Jmir Mental Health最新文献

{"title":"Assessing the Alignment of Large Language Models With Human Values for Mental Health Integration: Cross-Sectional Study Using Schwartz’s Theory of Basic Values","authors":"Dorit Hadar-Shoval, Kfir Asraf, Yonathan Mizrachi, Yuval Haber, Zohar Elyoseph","doi":"10.2196/55988","DOIUrl":"https://doi.org/10.2196/55988","url":null,"abstract":"<strong>Background:</strong> Large language models (LLMs) hold potential for mental health applications. However, their opaque alignment processes may embed biases that shape problematic perspectives. Evaluating the values embedded within LLMs that guide their decision-making have ethical importance. Schwartz’s theory of basic values (STBV) provides a framework for quantifying cultural value orientations and has shown utility for examining values in mental health contexts, including cultural, diagnostic, and therapist-client dynamics. <strong>Objective:</strong> This study aimed to (1) evaluate whether the STBV can measure value-like constructs within leading LLMs and (2) determine whether LLMs exhibit distinct value-like patterns from humans and each other. <strong>Methods:</strong> In total, 4 LLMs (Bard, Claude 2, Generative Pretrained Transformer [GPT]-3.5, GPT-4) were anthropomorphized and instructed to complete the Portrait Values Questionnaire—Revised (PVQ-RR) to assess value-like constructs. Their responses over 10 trials were analyzed for reliability and validity. To benchmark the LLMs’ value profiles, their results were compared to published data from a diverse sample of 53,472 individuals across 49 nations who had completed the PVQ-RR. This allowed us to assess whether the LLMs diverged from established human value patterns across cultural groups. Value profiles were also compared between models via statistical tests. <strong>Results:</strong> The PVQ-RR showed good reliability and validity for quantifying value-like infrastructure within the LLMs. However, substantial divergence emerged between the LLMs’ value profiles and population data. The models lacked consensus and exhibited distinct motivational biases, reflecting opaque alignment processes. For example, all models prioritized universalism and self-direction, while de-emphasizing achievement, power, and security relative to humans. Successful discriminant analysis differentiated the 4 LLMs’ distinct value profiles. Further examination found the biased value profiles strongly predicted the LLMs’ responses when presented with mental health dilemmas requiring choosing between opposing values. This provided further validation for the models embedding distinct motivational value-like constructs that shape their decision-making. <strong>Conclusions:</strong> This study leveraged the STBV to map the motivational value-like infrastructure underpinning leading LLMs. Although the study demonstrated the STBV can effectively characterize value-like infrastructure within LLMs, substantial divergence from human values raises ethical concerns about aligning these models with mental health applications. The biases toward certain cultural value sets pose risks if integrated without proper safeguards. For example, prioritizing universalism could promote unconditional acceptance even when clinically unwise. Furthermore, the differences between the LLMs underscore the need to standardize alignment","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"16 1","pages":""},"PeriodicalIF":5.2,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140573390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Studies of Social Anxiety Using Ambulatory Assessment: Systematic Review","authors":"Javier Fernández-Álvarez, Desirée Colombo, Juan Martín Gómez Penedo, Maitena Pierantonelli, Rosa María Baños, Cristina Botella","doi":"10.2196/46593","DOIUrl":"https://doi.org/10.2196/46593","url":null,"abstract":"<strong>Background:</strong> There has been an increased interest in understanding social anxiety (SA) and SA disorder (SAD) antecedents and consequences as they occur in real time, resulting in a proliferation of studies using ambulatory assessment (AA). Despite the exponential growth of research in this area, these studies have not been synthesized yet. <strong>Objective:</strong> This review aimed to identify and describe the latest advances in the understanding of SA and SAD through the use of AA. <strong>Methods:</strong> Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, a systematic literature search was conducted in Scopus, PubMed, and Web of Science. <strong>Results:</strong> A total of 70 articles met the inclusion criteria. The qualitative synthesis of these studies showed that AA permitted the exploration of the emotional, cognitive, and behavioral dynamics associated with the experience of SA and SAD. In line with the available models of SA and SAD, emotion regulation, perseverative cognition, cognitive factors, substance use, and interactional patterns were the principal topics of the included studies. In addition, the incorporation of AA to study psychological interventions, multimodal assessment using sensors and biosensors, and transcultural differences were some of the identified emerging topics. <strong>Conclusions:</strong> AA constitutes a very powerful methodology to grasp SA from a complementary perspective to laboratory experiments and usual self-report measures, shedding light on the cognitive, emotional, and behavioral antecedents and consequences of SA and the development and maintenance of SAD as a mental disorder.","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"15 1","pages":""},"PeriodicalIF":5.2,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140573437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digital Tools to Facilitate the Detection and Treatment of Bipolar Disorder: Key Developments and Future Directions.","authors":"Taiane de Azevedo Cardoso, Shruti Kochhar, John Torous, Emma Morton","doi":"10.2196/58631","DOIUrl":"10.2196/58631","url":null,"abstract":"<p><p>Bipolar disorder (BD) impacts over 40 million people around the world, often manifesting in early adulthood and substantially impacting the quality of life and functioning of individuals. Although early interventions are associated with a better prognosis, the early detection of BD is challenging given the high degree of similarity with other psychiatric conditions, including major depressive disorder, which corroborates the high rates of misdiagnosis. Further, BD has a chronic, relapsing course, and the majority of patients will go on to experience mood relapses despite pharmacological treatment. Digital technologies present promising results to augment early detection of symptoms and enhance BD treatment. In this editorial, we will discuss current findings on the use of digital technologies in the field of BD, while debating the challenges associated with their implementation in clinical practice and the future directions.</p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"11 ","pages":"e58631"},"PeriodicalIF":5.2,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11019420/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140337340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel Blended Transdiagnostic Intervention (eOrygen) for Youth Psychosis and Borderline Personality Disorder: Uncontrolled Single-Group Pilot Study.","authors":"Shaunagh O'Sullivan, Carla McEnery, Daniela Cagliarini, Jordan D X Hinton, Lee Valentine, Jennifer Nicholas, Nicola A Chen, Emily Castagnini, Jacqueline Lester, Esta Kanellopoulos, Simon D'Alfonso, John F Gleeson, Mario Alvarez-Jimenez","doi":"10.2196/49217","DOIUrl":"10.2196/49217","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Integrating innovative digital mental health interventions within specialist services is a promising strategy to address the shortcomings of both face-to-face and web-based mental health services. However, despite young people's preferences and calls for integration of these services, current mental health services rarely offer blended models of care.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This pilot study tested an integrated digital and face-to-face transdiagnostic intervention (eOrygen) as a blended model of care for youth psychosis and borderline personality disorder. The primary aim was to evaluate the feasibility, acceptability, and safety of eOrygen. The secondary aim was to assess pre-post changes in key clinical and psychosocial outcomes. An exploratory aim was to explore the barriers and facilitators identified by young people and clinicians in implementing a blended model of care into practice.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A total of 33 young people (aged 15-25 years) and 18 clinicians were recruited over 4 months from two youth mental health services in Melbourne, Victoria, Australia: (1) the Early Psychosis Prevention and Intervention Centre, an early intervention service for first-episode psychosis; and (2) the Helping Young People Early Clinic, an early intervention service for borderline personality disorder. The feasibility, acceptability, and safety of eOrygen were evaluated via an uncontrolled single-group study. Repeated measures 2-tailed t tests assessed changes in clinical and psychosocial outcomes between before and after the intervention (3 months). Eight semistructured qualitative interviews were conducted with the young people, and 3 focus groups, attended by 15 (83%) of the 18 clinicians, were conducted after the intervention.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;eOrygen was found to be feasible, acceptable, and safe. Feasibility was established owing to a low refusal rate of 25% (15/59) and by exceeding our goal of young people recruited to the study per clinician. Acceptability was established because 93% (22/24) of the young people reported that they would recommend eOrygen to others, and safety was established because no adverse events or unlawful entries were recorded and there were no worsening of clinical and social outcome measures. Interviews with the young people identified facilitators to engagement such as peer support and personalized therapy content, as well as barriers such as low motivation, social anxiety, and privacy concerns. The clinician focus groups identified evidence-based content as an implementation facilitator, whereas a lack of familiarity with the platform was identified as a barrier owing to clinicians' competing priorities, such as concerns related to risk and handling acute presentations, as well as the challenge of being understaffed.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;eOrygen as a blended transdiagnostic intervention has the potential to increase therapeutic co","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"11 ","pages":"e49217"},"PeriodicalIF":5.2,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11019426/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140337339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Developing and Implementing a Web-Based Branching Logic Survey to Support Psychiatric Crisis Evaluations of Individuals With Developmental Disabilities: Qualitative Study and Evaluation of Validity.","authors":"Deborah A Bilder, Mariah Mthembu, Whitney Worsham, Patricia Aguayo, Jacob R Knight, Steven W Deng, Tejinder P Singh, John Davis","doi":"10.2196/50907","DOIUrl":"10.2196/50907","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Individuals with developmental disabilities (DD) experience increased rates of emotional and behavioral crises that necessitate assessment and intervention. Psychiatric disorders can contribute to crises; however, screening measures developed for the general population are inadequate for those with DD. Medical conditions can exacerbate crises and merit evaluation. Screening tools using checklist formats, even when designed for DD, are too limited in depth and scope for crisis assessments. The Sources of Distress survey implements a web-based branching logic format to screen for common psychiatric and medical conditions experienced by individuals with DD by querying caregiver knowledge and observations.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This paper aims to (1) describe the initial survey development, (2) report on focus group and expert review processes and findings, and (3) present results from the survey's clinical implementation and evaluation of validity.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Sources of Distress was reviewed by focus groups and clinical experts; this feedback informed survey revisions. The survey was subsequently implemented in clinical settings to augment providers' psychiatric and medical history taking. Informal and formal consults followed the completion of Sources of Distress for a subset of individuals. A records review was performed to identify working diagnoses established during these consults.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Focus group members (n=17) expressed positive feedback overall about the survey's content and provided specific recommendations to add categories and items. The survey was completed for 231 individuals with DD in the clinical setting (n=161, 69.7% men and boys; mean age 17.7, SD 10.3; range 2-65 years). Consults were performed for 149 individuals (n=102, 68.5% men and boys; mean age 18.9, SD 10.9 years), generating working diagnoses to compare survey screening results. Sources of Distress accuracy rates were 91% (95% CI 85%-95%) for posttraumatic stress disorder, 87% (95% CI 81%-92%) for anxiety, 87% (95% CI 81%-92%) for episodic expansive mood and bipolar disorder, 82% (95% CI 75%-87%) for psychotic disorder, 79% (95% CI 71%-85%) for unipolar depression, and 76% (95% CI 69%-82%) for attention-deficit/hyperactivity disorder. While no specific survey items or screening algorithm existed for unspecified mood disorder and disruptive mood dysregulation disorder, these conditions were caregiver-reported and working diagnoses for 11.7% (27/231) and 16.8% (25/149) of individuals, respectively.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Caregivers described Sources of Distress as an acceptable tool for sharing their knowledge and insights about individuals with DD who present in crisis. As a screening tool, this survey demonstrates good accuracy. However, better differentiation among mood disorders is needed, including the addition of items and screening algorithm for unspecified mood d","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"11 ","pages":"e50907"},"PeriodicalIF":5.2,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11015367/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140319545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Implementation of Recommender Systems for Mental Health Recovery Narratives: Evaluation of Use and Performance.","authors":"Emily Slade, Stefan Rennick-Egglestone, Fiona Ng, Yasuhiro Kotera, Joy Llewellyn-Beardsley, Chris Newby, Tony Glover, Jeroen Keppens, Mike Slade","doi":"10.2196/45754","DOIUrl":"10.2196/45754","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Recommender systems help narrow down a large range of items to a smaller, personalized set. NarraGive is a first-in-field hybrid recommender system for mental health recovery narratives, recommending narratives based on their content and narrator characteristics (using content-based filtering) and on narratives beneficially impacting other similar users (using collaborative filtering). NarraGive is integrated into the Narrative Experiences Online (NEON) intervention, a web application providing access to the NEON Collection of recovery narratives.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to analyze the 3 recommender system algorithms used in NarraGive to inform future interventions using recommender systems for lived experience narratives.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Using a recently published framework for evaluating recommender systems to structure the analysis, we compared the content-based filtering algorithm and collaborative filtering algorithms by evaluating the accuracy (how close the predicted ratings are to the true ratings), precision (the proportion of the recommended narratives that are relevant), diversity (how diverse the recommended narratives are), coverage (the proportion of all available narratives that can be recommended), and unfairness (whether the algorithms produce less accurate predictions for disadvantaged participants) across gender and ethnicity. We used data from all participants in 2 parallel-group, waitlist control clinical trials of the NEON intervention (NEON trial: N=739; NEON for other [eg, nonpsychosis] mental health problems [NEON-O] trial: N=1023). Both trials included people with self-reported mental health problems who had and had not used statutory mental health services. In addition, NEON trial participants had experienced self-reported psychosis in the previous 5 years. Our evaluation used a database of Likert-scale narrative ratings provided by trial participants in response to validated narrative feedback questions.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Participants from the NEON and NEON-O trials provided 2288 and 1896 narrative ratings, respectively. Each rated narrative had a median of 3 ratings and 2 ratings, respectively. For the NEON trial, the content-based filtering algorithm performed better for coverage; the collaborative filtering algorithms performed better for accuracy, diversity, and unfairness across both gender and ethnicity; and neither algorithm performed better for precision. For the NEON-O trial, the content-based filtering algorithm did not perform better on any metric; the collaborative filtering algorithms performed better on accuracy and unfairness across both gender and ethnicity; and neither algorithm performed better for precision, diversity, or coverage.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Clinical population may be associated with recommender system performance. Recommender systems are susceptible to a wide range of undesirable biases.","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"11 ","pages":"e45754"},"PeriodicalIF":5.2,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11015364/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140319546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Combining mHealth Technology and Pharmacotherapy to Improve Mental Health Outcomes and Reduce Human Rights Abuses in West Africa: Intervention Field Trial","authors":"Dror Ben-Zeev, Anna Larsen, Dzifa A Attah, Kwadwo Obeng, Alexa Beaulieu, Seth M Asafo, Jonathan Kuma Gavi, Arya Kadakia, Emmanuel Quame Sottie, Sammy Ohene, Lola Kola, Kevin Hallgren, Jaime Snyder, Pamela Y Collins, Angela Ofori-Atta, M-Healer Research Team","doi":"10.2196/53096","DOIUrl":"https://doi.org/10.2196/53096","url":null,"abstract":"Background: In West Africa, healers greatly outnumber trained mental health professionals. People with serious mental illness (SMI) are often seen by healers in “prayer camps” where they may also experience human rights abuses. We developed M&M, an 8-week long dual-pronged intervention involving a) a smartphone-delivered toolkit designed to expose healers to brief psychosocial interventions and to encourage them to preserve human rights (M-Healer app), and b) a visiting nurse who provides medications to their patients (Mobile Nurse). Objective: To examine the feasibility, acceptability, safety, and preliminary effectiveness of the M&M intervention in real-world prayer camp settings. Methods: We conducted a single-arm field trial of M&M with people with SMI and healers in a prayer camp in Ghana. Healers were provided with smartphones with M-Healer installed and were trained by practice facilitators to use the digital toolkit. In parallel, a study nurse visited their prayer camp to administer medications to their patients. Clinical assessors administered study measures to participants with SMI at pre-treatment (baseline), mid-treatment (4 weeks) and post-treatment (8 weeks). Results: Seventeen participants were enrolled in the study and most (n=15, 88.3%) were retained. Participants had an average age of 44.3 (SD: 13.9) and 59% (n=10) were male. Fourteen participants (82%) had a diagnosis of schizophrenia and two participants (18%) were diagnosed with bipolar disorder. Four healers were trained to use M-Healer. On average, they self-initiated app use 31.9 (SD: 28.9) times per week. Healers watched an average of 19.1 videos (SD: 21.2), responded to 1.5 prompts (SD: 2.4), and used the app for 5.3 days (SD: 2.7) weekly. Pre/post analyses found a statistically significant and clinically meaningful reduction in psychiatric symptom severity (Brief Psychiatric Rating Scale: 52.3 to 30.9; Brief Symptom Inventory: 76.4 to 27.9), psychological distress (Talbieh Brief Distress Inventory: 37.7 to 16.9) shame (Other as Shamer Scale: 41.9 to 28.5), and stigma (Brief Internalized Stigma of Mental Illness Scale: 11.8 to 10.3). We recorded a significant reduction in days chained (1.6 to 0.5) and a promising trend for reduction in days of forced fasting (2.6 to 0.0, p = 0.059). We did not identify significant pre/post changes in patient-reported working alliance with healers (Working Alliance Inventory), depressive symptom severity (Patient Health Questionnaire-9), quality of life (Lehman Quality of Life Interview for the Mentally Ill), beliefs about medication (Beliefs about Medications Questionnaire – General Harm sub-scale) or other human rights abuses. No major side effects, health and safety violations, or serious adverse events occurred over the course of the trial. Conclusions: The M&M intervention proved to be feasible, acceptable, safe, and clinically promising. Preliminary findings suggest the M-Healer toolkit may have shifted healer behaviors at the praye","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"12 1","pages":""},"PeriodicalIF":5.2,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140312569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Urgent Need for an Evidence-Based Digital Mental Health Practice Model of Care for Youth","authors":"Brad Ridout, Rowena Forsyth, Krestina L Amon, Pablo Navarro, Andrew J Campbell","doi":"10.2196/48441","DOIUrl":"https://doi.org/10.2196/48441","url":null,"abstract":"Australian providers of mental health services and support for young people include private and public allied health providers, government initiatives (e.g., headspace), non-government organisations (e.g., Kids Helpline), GPs, and the hospital system. Over 20 years of research has established that many young people prefer to seek mental health support online, however clear client pathways within and between online and offline mental health services are currently lacking. The authors propose a Digital Mental Health Practice model of care for youth to assist with digital mental health service mapping. The proposed model offers accessible pathways for a client to engage with digital mental health services, provides clear navigation to access support for individual needs, and facilitates a seamless connection with offline mental health services using a transferrable electronic health records system. This future-looking model also includes emerging technologies, such as artificial intelligence and the metaverse, that must be accounted for as potential tools to be leveraged for digital therapies and support systems. The urgent need for a user-centered Digital Mental Health Practice model of care for youth in Australia is discussed, highlighting the shortcomings of traditional and existing online triage models evident during the COVID-19 pandemic, and the complex challenges that must be overcome such as the integration of diverse mental health care providers and establishment of a robust electronic health records system. Potential benefits of such a model include reduced pressure on emergency rooms, improved identification of immediate needs, enhanced referral practices, and the establishment of a cost-efficient national digital mental health care model with global applicability. The authors conclude by stressing the consequences of inaction, warning that delays may lead to more complex challenges as new technologies emerge and exacerbate the long-term negative consequences of poor mental health management on the economic and biopsychosocial well-being of young Australians.","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"26 1","pages":""},"PeriodicalIF":5.2,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140201374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparing the Perspectives of Generative AI, Mental Health Experts, and the General Public on Schizophrenia Recovery: Case Vignette Study","authors":"Zohar Elyoseph, Inbar Levkovich","doi":"10.2196/53043","DOIUrl":"https://doi.org/10.2196/53043","url":null,"abstract":"Background: Background: The current paradigm in mental healthcare focuses on clinical recovery and symptom remission. This model’s efficacy is influenced by therapist trust in patient recovery potential and therapeutic relationship depth. Schizophrenia is a chronic illness with severe symptoms where the possibility of recovery is a matter of debate. As artificial intelligence (AI) becomes integrated into the healthcare field, it is important to examine its ability to assess recovery potential in major psychiatric disorders such as schizophrenia. Objective: Objectives: To evaluate the ability of Large Languets Models (LLMs) in comparison to mental health professionals to assess the prognosis of schizophrenia with and without treatments and the long term positive and negative outcomes. Methods: Methods: Vignettes were input to LLMs interfaces and assessed ten times by four AI platforms: ChatGPT-3.5, ChatGPT-4, Google Bard, and Claude. A total of 80 evaluations were collected and benchmarked against existing norms to analyze what mental health professionals (general practitioners, psychiatrists, clinical psychologists and mental health nurses) and the general public think about schizophrenia prognosis with and without treatment and the positive and negative long-term outcomes of schizophrenia interventions. Results: Results: Prognosis with professional help: ChatGPT-3.5 was notably pessimistic, whereas ChatGPT-4, Claude and BARD aligned with professional views but differed from the general public. All LLMs believed untreated schizophrenia would remain static or worsen without professional help. Long-term outcomes: ChatGPT-4 and Claude predicted more negative outcomes than BARD and ChatGPT-3.5. For positive outcomes, ChatGPT-3.5 and Claude were more negative than BARD and ChatGPT-4. Conclusions: Conclusions: The findings that three out of the four LLMs aligned closely with the predictions of mental health professionals when considering the 'with treatment' condition is a demonstration of the potential of this technology in providing professional clinical prognosis. The pessimistic assessment of ChatGPT 3.5 is a disturbing finding since it may reduce the motivation of patients to start or persist with treatment for schizophrenia. Overall, while LLMs hold promise in augmenting healthcare, their application necessitates rigorous validation and a harmonious blend with human expertise.","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"101 1","pages":""},"PeriodicalIF":5.2,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140153341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficacy of BrighterSide, a Self-Guided App for Suicidal Ideation: Randomized Controlled Trial","authors":"Natasha Josifovski, Michelle Torok, Philip Batterham, Quincy Wong, Joanne R Beames, Adam Theobald, Sarah Holland, Kit Huckvale, Jo Riley, Nicole Cockayne, Helen Christensen, Mark Larsen","doi":"10.2196/55528","DOIUrl":"https://doi.org/10.2196/55528","url":null,"abstract":"Background: Self-guided digital interventions can reduce the severity of suicidal ideation, although there remain relatively few, rigorously evaluated, smartphone apps targeting suicidality. Objective: This trial evaluated whether the BrighterSide® smartphone app intervention was superior to a waitlist control group at reducing the severity of suicidal ideation. Methods: 550 adults aged 18-65 with recent suicidal ideation were recruited from the Australian community. In this randomized controlled trial, participants were randomly assigned to receive either the BrighterSide® intervention app, or to a waitlist control group which involved treatment as usual. The app was self-guided, and participants could use the app at their own pace for the duration of the study period. Self-report measures were collected at baseline, 6-weeks, and 12-weeks. The primary outcome was severity and frequency of suicidal ideation, and secondary outcomes included psychological distress and functioning and recovery. Additional data were collected on app engagement and participant feedback. Results: Suicidal ideation reduced over time for all participants, but there was no significant interaction between group and time. Similar improvements were observed for self-harm, functioning and recovery, days out of role, and coping. Psychological distress was significantly lower in the intervention group at the 6-week follow-up, but this was not maintained at 12 weeks. Conclusions: The BrighterSide® app did not lead to a significant improvement in suicidal ideation, relative to a waitlist control group. Possible reasons for this null finding are discussed. Clinical Trial: Australian New Zealand Clinical Trials Registry (ACTRN12621000712808).","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":"119 1","pages":""},"PeriodicalIF":5.2,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140153665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}