Embedded values-like shape ethical reasoning of large language models on primary care ethical dilemmas.

Objective: This article uses the framework of Schwartz's values theory to examine whether the embedded values-like profile within large language models (LLMs) impact ethical decision-making dilemmas faced by primary care. It specifically aims to evaluate whether each LLM exhibits a distinct values-like profile, assess its alignment with general population values, and determine whether latent values influence clinical recommendations. Methods: The Portrait Values Questionnaire-Revised (PVQ-RR) was submitted to each LLM (Claude, Bard, GPT-3.5, and GPT-4) 20 times to ensure reliable and valid responses. Their responses were compared to a benchmark derived from a diverse international sample consisting of over 53,000 culturally diverse respondents who completed the PVQ-RR. Four vignettes depicting prototypical professional quandaries involving conflicts between competing values were presented to the LLMs. The option selected by each LLM and the strength of its recommendation were evaluated to determine if underlying values-like impact output. Results: Each LLM demonstrated a unique values-like profile. Universalism and self-direction were prioritized, while power and tradition were assigned less importance than population benchmarks, suggesting potential Western-centric biases. Four clinical vignettes involving value conflicts were presented to the LLMs. Preliminary indications suggested that embedded values-like influence recommendations. Significant variances in confidence strength regarding chosen recommendations materialized between models, proposing that further vetting is required before the LLMs can be relied on as judgment aids. However, the overall selection of preferences aligned with intrinsic value hierarchies. Conclusion: The distinct intrinsic values-like embedded within LLMs shape ethical decision-making, which carries implications for their integration in primary care settings serving diverse populations. For context-appropriate, equitable delivery of AI-assisted healthcare globally it is essential that LLMs are tailored to align with cultural outlooks.

Integrating Previous Suicide Attempts, Gender, and Age Into Suicide Risk Assessment Using Advanced Artificial Intelligence Models.

Objective: Suicide is a critical global health concern. Research indicates that generative artificial intelligence (GenAI) and large language models, such as generative pretrained transformer-3 (GPT-3) and GPT-4, can evaluate suicide risk comparably to experts, yet the criteria these models use are unclear. This study explores how variations in prompts, specifically regarding past suicide attempts, gender, and age, influence the risk assessments provided by ChatGPT-3 and ChatGPT-4.Methods: Using a controlled scenario based approach, 8 vignettes were created. Both ChatGPT-3.5 and ChatGPT 4 were used to predict the likelihood of serious suicide attempts, suicide attempts, and suicidal thoughts. A univariate 3-way analysis of variance was conducted to analyze the effects of the independent variables (previous suicide attempts, gender, and age) on the dependent variables (likelihood of serious suicide attempts, suicide attempts, and suicidal thoughts).Results: Both ChatGPT-3.5 and ChatGPT-4 recognized the importance of previous suicide attempts in predicting severe suicide risks and suicidal thoughts. ChatGPT-4 also identified gender differences, associating men with a higher risk, while both models disregarded age as a risk factor. Interaction analysis revealed that ChatGPT-3.5 associated past attempts with a higher likelihood of suicidal thoughts in men, whereas ChatGPT-4 showed an increased risk for women.Conclusions: The study highlights ChatGPT-3.5 and ChatGPT-4's potential in suicide risk evaluation, emphasizing the importance of prior attempts and gender, while noting differences in their handling of interactive effects and the negligible role of age. These findings reflect the complexity of GenAI decision-making. While promising for suicide risk assessment, these models require careful application due to limitations and real-world complexities.

Non-Suicidal Self-Injury among Adolescents: Effect of Knowledge, Attitudes, Role Perceptions, and Barriers in Mental Health Care on Teachers' Responses.

BACKGROUND: Non-suicidal self-injury (NSSI) involves the deliberate harm of one's body without the intent to commit suicide and is particularly concerning among adolescents. Teachers play a critical role as gatekeepers in identifying and addressing self-harm, underscoring the importance of their knowledge and response strategies in this area. This study explored how teachers' knowledge, attitudes towards NSSI, perceived roles, and workplace barriers affect their responses to students exhibiting NSSI behaviors. METHODS: A cross-sectional survey was conducted among 203 middle and high school teachers in Israel. Data were collected during July and August 2023 using six validated questionnaires. RESULTS: Higher levels of teacher knowledge, positive attitudes, and strong role perceptions correlated with more effective responses to NSSI, whereas increased workplace barriers tended to diminish response efficacy. Positive correlations emerged between role perception and both knowledge and attitudes, whereas negative correlations emerged between workplace barriers, attitudes, and role perceptions. Teaching experience moderated the impact of role perception and workplace barriers on responses. Significant differences were observed between regular and special education settings, although no differences were noted in referral rates or years of seniority. CONCLUSIONS: These findings suggest that enhancing teacher knowledge and attitudes towards NSSI, while addressing workplace barriers, can improve response efficacy in educational settings.

"I Felt a Sense of Mission during Moments of Crisis": Mental Health Professionals' Perspectives on Their Initial Treatment of Evacuees during the Israel-Hamas Conflict.

This study examines mental health service providers who provided care to evacuees during the Israel-Hamas conflict. Utilizing a phenomenological qualitative method, the research delves into the psychological impact on the participants' lived experiences. The sample included 25 mental health providers (13 female, age range 28-63, mean 42.4, SD 7.3; 15 psychologists and 10 social worker, average seniority 10.8 years, SD 5.2, range 2-18 years). Data were collected through semi-structured interviews conducted between December 2023 and March 2024. The data analysis revealed a dual narrative: Participants paid a major personal price and experienced secondary traumatization manifesting in emotional detachment, physical symptoms, and heightened arousal. They also derived a profound sense of meaning and fulfillment from their work, contributing to personal and professional growth. These findings underscore the complexity of their experiences, which were marked by the challenges of secondary trauma and the resilience fostered through their work. This study emphasizes the importance of support systems, including social and familial networks and professional supervision, in navigating these challenges. This study has several limitations, including small sample size and the use of virtual interviews, suggesting the need for further research with a broader participant base and in different contexts.

The impact of history of depression and access to weapons on suicide risk assessment: a comparison of ChatGPT-3.5 and ChatGPT-4.

The aim of this study was to evaluate the effectiveness of ChatGPT-3.5 and ChatGPT-4 in incorporating critical risk factors, namely history of depression and access to weapons, into suicide risk assessments. Both models assessed suicide risk using scenarios that featured individuals with and without a history of depression and access to weapons. The models estimated the likelihood of suicidal thoughts, suicide attempts, serious suicide attempts, and suicide-related mortality on a Likert scale. A multivariate three-way ANOVA analysis with Bonferroni post hoc tests was conducted to examine the impact of the forementioned independent factors (history of depression and access to weapons) on these outcome variables. Both models identified history of depression as a significant suicide risk factor. ChatGPT-4 demonstrated a more nuanced understanding of the relationship between depression, access to weapons, and suicide risk. In contrast, ChatGPT-3.5 displayed limited insight into this complex relationship. ChatGPT-4 consistently assigned higher severity ratings to suicide-related variables than did ChatGPT-3.5. The study highlights the potential of these two models, particularly ChatGPT-4, to enhance suicide risk assessment by considering complex risk factors.

The Artificial Third: A Broad View of the Effects of Introducing Generative Artificial Intelligence on Psychotherapy.

Unlabelled: This paper explores a significant shift in the field of mental health in general and psychotherapy in particular following generative artificial intelligence's new capabilities in processing and generating humanlike language. Following Freud, this lingo-technological development is conceptualized as the "fourth narcissistic blow" that science inflicts on humanity. We argue that this narcissistic blow has a potentially dramatic influence on perceptions of human society, interrelationships, and the self. We should, accordingly, expect dramatic changes in perceptions of the therapeutic act following the emergence of what we term the artificial third in the field of psychotherapy. The introduction of an artificial third marks a critical juncture, prompting us to ask the following important core questions that address two basic elements of critical thinking, namely, transparency and autonomy: (1) What is this new artificial presence in therapy relationships? (2) How does it reshape our perception of ourselves and our interpersonal dynamics? and (3) What remains of the irreplaceable human elements at the core of therapy? Given the ethical implications that arise from these questions, this paper proposes that the artificial third can be a valuable asset when applied with insight and ethical consideration, enhancing but not replacing the human touch in therapy.

Exploring Post-Traumatic Growth Among Young Adults Coping With Parental Loss: The Role of Resilience and of Willingness to Seek Psychological Help.

This study examined the roles of resilience and willingness to seek psychological help in influencing Post-Traumatic Growth (PTG) among 173 emerging adults who experienced parental loss during their school years. A positive relationship was found between resilience, the willingness to seek psychological help, and PTG. Participants who endured loss over five years prior manifested increased PTG (New-Possibilities, Spiritual Change, and Appreciation of Life sub-scales) relative to those with more recent losses. The multiple regression model was notable, accounting for 33% of the variance in PTG. Both resilience and the willingness to seek psychological help assistance significantly predicted PTG, surpassing other predictors in the model. It is worth noting that the type of loss, whether sudden or anticipated, did not alter PTG levels. In essence, this study underscores the enduring positive psychological impact of parental loss on emerging adults, highlighting the critical need for comprehensive psychological resources and support for such individuals.

"Hearing the pupils' voices through my own struggles": A qualitative study of return to work among school counselors who are breast cancer survivors.

For breast cancer survivors, returning to work is an important step for their personal, financial, and psycho-social recovery. Returning to work as a school counselor can be particularly challenging because of the demands of their job and stress at work. This qualitative study examines return to work among school counselors who are breast cancer survivors. In-depth, semi-structured interviews were conducted with 28 survivors of breast cancer stages I-III between the ages of 32 and 55, and up to ten years after the completion of chemotherapy. Interviews focused on the discovery of the illness, treatment period, ramifications of the diagnosis on various aspects of life, and implications for work. Using thematic analysis of the data collected, analysis of the findings revealed three key themes: 1) "Everyone is replaceable": The significance of disruptions in work continuity for school counselors who are breast cancer survivors. 2) "From Zero to a Hundred": Challenges Faced by Counselors in Returning to Work after Breast Cancer Recovery.3) "It's hard to listen to counselees' problems when I am immersed in my own crisis": How surviving breast cancer affects return to work among school counselors. Findings highlight the unique needs of these counselors and the challenges they face upon returning to work. The study discusses recommendations for school principals including training, advocacy, and awareness to support survivors and improve their return to work.

International Survey of Psychosocial Care for Cancer Survivors in Low-/Middle- and High-Income Countries: Current Practices, Barriers, and Facilitators to Care.

PURPOSE: The number of cancer survivors living with and beyond cancer treatment is rising globally. It is fundamental to understand the extent and type of psychosocial care services offered worldwide. We evaluated models of cancer survivorship care, psychosocial care practices in the post-treatment survivorship phase, and barriers/facilitators to delivery of psychosocial care services, including in low- and middle-income countries (LMICs). METHODS: The International Psycho-Oncology Society (IPOS) Survivorship Special Interest Group led a cross-sectional online survey between March and November 2022. Health care professionals and researchers in psycho-oncology were invited through the IPOS global membership, social media, and snowballing. The survey was administered to individuals but included questions related to practices in their country at a national level. RESULTS: Two hundred eighty-three respondents from 37 countries participated (40% from LMICs), with a median of 12 years of experience (IQR, 6-20) in the psycho-oncology field. Participants reported that the most common elements of routine survivorship care were related to the prevention/management of recurrences/new cancers (74%), physical late effects (59%), and chronic medical conditions (53%), whereas surveillance/management of psychosocial late effects (27%) and psychosocial/supportive care (25%) were least common. Service availability was more commonly reported in high-income countries (HICs) than LMICs related to reproductive health (29% v 17%), genetic counseling/support (40% v 20%), and identifying/managing distress (39% v 26%) and pain (66% v 48%). Key barriers included providers focusing on treatment not survivorship (57%), medical not psychosocial care (60%), and a lack of allied health providers to deliver psychosocial care (59%). CONCLUSION: The psychosocial needs of people living with cancer are not adequately available and/or provided in post-treatment survivorship even in HICs, because of barriers at patient, provider, and system levels.

Comparing the Perspectives of Generative AI, Mental Health Experts, and the General Public on Schizophrenia Recovery: Case Vignette Study.

Background: The current paradigm in mental health care focuses on clinical recovery and symptom remission. This model's efficacy is influenced by therapist trust in patient recovery potential and the depth of the therapeutic relationship. 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 health care field, it is important to examine its ability to assess recovery potential in major psychiatric disorders such as schizophrenia. Objective: This study aimed to evaluate the ability of large language models (LLMs) in comparison to mental health professionals to assess the prognosis of schizophrenia with and without professional treatment and the long-term positive and negative outcomes. Methods: Vignettes were inputted into LLMs interfaces and assessed 10 times by 4 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 professional treatment and the positive and negative long-term outcomes of schizophrenia interventions. Results: For the prognosis of schizophrenia with professional treatment, 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 treatment. For 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 pessimistic than Bard and ChatGPT-4. Conclusions: The finding that 3 out of the 4 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, although LLMs hold promise in augmenting health care, their application necessitates rigorous validation and a harmonious blend with human expertise.

Assessing prognosis in depression: comparing perspectives of AI models, mental health professionals and the general public.

BACKGROUND: Artificial intelligence (AI) has rapidly permeated various sectors, including healthcare, highlighting its potential to facilitate mental health assessments. This study explores the underexplored domain of AI's role in evaluating prognosis and long-term outcomes in depressive disorders, offering insights into how AI large language models (LLMs) compare with human perspectives. METHODS: Using case vignettes, we conducted a comparative analysis involving different LLMs (ChatGPT-3.5, ChatGPT-4, Claude and Bard), mental health professionals (general practitioners, psychiatrists, clinical psychologists and mental health nurses), and the general public that reported previously. We evaluate the LLMs ability to generate prognosis, anticipated outcomes with and without professional intervention, and envisioned long-term positive and negative consequences for individuals with depression. RESULTS: In most of the examined cases, the four LLMs consistently identified depression as the primary diagnosis and recommended a combined treatment of psychotherapy and antidepressant medication. ChatGPT-3.5 exhibited a significantly pessimistic prognosis distinct from other LLMs, professionals and the public. ChatGPT-4, Claude and Bard aligned closely with mental health professionals and the general public perspectives, all of whom anticipated no improvement or worsening without professional help. Regarding long-term outcomes, ChatGPT 3.5, Claude and Bard consistently projected significantly fewer negative long-term consequences of treatment than ChatGPT-4. CONCLUSIONS: This study underscores the potential of AI to complement the expertise of mental health professionals and promote a collaborative paradigm in mental healthcare. The observation that three of the four LLMs closely mirrored the anticipations of mental health experts in scenarios involving treatment underscores the technology's prospective value in offering professional clinical forecasts. The pessimistic outlook presented by ChatGPT 3.5 is concerning, as it could potentially diminish patients' drive to initiate or continue depression therapy. In summary, although LLMs show potential in enhancing healthcare services, their utilisation requires thorough verification and a seamless integration with human judgement and skills.

The influence of emotional reactions and compliance with Ministry of Health guidelines during the COVID-19 pandemic in Israel: a longitudinal study of gender differences.

This study sought to examine gender differences in emotional reactions and compliance with Ministry of Health (MOH) guidelines during the COVID-19 pandemic in Israel, with the goal of gaining a deeper understanding of these gender-related variations throughout the lockdown periods. A longitudinal study comprising 2509 participants was conducted during two of Israel's lockdowns: 1424 participants completed a questionnaire during the first lockdown (23 April-5 May 2020); of these, 1085 completed a follow-up questionnaire during the second lockdown (September 30-October 10, 2020). Participants exhibited higher levels of compliance with MOH guidelines (e.g., wearing face masks, maintaining social distancing) and knowledge about COVID-19 during the second lockdown, whereas they exhibited more negative emotional reactions during the first lockdown. Female participants scored higher than male participants on all measures. Multiple regression results showed that about 21% of the variance in compliance with MOH guidelines was explained by lockdown type (i.e., first or second), gender, and age, while knowledge and negative emotional reactions added another 19% to the explained variance. The results suggest that the impact of the pandemic on emotional reactions decreased over time, with people exhibiting greater compliance with MOH guidelines and more knowledge about COVID-19. Moreover, the behavioral and psychological impact of the pandemic was greater on women than on men. The results suggest that healthcare professionals should pay more attention to mental health issues during a pandemic. Moreover, policymakers should focus on women as a vulnerable group and suggest appropriate solutions to reduce their emotional distress. Furthermore, governments and employers should provide greater flexibility and support for single mothers during the pandemic. In addition, gender inequality during lockdowns may place women at greater risk of psychological distress.

Beyond the Surface: Teachers' Perceptions and Experiences in Cases of Non-Suicidal Self-Injury Among High School Students.

Non-suicidal self-injury (NNSI) among adolescents is a significant concern. This study aimed to explore teachers' perceptions and experiences in cases of NSSI among their students. This qualitative-phenomenological study used in-depth, semi-structured interviews conducted with 27 teachers from high-schools in Israel. Thematic analysis was used to identify patterns and themes. Theme 1 highlighted the emotional impact of discovering self-injury incidents, including panic, confusion, and helplessness. Theme 2 focused on teachers' limited professional support and their need for training and guidance. Theme 3 explored teachers' desire to help students and their strategies for building connections and providing empathy, sometimes despite emotional detachment. Theme 4 emphasized the importance of involving parents and the need for effective communication. This study emphasizes the importance of providing teachers comprehensive training to address NSSI effectively. These findings provide a better understanding of teachers' experiences and underscore the need for enhanced support systems.

Identifying depression and its determinants upon initiating treatment: ChatGPT versus primary care physicians.

OBJECTIVE: To compare evaluations of depressive episodes and suggested treatment protocols generated by Chat Generative Pretrained Transformer (ChatGPT)-3 and ChatGPT-4 with the recommendations of primary care physicians. METHODS: Vignettes were input to the ChatGPT interface. These vignettes focused primarily on hypothetical patients with symptoms of depression during initial consultations. The creators of these vignettes meticulously designed eight distinct versions in which they systematically varied patient attributes (sex, socioeconomic status (blue collar worker or white collar worker) and depression severity (mild or severe)). Each variant was subsequently introduced into ChatGPT-3.5 and ChatGPT-4. Each vignette was repeated 10 times to ensure consistency and reliability of the ChatGPT responses. RESULTS: For mild depression, ChatGPT-3.5 and ChatGPT-4 recommended psychotherapy in 95.0% and 97.5% of cases, respectively. Primary care physicians, however, recommended psychotherapy in only 4.3% of cases. For severe cases, ChatGPT favoured an approach that combined psychotherapy, while primary care physicians recommended a combined approach. The pharmacological recommendations of ChatGPT-3.5 and ChatGPT-4 showed a preference for exclusive use of antidepressants (74% and 68%, respectively), in contrast with primary care physicians, who typically recommended a mix of antidepressants and anxiolytics/hypnotics (67.4%). Unlike primary care physicians, ChatGPT showed no gender or socioeconomic biases in its recommendations. CONCLUSION: ChatGPT-3.5 and ChatGPT-4 aligned well with accepted guidelines for managing mild and severe depression, without showing the gender or socioeconomic biases observed among primary care physicians. Despite the suggested potential benefit of using atificial intelligence (AI) chatbots like ChatGPT to enhance clinical decision making, further research is needed to refine AI recommendations for severe cases and to consider potential risks and ethical issues.

Suicide Risk Assessments Through the Eyes of ChatGPT-3.5 Versus ChatGPT-4: Vignette Study.

BACKGROUND: ChatGPT, a linguistic artificial intelligence (AI) model engineered by OpenAI, offers prospective contributions to mental health professionals. Although having significant theoretical implications, ChatGPT's practical capabilities, particularly regarding suicide prevention, have not yet been substantiated. OBJECTIVE: The study's aim was to evaluate ChatGPT's ability to assess suicide risk, taking into consideration 2 discernable factors-perceived burdensomeness and thwarted belongingness-over a 2-month period. In addition, we evaluated whether ChatGPT-4 more accurately evaluated suicide risk than did ChatGPT-3.5. METHODS: ChatGPT was tasked with assessing a vignette that depicted a hypothetical patient exhibiting differing degrees of perceived burdensomeness and thwarted belongingness. The assessments generated by ChatGPT were subsequently contrasted with standard evaluations rendered by mental health professionals. Using both ChatGPT-3.5 and ChatGPT-4 (May 24, 2023), we executed 3 evaluative procedures in June and July 2023. Our intent was to scrutinize ChatGPT-4's proficiency in assessing various facets of suicide risk in relation to the evaluative abilities of both mental health professionals and an earlier version of ChatGPT-3.5 (March 14 version). RESULTS: During the period of June and July 2023, we found that the likelihood of suicide attempts as evaluated by ChatGPT-4 was similar to the norms of mental health professionals (n=379) under all conditions (average Z score of 0.01). Nonetheless, a pronounced discrepancy was observed regarding the assessments performed by ChatGPT-3.5 (May version), which markedly underestimated the potential for suicide attempts, in comparison to the assessments carried out by the mental health professionals (average Z score of -0.83). The empirical evidence suggests that ChatGPT-4's evaluation of the incidence of suicidal ideation and psychache was higher than that of the mental health professionals (average Z score of 0.47 and 1.00, respectively). Conversely, the level of resilience as assessed by both ChatGPT-4 and ChatGPT-3.5 (both versions) was observed to be lower in comparison to the assessments offered by mental health professionals (average Z score of -0.89 and -0.90, respectively). CONCLUSIONS: The findings suggest that ChatGPT-4 estimates the likelihood of suicide attempts in a manner akin to evaluations provided by professionals. In terms of recognizing suicidal ideation, ChatGPT-4 appears to be more precise. However, regarding psychache, there was an observed overestimation by ChatGPT-4, indicating a need for further research. These results have implications regarding ChatGPT-4's potential to support gatekeepers, patients, and even mental health professionals' decision-making. Despite the clinical potential, intensive follow-up studies are necessary to establish the use of ChatGPT-4's capabilities in clinical practice. The finding that ChatGPT-3.5 frequently underestimates suicide risk, especially in severe cases, is particularly troubling. It indicates that ChatGPT may downplay one's actual suicide risk level.

Growing up with a sibling with depression: A qualitative study in Israel.

Depression has major consequences for the entire family, among them emotional distress, disrupted daily routine and social damage caused by negative stigmas. The aim of this study was to explore the retrospective experiences of young adults who lived with a sibling with depression while they were adolescents. The present study adopted a qualitative-phenomenological approach. The research participants were recruited via purposive sampling on social networks across Israel from May to September 2022. Semi-structured interviews were conducted with 21 participants aged 18-29 who lived with a sibling with depression during their adolescence. Data collection continued until saturation of concepts was reached. The results underwent thematic analysis. Three themes emerged from the qualitative analyses: 1) "I felt like I was living in a minefield": Adolescence while living with a sibling with depression; 2) "One step forward and two steps back": Siblings' coping strategies; 3) "My parents were not there for me when I needed them": Participants' experiences with their parents during their adolescence. The research findings indicate that adolescents who grew up with a sibling affected by depression had to cope with an acute family crisis, whose serious ramifications required emotional and social support. Mental health professionals and counselors working within educational institutions and the broader community should provide support and intervention for adolescents who have siblings struggling with depression. This intervention may take the form of individual or group therapy that aims to foster a sense of belonging and help affected individuals. Creating a supportive environment that meets the needs of the affected siblings is also crucial in addressing this issue effectively.

Beyond human expertise: the promise and limitations of ChatGPT in suicide risk assessment.

ChatGPT, an artificial intelligence language model developed by OpenAI, holds the potential for contributing to the field of mental health. Nevertheless, although ChatGPT theoretically shows promise, its clinical abilities in suicide prevention, a significant mental health concern, have yet to be demonstrated. To address this knowledge gap, this study aims to compare ChatGPT's assessments of mental health indicators to those of mental health professionals in a hypothetical case study that focuses on suicide risk assessment. Specifically, ChatGPT was asked to evaluate a text vignette describing a hypothetical patient with varying levels of perceived burdensomeness and thwarted belongingness. The ChatGPT assessments were compared to the norms of mental health professionals. The results indicated that ChatGPT rated the risk of suicide attempts lower than did the mental health professionals in all conditions. Furthermore, ChatGPT rated mental resilience lower than the norms in most conditions. These results imply that gatekeepers, patients or even mental health professionals who rely on ChatGPT for evaluating suicidal risk or as a complementary tool to improve decision-making may receive an inaccurate assessment that underestimates the actual suicide risk.

"I wanted to hide but also to be found": the high school experiences of young adults who grew up in the same home as a sibling with depression.

BACKGROUND: Depression is a mental health condition that can have far-reaching consequences for the entire family, not just for the affected individual. Siblings are particularly vulnerable in that the unremitting stress and guilt at home can affect multiple aspects of their lives, including relationships, added responsibilities, and health. This pressure may affect siblings' own emotional well-being and academic success. Most studies in this field have examined the impact of depression on the affected adolescents or their parents, whereas few have examined the impact on siblings. Sibling studies have been limited by lack of sample homogeneity, especially in the context of coping in high school. This study sought to examine the retrospective experiences of young adults who lived in the same house as a sibling with depression while they were in high school. METHODS: This qualitative study examined 21 young adults (aged 18-29) who grew up with a sibling with depression. In-depth, semi-structured interviews were conducted from May to September 2022. The interviews were recorded and transcribed and underwent thematic analysis. RESULTS: Three main themes emerged from the interviews: (1) "School as a place of refuge": The perspective of participants who grew up with a sibling with depression regarding their high school experience. (2) "I wanted the adults at school to see me": Relations between research participants and the school educational staff. (3) "I was afraid people would relate to me as the sibling of a crazy person": Participants' relationships with their peers. CONCLUSIONS: This study sheds light on the experiences of adolescents who grew up with a sibling with depression. The findings point to feelings of being invisible, self-nullification, avoiding sharing with others, and transparency. The participants were afraid that if their peers found out about their sibling they would also be stigmatized and alienated. The study shows that adolescents living with a sibling with depression need support at school.