youth interpret distress, search for mental health information, and seek coping support. Yet little Kenya-specific research explains why young people increasingly engage in self-diagnosis and symptom attribution through online environments, or how neurocognitive processes interact with psychosocial drivers and algorithmic exposure to shape help-seeking trajectories. This paper examines the neurocognitive and psychosocial mechanisms underlying digital help-seeking among Kenyan youth, focusing on three linked domains: (i) self-diagnosis as a sense-making strategy, (ii) affect regulation motives that reinforce online searching and scrolling, and (iii) algorithm-shaped symptom attribution, whereby repeated exposure to mental health narratives influences symptom labeling and perceived identity. Using an exploratory design combining digital landscape synthesis, theory-guided review, and stakeholder-informed interpretation, we develop a mechanistic framework explaining how uncertainty reduction, attentional capture, reinforcement learning, social proof, and parasocial trust interact with stigma, service constraints, and peer norms to produce distinctive digital help-seeking patterns. We argue that self-diagnosis is often psychologically adaptive in the short term because it provides emotional relief, coherence, and belonging; however, it can also generate maladaptive cycles through diagnostic anchoring, confirmation bias, and algorithmically amplified symptom salience. The paper proposes an integrated model of digital symptom attribution and identifies governance priorities including credibility cues, algorithm-aware psychoeducation, and referral integration that links high-risk digital trajectories to professional support. The framework offers a foundation for Kenya-specific research, including survey-based measurement of cognitive mechanisms, platform exposure mapping, and longitudinal designs to evaluate whether algorithm-driven symptom narratives intensify distress or improve help-seeking outcomes.
Rose Odhiambo· American Journal of Psychiat...· 0 citations
We developed a large language model designed to explore student mental well-being support in conversational settings, aimed at providing accessible, empathetic, and accurate responses to students facing challenges such anxiety, stress, loneliness, and academic pressure. Many students face barriers to seeking traditional counseling, such as stigma, scheduling constraints or discomfort with face-to-face interactions. The system addresses these challenges by offering a potential accessible conversational support channel through natural, conversational interactions with a large language model. Our approach focused on two strategies. Firstly, we enhanced the model's communication style to reflect counseling best practices such as empathy, active listening, and emotional validation. The second strategy is to enhance the model's understanding of mental health scenarios using realworld text sources and instructions. The model was trained on diverse, anonymized datasets from real counseling transcripts, emotional support dialogue corpora, and peer-support forums. We integrated prompt engineering, fine-tuning, and an iterative self-reflection loop to identify potentially unsupported or hallucination-prone responses, with the goal to improve factuality and safety in generated responses. We find that fine-tuning on student-centered data consistently outperforms both baseline and mixed-data approaches, emphasizing the importance of domainspecific adaptation. The model shows potential for confidential support, suggesting possible use as an early stage aid for coping strategies, and connects students to campus resources, reducing barriers to help seeking and supporting academic performance.
Sarthak Musmade, Lu Liu· International Conference on...· 0 citations
The analysis revealed a rapid acceleration in scholarly output, with a compound annual growth rate of 140%, driven by advancements in models such as GPT-3 and GPT-4, alongside strategic funding and industry initiatives.
Mohammad Ali Hussiny, T. Saidi, Minna Pikkarainen et al.· Frontiers in Psychiatry· 1 citation
Lived experience narratives provide a rich account of how individuals interpret and organize their mental health, capturing dimensions of meaning and context that are often missed by structured assessments and traditional language-based features. Despite their importance, the unstructured nature of these data has limited their systematic use. Recent advances in large language models (LLMs) enable scalable analysis of narrative data, allowing for the extraction of thematic and structural features across large datasets. These approaches position lived experience as a promising digital biomarker, with the potential to capture early, ecologically valid signals and complement existing clinical measures. However, challenges related to validation, interpretability, bias, and data governance remain. We outline emerging methodological frameworks and discuss how LLMbased approaches can support more scalable, longitudinal, and person-centered models of mental health.
Jenna M. Reinen, Cheryl M. Corcoran, René S. Kahn et al.· International Conference on...· 0 citations