GenAI chatbot use for mental health support has become commonplace among Canadian university students and is concentrated among those with greater mental health needs and fewer social support resources.
Abstract
Background. General purpose generative AI (GenAI) chatbots are increasingly used by students for mental health support. Research on prevalence estimates vary widely, rarely link use to validated clinical measures, and have not been reported in a Canadian student population. We estimated the prevalence trends, patterns, perceived impact, and correlates of GenAI use for mental health support among Canadian university students. Methods. We analysed one year (May 2025 to April 2026) repeated cross-sectional data from the Canadian arm of the WHO World Mental Health International College Student survey (WMH-ICS) The primary outcome was past-year prevalence of GenAI use for mental health support. Specific use purposes, perceived impact, reasons for non-use, and future use intent were also analysed. Factors associated with GenAI use were assessed using modified Poisson regression. As a sensitivity analysis, an elastic-net penalised regression model was fitted to assess the robustness of findings to an alternative modelling approach. Results. The past-year prevalence of GenAI chatbot use for mental health support was 25.2% (95% CI: 22.7 - 27.9), with a lifetime prevalence of 30.2%. Use was mostly occasional and predominately for seeking mental health information, stress management, and emotional support/companionship. Students of Asian ethnicity, those with higher clinical burden, recent adverse life experiences, weaker social support, and prior digital help-seeking behaviours were more likely to use GenAI for mental health purposes. Conversely, 2SLGBTQ+ students and those with romantic partners were less likely. Nearly three-quarters (74.2%) of users perceived such use to have a positive impact on their mental health and emotional wellbeing. Non-users reported preference for human interaction, distrust of GenAI in mental health (67.4% each), and privacy/security concerns (50.3%). Non-use also reflected principled objections to AI, including ethical and environmental concerns, with most non-users indicating no future use intention. Conclusions. GenAI chatbot use for mental health support has become commonplace among Canadian university students and is concentrated among those with greater mental health needs and fewer social support resources. Although most users perceived these tools as beneficial, their clinical effectiveness and safety remain uncertain. Rigorous prospective studies are needed to determine whether perceived benefits translate into improved mental health outcomes and whether purpose-built GenAI mental health interventions offer greater clinical benefit and safety than general-purpose chatbots.
Findings may help universities identify students who report difficulty controlling AI use, academic worry, delayed bedtime, academic avoidance, or trouble setting limits on AI use and point to health literacy, self-regulation, and time management as candidate resources for future longitudinal and intervention studies.
Zeyu Zhang, Yinlan Zhang, Xiaomei Lu et al.· Frontiers in Public Health· 0 citations
The extent to which people use general-purpose large language models (LLMs) for their mental health is unknown. Information about use patterns is important for clinicians, developers, and regulators. We surveyed U.S. adults (n = 1871) between August and October 2024 using stratified sampling across age, sex, and race/ethnicity to approximate national demographics. We found that 24% of participants use LLMs for mental health; they are disproportionately young, male, and Black, and have poor mental health. Participants reported difficulty accessing traditional treatment and using LLMs because they are free, convenient, and available. They report using LLMs for emotional support, learning therapy skills, and supplementing existing therapy. Using Pew-reported estimates of population LLM use, we conservatively estimate that as of 2024, 14–18 million U.S. adults may have been using LLMs for mental health. This work highlights the need for monitoring and evaluation to understand the potential harms and benefits of such use.
Elizabeth C. Stade, Zoe M. Tait, Samuel T. Campione et al.· npj Digital Medicine· 2 citations
Mental health issues, especially depressive symptoms, among young adults represent a public health challenge. Conventional psychological assessment tools have limited sensitivity and specificity for identifying individuals at risk. This study aims to develop an explainable machine learning-based model to stratify concurrent depression risk in young adults. This study included 100,257 college students and collected mental health variables including depression, anxiety, resilience, parent-child relationship, and duration of mobile phone usage. The screening capabilities of 13 machine learning algorithms were systematically evaluated and compared. The SHapley Additive exPlanations (SHAP) framework was employed for the interpretability of the final model. The median scores for parent-child relationship, resilience, anxiety, and mobile phone usage time was 42.0, 28.0, 1.0 and 28.0, respectively. Among the 13 machine learning algorithms, the XGBoost model demonstrated superior performance. The final multivariate screening model achieved an area under the curve (AUC) of 0.887, a sensitivity of 0.787, a specificity of 0.830, and an accuracy of 0.816 in classifying young adults' concurrent depression risk. The SHAP analysis showed the importance of each variable: anxiety (2.303) > resilience (0.774) > parent-child relationship (0.708) > mobile phone usage time (0.411). The final multivariate model exhibited stable performance during cross-validation (AUC = 0.885 ± 0.032), significantly better than the single-variable model (P < 0.001) and better screening reliability (Brier score 0.153). The final multivariate XGBoost model provides a highly accurate and interpretable approach for young adults' depression risk stratification. As the model was developed using cross-sectional data collected during the COVID-19 campus lockdown, prospective validation is required before clinical deployment. Notably, anxiety level emerged as the most influential risk factor, and resilience demonstrated a significant protective effect.
These findings demonstrate the value of an artificial intelligence-driven literature review for informing comprehensive strategies to ad- dress the multifactorial nature of AUD.
Chenlan Wang, Kurtis Riener, LuoSan Yue· Proceedings of the Internati...· 0 citations
Epidemiological data indicate that the prevalence of depression among young people (YP) is increasing. Although evidence-based interventions are effective for many, 20 to 40% fail, and relapse is common. This study focused on second-line treatments. Modifying psychological processes that predispose individuals to treatment failure or relapse necessitates the development of interventions, but their evaluation depends on a clear understanding of current practice to enable meaningful comparison and assessment of effectiveness. We explored treatment as usual (TAU) for YP receiving support for depression in a two-site pilot, feasibility trial comparing Mindfulness for Adolescents and Carers (MAC) and TAU against TAU in UK Child & Adolescent Mental Health Services (CAMHS). Qualitative data were collected via interviews with senior managers and case-managing clinicians. Quantitative data were gathered through Treatment Recording Sheets completed by clinicians and a clinical service audit. Data were synthesised across sites to provide a comprehensive description of TAU, informing the design of a future definitive trial of MAC. Despite site-level variation in service delivery, key components and processes of CAMHS TAU were identified. Typical pathways included referral and intake; waiting/supportive provision; first- and second-line treatment/s; discharge and re-referral; and crisis support. Care coordination emerged as the most common second-line provision and was provided to YP in both trial arms. Participants received varied combinations of support; those allocated to MAC received less CAMHS input than those allocated to TAU. There is substantial variability in service provision for YP experiencing depression. Future studies should compare treatment plus care coordination with TAU.
SB Mitchell, Vashti Berry, J. Westbrook et al.· Discover Health Systems· 0 citations
PURPOSE
Social exclusion encompasses multiple domains, material, digital, relational, political, and structural. These may have different impacts on an individual's mental health. We evaluated associations between domains of social exclusion and mental health problems in a sample of English adults.
METHODS
Using data from Understanding Society, we analysed social exclusion exposures and mental health outcomes at Wave 11 (2019-21) among 34,403 adults. We generated standardised domain scores and an overall social exclusion measure using principal component analysis. Mental health was assessed using the General Health Questionnaire (GHQ-12) continuous symptom scores (range 0-36). We used survey-weighted linear regression models adjusted for age, sex, and ethnicity.
RESULTS
Each standard deviation increase in overall social exclusion was associated with 1.28-point increase in GHQ-12 symptoms (95% CI: 1.11,1.44). Domain-specific analyses showed the strongest associations for material exclusion ([Formula: see text]=1.34, 95% CI: 1.20,1.47) and relational exclusion ([Formula: see text]=1.37, 95% CI:1.09,1.65). Digital, political, and structural exclusion showed smaller positive associations ([Formula: see text]=0.15 to 0.37). Associations did not differ by sex and remained robust when examining mental health caseness as a binary outcome.
CONCLUSION
Social exclusion was associated with worse mental health, with strongest effects for relational and material domains. Policies targeting these domains may have the largest impact on population mental health.
M. Spano, D. Osborn, N. Launders et al.· Social Psychiatry and Psychi...· 0 citations