Frontiers in Psychiatry

Introduction: Auditory verbal hallucinations (AVH) affect 60-80% of people with schizophrenia, yet existing assessment tools inadequately capture their phenomenological complexity. Aim: To determine whether individuals with schizophrenia spectrum disorders could accurately match auditory verbal hallucination loudness to an external audio track, and to explore participant perspectives on this approach. Methods: Eight participants with schizophrenia spectrum disorders and active AVHs rated loudness via a Likert scale and by adjusting a headphone audio track to match their experience. Structured interviews and thematic analysis captured participant viewpoints. Results: No significant correlation was found between audio tool and Likert scale scores. Seven of eight participants reported the audio tool provided greater precision in quantifying AVH loudness and better enabled them to convey their internal experience to others. Discussion: Audio-matching tools may offer meaningful advantages over traditional scales for quantifying AVH loudness, even where statistical convergence with existing measures is absent. Limitations: Small sample size; loudness alone cannot fully capture the qualitative experience of hearing voices. Implications: This tool shows promise for longitudinal tracking of AVH loudness. Recommendations: Further investigation of digital approaches to AVH assessment is warranted.

Background: Mental health conditions represent a significant public health burden in sub-Saharan Africa, where access to specialist services remains limited. Sokoto State, Nigeria, has an estimated population exceeding 5.4 million with fewer than five psychiatrists, leaving frontline health workers to provide much of the mental health care. LUMANA is a human-centred artificial intelligence (AI) system developed to support mental health screening in resource-constrained settings. Methods: We conducted a field validation of LUMANA in Sokoto State, Nigeria, on 28-29 January 2026 following ethical approval (SKHREC/014/2026). Twenty stakeholders were enrolled, of whom 17 completed structured evaluations across eight standardized clinical scenarios using five assessment tools. The evaluation examined AI output safety, Hausa language performance, human-AI workflow integration, trust and cultural appropriateness, and implementation readiness. Results: A total of 136 AI output safety assessments were completed. Overall, 113/136 (83%) AI outputs were rated acceptable as shown, while 23/136 (17%) were considered acceptable only with human review; no AI output was rated unacceptable. Religion and culture were identified as important determinants of trust by 14/17 (82%) participants, while 17/17 (100%) agreed that disclosures of self-harm or suicide should always involve a human clinician. Mean language quality scores were 9.9/10 for Hausa transcription, 9.9/10 for Hausa-to-English translation, and 9.2/10 for English-to-Hausa summary generation. Clinicians considered 7/8 (88%) human-AI workflow scenarios comfortable for routine practice. Three operational improvements were identified before pilot implementation: clearer protocols for interpreting distress, refinement of language to minimise potentially stigmatizing expressions, and automatic escalation of suspected psychotic symptoms. Conclusions: This scenario-based field validation provides preliminary evidence that LUMANA is perceived as culturally appropriate, acceptable and capable of supporting human-supervised mental health screening in a low-resource setting. However, the findings are based on a small convenience sample using standardized scenarios rather than live clinical use. Accordingly, the results support progression to a carefully monitored pilot implementation following completion of the identified system improvements, with prospective evaluation of safety, workflow integration and implementation outcomes before consideration of wider deployment.

Depression is one of the most prevalent mental health disorders globally. Social media content can reflect emotional states, offering real-time signals of depressive symptoms. This study aimed to develop a deep learning model to predict depression using Twitter/X data, and to characterize potential causes of depression using a large language model (LLM). Twitter/X posts from April 20, 2023, to July 24, 2024, were obtained using the key phrase ("I" or "me") and "diagnosed with depression". After data cleaning and GPT-4o labelling, 2,275 depressive Twitter users, and an additional 1,661 non-depressive users from tweets using the keyword "today", were identified. A deep learning RoBERTa model for predicting depression was built on tweets from both user groups, with an 80/20 training-testing split. GPT-4o models were subsequently employed to analyze depressive users posts to understand potential causes for depression. The RoBERTa model achieved strong performance in predicting depression among Twitter users from their tweets, with an accuracy of 0.822, an F1 score of 0.855, and an AUC of 0.809. Common reasons for depression identified by GPT-4o models included societal pressure, low self-esteem, cultural influences, and identity-related challenges. These findings highlight the potential of deep learning models in early screening of depression using social media data. Insights into potential reasons for depression may inform targeted prevention strategies, public health interventions, and improved mental health support for at-risk populations.

Background Adolescent mental health after the COVID-19 pandemic has been a major public health concern. Studies covering the post-pandemic period remain scarce and rarely include gender-diverse youth. We examined trends in mental health among cisgender, transgender, and nonbinary youth during the pandemic and through 2025. Methods Participants (N=636,201) were adolescents in grades 8-11 from the Finnish School Health Promotion study between 2019 and 2025, a repeated cross-sectional classroom survey. Validated questionnaires were used to assess clinical levels of depressive, generalized anxiety and social anxiety symptoms, as well as mental well-being. Results In 2025 there were 81,145 (48.6%) cisgender girls, 1137 (0.7%) transgender boys, 78,898 (47.2%) cisgender boys, 449 (0.3%) transgender girls, and 4658 (2.8%) nonbinary youth. Clinically significant depressive and generalized anxiety symptoms remained more prevalent in 2025 (reference) compared with pre-pandemic levels across all groups (ORs for 2019, 0.48-0.86; p<0.01). Compared with other groups, symptoms were most prevalent among transgender boys and nonbinary youth. Among cisgender youth, generalized anxiety symptoms increased across the post-pandemic period (ORs for 2021 and 2023, 0.81-0.95; p<0.0001). Nonbinary youth were the only group to show a recovery pattern in depressive, generalized anxiety, and social anxiety symptoms (ORs for 2021, 1.36-1.52; p<00001) and demonstrated improved mental well-being after the pandemic (beta for 2021, -0.68; p<0.0001). Conclusions The results indicate that the elevated levels of mental health problems among adolescents associated with the COVID-19 pandemic have not subsided. They also reveal persistent disparities by gender identity and call for targeted policy action.

Introduction: In 2020, Oregon became the first US state to legalize state-regulated psilocybin services. This study aims to examine: 1) the clinical and demographic characteristics, 2) psilocybin use motivations, and 3) differences in preparedness among patients seeking care in a Oregon- based pilot consult service specializing in psilocybin risk reduction. Methods: This retrospective chart review abstracted sociodemographics, trauma history, and medical and psychiatric risks of patients (November 2023 - September 2025). The Psychedelic Preparedness Scale (PPS), a validated self-report questionnaire, measured preparedness. Two sample t-tests examined associations of PPS scores by insurance, consult motivations, and prior psychedelic use. Results: Patients (N=29) had a mean age of 47.14 years (SD=15.9), were majority female (55.2%); White (82.8%); and privately insured (62.1%). Patients mostly sought psilocybin to address only a psychiatric concern (75.9%); 27.6% anticipated naturalistic (non-state regulated) use. Most patients were deemed low risk for adverse events. Prevalence of prior challenging psychedelic experiences (CPE) was 17.2%; 58.6% reported lifetime psilocybin use. 86.2% endorsed >1 form of lifetime trauma. Of PPS completers (N=23, 79%), mean score was 91.3 (SD = 23.99). Scores did not significantly differ by insurance; consultation motivation; CPE; prior psilocybin or psychedelic use. Conclusion: Patients utilizing a novel consultation service demonstrate a high prevalence of trauma, prior psilocybin use, and baseline preparedness. While preliminary, this is among the first descriptions of patients seeking medical and psychiatric consultation when considering psilocybin and highlight the potential role of healthcare systems in providing evidence-based patient education and risk reduction as interest in psychedelics grows.

A single injection of a subanesthetic dose of ketamine, an NMDA receptor antagonist, induces schizophrenia-like behavior in mice. Previous studies investigated the effect of single injection of ketamine on behavior and c-Fos expression in mice dose-dependently. However, there has not been functional magnetic resonance imaging (fMRI) study to investigate the effect of ketamine in mice. Here, we used awake fMRI and investigated the dose-dependent effect of single injection of ketamine in awaked mice. The lower-dose (5 mg/kg) ketamine increased functional connectivity (FC) with the thalamus and the hypothalamic nuclei. Higher-dose (50 mg/kg) ketamine increased widespread FC with cerebral cortex as well as hippocampus and thalamic nuclei. Graph theory analysis reveals that several regions of hypothalamus and cerebral cortex are essential for the network change by ketamine injection. These results indicate that sub-anesthetic ketamine dose-dependently alters the brain and indues the abnormal behavior in mice.

Frequent comparisons with idealized portrayals of others on social media may be linked to poorer mental health. To test this hypothesis, we systematically searched PsycInfo, MEDLINE, and Web of Science for studies published through April 28, 2026. A total of 149 observational studies (N = 76,262) and 77 experimental studies (N = 15,404) were included in meta-analytic syntheses. Upward social comparison tendency correlated cross-sectionally with mental health outcomes, including psychopathology (r = .32) and body dissatisfaction (r = .54). Worse comparative self-evaluations were associated with poorer mental health and vice versa, with moderate-to-large effect sizes. Across experimental studies, upward social comparisons adversely affected mental health. Results were robust in sub-group analyses (e.g., female or young samples). Across study designs, there was no evidence that downward comparisons were linked to mental health. These findings identify upward social comparisons as a robust correlate and potential risk factor for poor mental health.

Background: In the age of TikTok, YouTube, and Netflix, video streaming (VS) is highly popular among adolescents. Yet, risky to addiction-like viewing patterns (i.e., problematic (P)VS) may adversely affect well-being. Prevalence estimates based on established criteria and etiological understanding of this phenomenon remain scarce. It is associated with de-pression, a major issue within the youth mental health crisis. However, causality remains unclear. This study investigated prevalence trends of adolescent PVS and its temporal rela-tionship with depression. Methods: Population-based data were drawn from four annual waves (2022-2025) of a rep-resentative online survey among 3,477 German adolescents (aged 10-17 years). Weighted annual PVS prevalence estimates were calculated based on standardized measures applying ICD-11 criteria of behavioural addictions distinguishing pathological from hazardous behav-ioural patterns. A cross-lagged panel analysis examined the reciprocal relationship between PVS and depression over four years. Results: Prevalence of pathological VS ranged between 2 to 4% across waves. Hazardous VS prevalence was 13-14% from 2022 to 2024, before increasing to 25% in 2025. Up to 81% of adolescents with pathological VS (21% with hazardous VS) showed clinically relevant symptoms of depression, versus 8-9% of non-affected adolescents. Depression significantly predicted PVS in two of three lags ({beta}W1-W2=0.233, {beta}W2-W3=0.155), but not vice versa. Conclusions: Prevalence rates and their divergent associations with depression support dis-tinguishing hazardous from pathological VS and underline the clinical relevance of PVS. Depression preceded PVS, pointing to the role of maladaptive coping. This has direct impli-cations for effective intervention measures. Future research should clarify the mechanisms underlying this relationship.

IMPORTANCE With a growing global user base of 3.5 billion and users spending nearly as much time gaming as on social media, video gaming's effects on mental well-being have attracted scholarly and public interest. Despite the WHO's inclusion of gaming disorder in ICD-11 and government-mandated restrictions in multiple countries, the causal evidence supporting such policies remains limited. OBJECTIVE To investigate the causal effect of video gaming on mental well-being in the post-COVID period. DESIGN A natural experiment of game console lottery was used to identify the causal effect of video gaming on mental well-being. The intention-to-treat effect was estimated using multivariate regression and propensity score matching. Causal effects of game engagement were estimated using the instrumental variable method (two-stage least squares) and a causal machine learning algorithm, instrumental forest. SETTING Online surveys were conducted between September 2022 and March 2023, covering all 47 prefectures in Japan. PARTICIPANTS A total of 71,435 participants aged 10-69 answered the surveys. 6,911 individuals participated in the natural experiment. EXPOSURES Video game engagement, including video game console ownership, use of the console in the last 30 days, and video gaming duration. MAIN OUTCOMES AND MEASURES Psychological distress and life satisfaction. RESULTS The intention-to-treat effects of winning game console lotteries on mental well-being were positive (0.1 SD). Game console ownership improved mental well-being by 0.1-0.2 SD, and past-month play improved it by 0.2-0.3 SD. An extra hour of daily video game play led to 0.3-0.5 SD improvements in mental well-being. CONCLUSIONS AND RELEVANCE This study found that video gaming had a positive effect on mental well-being in the post-COVID period. The consistency of the effect size with that of a related COVID-period study adds robustness to our findings. Our findings add to the growing evidence that digital media screen time has diverse effects on well-being and support public health policies that recognize the potential mental well-being benefits of appropriate levels of video gaming.

Objective: The Genetic Risk for Serious Mental Illness and Development ("Paisa Kids") Study is establishing a longitudinal cohort of 3,000 youth enriched transdiagnostically for risk for serious mental illnesses (SMI; i.e., schizophrenia, bipolar disorder, and severe depression) from the Paisa region of Colombia. Its objective is to clarify the early clinical, neurobehavioral, genetic, and environmental profiles associated with later SMI and distinguish transdiagnostic from disorder-specific pathways. This paper describes the study infrastructure, recruitment streams, and baseline assessment battery, and characterizes the first two-thirds of the cohort. Method: Youth aged 6-15 years are being recruited. Youth recruited for high SMI risk either have a first or second degree adult relative with a SMI diagnosis or a psychiatric diagnosis themselves identified through electronic medical records (EMRs) associated with later SMI. Lower-risk youth have a relative without a SMI diagnosis. Baseline measures include psychiatric and developmental history, neurobehavioral functioning, environmental exposures, and genome-wide genotyping. Psychiatric EMRs provide longitudinal follow-up. Results: Among 2,384 participants enrolled thus far (mean age=11.2 years, 45.0% female), 2,211 were recruited for high SMI-risk and 173 for lower SMI-risk. Overall, 73.2% met criteria for a history of one or more psychiatric disorders. Logistic regression comparing rates of diagnoses and ACEs between recruitment streams showed elevated rates of depressive and anxiety disorders, attention-deficit/hyperactivity disorder, oppositional defiant disorder, and several ACEs among high versus lower SMI-risk youth. Conclusion: Integrating multidomain phenotyping, genomic data, and longitudinal EMR follow-up, the Paisa Kids cohort provides a unique resource for investigating pathways of risk and resilience to SMI.

Despite its strong link to neuropsychiatric conditions, frustration remains critically understudied in humans and animals alike. Therefore, there is an urgent need to develop tools to understand and therapeutically target frustration-related functions. Interestingly, humans and rats respond similarly during frustrative nonreward by increasing barpress durations. We previously validated barpress duration in rat operant tasks as a reliable measure of frustration-related behavior; however, it is wellknown that in addition to duration of responding, emotional states such as frustration alter other aspects of responding such as force of pressing. One-dimensional, static measures such as maximum force could miss rich information contained within operant data. Thus, the objective of this study is to apply machine learning (ML) to force/time profiles to discriminate frustration-related barpresses from non-frustration-related barpresses. Results showed an AUROC for FR1 (i.e., non-frustrated) vs. extinction (frustrated condition) for individual barpresses of 0.65 that improved to 0.84 with a chunk size of 10. The model generalized well to progressive ratio responding, a different kind of frustration procedure. We conclude that force/time profiling does provide utility beyond one dimensional measures of duration or force separately, meaning that we can indeed infer the internal state of frustration from behavior using ML techniques. Importantly, this project will also serve as proof-of-concept for applying ML to predict other internal states from barpress data.

Objective This study aimed at analyzing changes in the prevalence of excessive internet use and to analyze its associations with mental and cognitive health problems among adolescents. Method We used repeated cross-sectional data from the School Health Promotion study, a population-based nationwide classroom survey in Finland. All adolescents in grades 8-11 were invited, with a total of 602,226 responses in 2017-2023. Excessive internet use was measured with the five-item Excessive Internet Use Scale covering core behavioral addiction symptoms. Depressive symptoms, generalized anxiety, and social anxiety were measured with validated questionnaires. Problems in cognitive functioning were assessed with one-item indicators. Analyses were done with Chi-square tests and logistic regression. Results From 2017 to 2023, the prevalence of excessive internet use increased, especially among girls. The increasing trend was significantly (p<0.001) steeper among girls, with the OR for time (in years) being 1.12 in grades 8-9 and 1.14 in grades 10-11, while among boys the corresponding ORs were 1.00 and 1.04, respectively. Excessive internet use associated with short sleep, depressive, generalized anxiety, and social anxiety symptoms (ORs between 1.73-2.42 in boys; 1.92-2.26 in girls; p<0.001) and with difficulties in tasks requiring reading, writing, and mathematics, and difficulties in concentrating (ORs 1.84-2.31 in boys; 1.83-1.98 in girls; p<0.001). Conclusion The prevalence of excessive internet use showed an increasing trend, especially among girls. The associations of excessive internet use with mental health symptoms and cognitive challenges were clear.

Social media has an estimated global user base of 3.5 billion. Adolescents, constituting over 90% of this group, and thereby university students. are extensively involved in social media use, emphasizing its significance in their daily lives. Excessive use of social media poses risks such as depression, affecting academic performance. The objective of this study was to determine the association between social media use and depression among university students in Urban Northern Tanzania. This was a cross-sectional study that involved university students from three universities. Data was collected through an online close-ended questionnaire. Statistical analysis included the use of frequencies, percentages, chi-square and bivariate logistic regression at 95% confidence intervals (CIs) and significance at p value <0.05. A total of 384 participants were enrolled in the study. 36.7% reported that they used social media for 1 to 3 hours daily. 58.1% of all participants reported high frequency of social media use for social purposes. 29.2% of university students who participated in the study were screened to have depression. The prevalence of depression increased with longer daily duration of social media use. Those using social media for more than 5 hours daily, had 3.049 times higher odds of being depressed. Moderate frequency of social media use for social purposes was linked to reduced depression. Daily duration of social media was significantly associated with depression levels among university students. Significant associations were also found between moderate use of social media for socializing, with lower risk of depression. We recommend that universities develop targeted mental health interventions and promote balanced, purposeful social media use among students.

Individuals with major depressive disorder (MDD) show an attentional bias toward negatively valenced stimuli and thoughts. In this study, we applied a closed-loop neurofeedback procedure designed to reduce this bias. Participants were shown composite negative faces and neutral scenes with variable opacity and were instructed to attend to the neutral scene while ignoring the negative face. Internal attentional states were decoded in real time from functional magnetic resonance imaging (fMRI) data. When a participant's decoded attentional state indicated a failure to ignore the negative faces, the faces became more visible (higher opacity), thus externalizing the brain's attentional lapse in that moment. Forty-eight individuals with MDD were randomly assigned to a real neurofeedback training group (N = 24) or a sham control group (N = 24); the control group received feedback yoked to a participant in the real group. All participants completed three fMRI neurofeedback sessions. The main outcome quantified the extent to which participants got ``stuck'' in the most negative attentional state of focusing strongly on the negative faces. We hypothesized that the real neurofeedback group would learn to escape that state by the end of training, relative to the sham control group. Consistent with our hypothesis, neurofeedback training reduced the probability of getting stuck in the most negative attentional state. In offline analyses, training reduced fMRI activity in the precuneus/posterior cingulate and the medial prefrontal cortex when participants successfully attended to scenes and ignored faces. These results demonstrate the efficacy of remediating the negative attentional bias in depression with closed-loop neurofeedback from real-time fMRI.

Background: Adolescents frequently consult online resources regarding sensitive health topics, including suicide. Although content analyses demonstrate that online platforms contain both suicide-preventive and pro-suicide material, they rarely incorporate subjective data from adolescents at elevated suicide risk. Aims: This exploratory study examined associations between reasons for seeking suicide-related information online and the digital media used to access such content among adolescents with suicidal ideation. Method: Over one year, 131 adolescents aged 13 to 18 with current or past suicidal ideation were recruited from the Emergency Department of an urban pediatric hospital to complete a cross-sectional survey. Participants reported whether they had sought suicide-related information online, the type of information sought, and the online locations used. Mixed-effects regression models examined associations between reasons for online searches and digital media used. Results: Of 131 enrolled adolescents, 113 completed the survey; 69 (61.1%) reported ever seeking suicide-related information online. Searches were more often intended to assist with acting on suicidal thoughts than to reduce suicidal thoughts (63.7% vs. 55.1%). Social media were used for help-seeking searches slightly more often than professional health organization websites (42.1% vs. 31.6%). Among adolescents who used social media for their searches, discussion forums were significantly more likely to be used when seeking information to act on suicidal thoughts (OR=7.0, p=.02). Conclusion: The majority of adolescents with suicidal ideation may seek out suicide-related content online, particularly to facilitate suicide planning. Social media may be used as frequently as professional mental health websites like 988 or The Trevor Project for suicide-related searches.

Autism Spectrum Disorder (ASD) is a heterogeneous neurodevelopmental condition defined by differences in social communication and restricted, repetitive behaviours. As diagnostic criteria have broadened, ASD is now recognised across a wider range of individuals, raising key questions about its structure: does ASD have discrete sub-types, or is it better conceptualised as a continuous, possibly multidimensional, condition? We aim to explore whether a multidimensional continuum model more accurately captures the variability within ASD. We analysed a large SPARK phenotypic dataset of medical history and diagnostic surveys (background history, SCQ, RBS-R; n=36,710 individuals). We apply and compare two traditional statistical approaches, Factor Analysis and Gaussian Mixture Models, with a modern machine learning technique, the Variational Autoencoder (VAE). VAEs reconstructed unseen test data with ~4-fold better accuracy than Factor Analysis, and ~8-fold better accuracy than Gaussian Mixture Models. We identified four stable latent factors across 100 independently trained VAEs. These four dimensions provide an individual behavioural profile that can be visualized using radar-plots, offering a compact way to compare profiles at the person level. Through further analysis, we found evidence for 3 overlapping clusters or subtypes of ASD identified within the 4D latent space. This work aims to inform new ways of modelling ASD using a VAE that will be able to discern between a continuum or a clustered output and that go beyond binary diagnosis, instead reflecting the complex range of trait profiles, with implications for personalised diagnosis and intervention.

Objectives: To estimate the prevalence of Post-Exertional Malaise (PEM) among adults with prior COVID-19 and associated mental health and disability outcomes. Methods: We conducted a cross-sectional analysis of data from a survey of 9,620 adults with prior COVID-19 in New York City, collected May - June 2024. PEM was measured with the DePaul Symptom Questionnaire - Post Exertional Malaise, categorized by symptom duration (< 14 vs. [&ge;]14 hours). Weighted prevalence estimates were stratified by socio-demographic and clinical characteristics. Modified Poisson regression was used to assess the association of PEM with depression, anxiety, and disability. Results: The prevalence of PEM symptoms was 20.9% overall and 4.0% with symptom duration [&ge;]14 hours, representing over 800,000 New Yorkers affected and over 150,000 who meet a diagnostic criterion for ME/CFS. PEM prevalence was higher among women, transgender and non-binary adults, people of color, and lower educational attainment, chronic comorbidities, or disabilities. PEM was associated with 3 - 4 times higher prevalence of mental health outcomes and 4 - 5 times higher disability scores. Conclusions: PEM symptoms were common and strongly associated with disability and adverse mental health. Screening, pathways to care, and supportive policies are needed to mitigate long-term consequences, particularly among marginalized populations.

Objective: To compare appointment completion and offered-slot utilization between virtual and in-person outpatient psychiatric care in Peru. Methods: We conducted a retrospective ecological longitudinal study using routinely collected monthly aggregate administrative indicators from January 2022 through June 2026, with modality-month as the unit. Virtual-minus-in-person differences and 95% confidence intervals used Newey-West standard errors with 12 lags. June 2026 was partial; slot utilization was restricted to 2024-2026. Results: Across 54 paired months, mean completion was 90.9% (SD=3.9) for virtual and 83.8% (SD=2.7) for in-person care; difference +7.2 points (95% CI=4.4-9.9). Across 30 paired months, slot utilization was 73.0% (SD=8.3) and 90.5% (SD=3.8), respectively; difference -17.5 points (95% CI=-24.5 to -10.4). Sensitivity analyses were similar. Conclusions: Virtual care had higher completion but lower slot utilization. Attendance and capacity use should be evaluated separately when planning hybrid psychiatric services. Outpatient psychiatric services increasingly operate as hybrid systems in which virtual and in-person visits coexist.

Background: Generative AI is evolving at a rapid pace, and many individuals are utilizing chatbots for mental health support. The safety of chatbots amid suicide disclosures is a major public health focus. However, the rate and correlates of intentions to seek help from chatbots for suicide thoughts is unknown. Objective: We sought to understand intentions to seek help from chatbots for suicide thoughts, compared to informal, formal, and anonymous online sources. Methods: Participants with clinically significant depression or anxiety (N=58) completed the General Help Seeking Questionnaire regarding help-seeking intentions for suicide thoughts and general emotional problems. Two questions were added to assess intentions to seek help from chatbots and anonymous online sources. Wilcoxon tests were used to compare intentions to use chatbots with intentions to use anonymous online sources and with groupings of informal (e.g., friends, family) and formal (e.g., therapist, general practitioner) sources. Kendall's correlations were used to examine correlations among groupings and individual informal and formal sources, and regression models further examined individual source associations adjusting for general help-seeking intentions. Exploratory analyses assessed whether demographic characteristics, mental health symptoms, and suicide risk were associated with help-seeking intentions for chatbots. Results: Participants endorsed lower help-seeking intentions for suicide thoughts from chatbots than from informal and formal sources. Intention to use chatbots for suicide thoughts was not correlated with informal and formal sources but was correlated with anonymous online sources. At the individual source level, chatbot intentions were positively associated with intimate partners but negatively associated with outreach to friends after adjustment for general help seeking tendency. Anxiety symptom severity was positively correlated with chatbot use intentions, but not with other sources of support. Conclusions: While preliminary, intentions to use chatbots for suicide thoughts appear mostly disconnected from intentions to seek help from other informal and formal supports. Future studies should evaluate the dynamics of help seeking for suicide thoughts via chatbots amidst and, perhaps in place of, other sources of support.

Background: People with severe mental illness (SMI) report poorer oral health outcomes than the general population, are less likely to attend regular dental check-ups, and may require additional support to visit a dentist. Our previous research showed that interventions tailored to their needs and support from the mental health care system are needed to improve oral health outcomes in people with SMI. Although there is some provision of urgent dental care for people with SMI, there are currently limited available interventions that integrate dental and mental health care and provide dental support in a mental healthcare setting. To address this gap, the aims of the SMILE project are to 1) co-develop and 2) test the feasibility and acceptability of an oral health intervention for people with SMI, delivered by mental health professionals in secondary community mental health care settings. This paper describes the protocol for the feasibility and acceptability testing of the SMILE intervention. Methods: This is a single-arm, non-randomised feasibility trial assessing the feasibility and acceptability of an intervention to support the oral health of mental healthcare service users with SMI. The intervention was co-produced with service users, carers, mental health and dental health professionals, and includes advice on maintaining good oral hygiene and lifestyle habits to maintain good oral health. The main aim of the study is to assess the feasibility and acceptability of delivering the intervention, using quantitative data collected at baseline, three and six-month follow-up, and qualitative interviews with participants and mental health staff involved in delivering the intervention. Results: The study results will determine whether the intervention can be successfully delivered in secondary community mental health care settings and inform the most appropriate primary and secondary outcomes for a future trial. Conclusion: The findings of this study will inform the feasibility of progression to the pilot stage of a randomised controlled trial to assess the clinical and cost effectiveness of delivering an oral health intervention in a mental healthcare setting.