Hasil untuk "Mental healing"

Yaling Shen, Stephanie Fong, Yiwen Jiang et al.

The increasing integration of large language models (LLMs) into mental health applications necessitates robust frameworks for evaluating professional safety alignment. Current evaluative approaches primarily rely on refusal-based safety signals, which offer limited insight into the nuanced behaviors required in clinical practice. In mental health, clinically inadequate refusals can be perceived as unempathetic and discourage help-seeking. To address this gap, we move beyond refusal-centric metrics and introduce \texttt{PsychEthicsBench}, the first principle-grounded benchmark based on Australian psychology and psychiatry guidelines, designed to evaluate LLMs' ethical knowledge and behavioral responses through multiple-choice and open-ended tasks with fine-grained ethicality annotations. Empirical results across 14 models reveal that refusal rates are poor indicators of ethical behavior, revealing a significant divergence between safety triggers and clinical appropriateness. Notably, we find that domain-specific fine-tuning can degrade ethical robustness, as several specialized models underperform their base backbones in ethical alignment. PsychEthicsBench provides a foundation for systematic, jurisdiction-aware evaluation of LLMs in mental health, encouraging more responsible development in this domain.

Elham Aghakhani, Rezvaneh Rezapour

Large language models (LLMs) are increasingly used for emotional support and mental health-related interactions outside clinical settings, yet little is known about how people evaluate and relate to these systems in everyday use. We analyze 5,126 Reddit posts from 47 mental health communities describing experiential or exploratory use of AI for emotional support or therapy. Grounded in the Technology Acceptance Model and therapeutic alliance theory, we develop a theory-informed annotation framework and apply a hybrid LLM-human pipeline to analyze evaluative language, adoption-related attitudes, and relational alignment at scale. Our results show that engagement is shaped primarily by narrated outcomes, trust, and response quality, rather than emotional bond alone. Positive sentiment is most strongly associated with task and goal alignment, while companionship-oriented use more often involves misaligned alliances and reported risks such as dependence and symptom escalation. Overall, this work demonstrates how theory-grounded constructs can be operationalized in large-scale discourse analysis and highlights the importance of studying how users interpret language technologies in sensitive, real-world contexts.

Haruna Irino, Satoko Sasagawa, Chika Yokoyama et al.

Background: This study aimed to determine the prevalence of persistent perinatal depressive symptoms and to identify the psychosocial characteristics associated with persistent symptoms among Japanese women. While persistent perinatal depression significantly impact both mothers and children, evidence from Japan remains limited. Methods: A longitudinal online survey was conducted at three time points: during pregnancy, 1-month postpartum, and 6-month postpartum. Depressive symptoms were assessed using the Edinburgh Postnatal Depression Scale (EPDS), and daily impairment was measured using the difficulty item of the Patient Health Questionnaire-9. The EPDS cutoff score was 13 during pregnancy and 9 for postpartum. Group differences were examined using chi-square tests and one-way ANOVAs, followed by multivariable logistic regression. Results: Among the 1039 participants (31.85±4.31 years), those who scored over cutoff during pregnancy, 1-month postpartum, and 6-month postpartum were 9.5%, 12.5%, and 11.3%, respectively. Depressive symptoms remained consistently high at all three-time points for 41 women (3.9%), fluctuated for 238 (22.9%), and remained low for 760 (73.2%). Persistent perinatal depressive symptoms were associated with greater daily impairment and were predicted by lower household income, personal psychiatric history, fewer sleeping hours at 1-month postpartum, and fear of COVID-19. Limitations: Data were self-reported via the internet, and daily impairment was assessed using a single item. Conclusions: Persistent perinatal depressive symptoms are associated with greater daily impairment and specific psychosocial vulnerabilities. Given their potential adverse effects on both mothers and children, continuous monitoring and support for depressive symptoms throughout the perinatal period are essential from a public health perspective.

Giulia Lupi, Seol Ah Park, Martin Ambroz et al.

In this paper, we propose a new workflow to analyze macrophage motion during wound healing. These immune cells are attracted to the wound after an injury and they move showing both directional and random motion. Thus, first, we smooth the trajectories and we separate the random from the directional parts of the motion. The smoothing model is based on curve evolution where the curve motion is influenced by the smoothing term and the attracting term. Once we obtain the random sub-trajectories, we analyze them using the mean squared displacement to characterize the type of diffusion. Finally, we compute the velocities on the smoothed trajectories and use them as sparse samples to reconstruct the wound attractant field. To do that, we consider a minimization problem for the vector components and lengths, which leads to solving the Laplace equation with Dirichlet conditions for the sparse samples and zero Neumann boundary conditions on the domain boundary.

Janak Kapuriya, Aman Singh, Jainendra Shukla et al.

Traditional mental health support systems often generate responses based solely on the user's current emotion and situations, resulting in superficial interventions that fail to address deeper emotional needs. This study introduces a novel framework by integrating spiritual wisdom from the Bhagavad Gita with advanced large language model GPT-4o to enhance emotional well-being. We present the GITes (Gita Integrated Therapy for Emotional Support) dataset, which enhances the existing ExTES mental health dataset by including 10,729 spiritually guided responses generated by GPT-4o and evaluated by domain experts. We benchmark GITes against 12 state-of-the-art LLMs, including both mental health specific and general purpose models. To evaluate spiritual relevance in generated responses beyond what conventional n-gram based metrics capture, we propose a novel Spiritual Insight metric and automate assessment via an LLM as jury framework using chain-of-thought prompting. Integrating spiritual guidance into AI driven support enhances both NLP and spiritual metrics for the best performing LLM Phi3-Mini 3.2B Instruct, achieving improvements of 122.71% in ROUGE, 126.53% in METEOR, 8.15% in BERT score, 15.92% in Spiritual Insight, 18.61% in Sufficiency and 13.22% in Relevance compared to its zero-shot counterpart. While these results reflect substantial improvements across automated empathy and spirituality metrics, further validation in real world patient populations remains a necessary step. Our findings indicate a strong potential for AI systems enriched with spiritual guidance to enhance user satisfaction and perceived support outcomes. The code and dataset will be publicly available to advance further research in this emerging area.

Zhanyi Ding, Zhongyan Wang, Yeyubei Zhang et al.

Social media platforms provide valuable insights into mental health trends by capturing user-generated discussions on conditions such as depression, anxiety, and suicidal ideation. Machine learning (ML) and deep learning (DL) models have been increasingly applied to classify mental health conditions from textual data, but selecting the most effective model involves trade-offs in accuracy, interpretability, and computational efficiency. This study evaluates multiple ML models, including logistic regression, random forest, and LightGBM, alongside deep learning architectures such as ALBERT and Gated Recurrent Units (GRUs), for both binary and multi-class classification of mental health conditions. Our findings indicate that ML and DL models achieve comparable classification performance on medium-sized datasets, with ML models offering greater interpretability through variable importance scores, while DL models are more robust to complex linguistic patterns. Additionally, ML models require explicit feature engineering, whereas DL models learn hierarchical representations directly from text. Logistic regression provides the advantage of capturing both positive and negative associations between features and mental health conditions, whereas tree-based models prioritize decision-making power through split-based feature selection. This study offers empirical insights into the advantages and limitations of different modeling approaches and provides recommendations for selecting appropriate methods based on dataset size, interpretability needs, and computational constraints.

Mithat Can Ozgun, Jiahuan Pei, Koen Hindriks et al.

LLM-based agents have emerged as transformative tools capable of executing complex tasks through iterative planning and action, achieving significant advancements in understanding and addressing user needs. Yet, their effectiveness remains limited in specialized domains such as mental health diagnosis, where they underperform compared to general applications. Current approaches to integrating diagnostic capabilities into LLMs rely on scarce, highly sensitive mental health datasets, which are challenging to acquire. These methods also fail to emulate clinicians' proactive inquiry skills, lack multi-turn conversational comprehension, and struggle to align outputs with expert clinical reasoning. To address these gaps, we propose DSM5AgentFlow, the first LLM-based agent workflow designed to autonomously generate DSM-5 Level-1 diagnostic questionnaires. By simulating therapist-client dialogues with specific client profiles, the framework delivers transparent, step-by-step disorder predictions, producing explainable and trustworthy results. This workflow serves as a complementary tool for mental health diagnosis, ensuring adherence to ethical and legal standards. Through comprehensive experiments, we evaluate leading LLMs across three critical dimensions: conversational realism, diagnostic accuracy, and explainability. Our datasets and implementations are fully open-sourced.

Maheshwari Neelam, Kamaldeep Bhui, Trent Cowan et al.

This study examines how the desiccation of Utah Great Salt Lake GSL, exacerbated by anthropogenic changes, poses significant health risks, particularly communities mental health. Reduced water inflow has exposed the lakebed, increasing airborne particulate matter PM2.5 and dust storms, which impact air quality. By integrating diverse datasets spanning from 1980 to present including insitu measurements, satellite imagery, and reanalysis products this study synthesizes hydrological, atmospheric, and epidemiological variables to comprehensively track the extent of the GSL surface water, local air quality fluctuations, and their effects on community mental health. The findings indicate a clear relationship between higher pollution days and more severe depressive symptoms. Specifically, individuals exposed to 22 days with PM2.5 levels above the World Health Organizations 24 hour guideline of 15 ug per m3 were more likely to experience severe depressive symptoms. Our results also suggest that people experiencing more severe depression not only face a higher number of high pollution days but also encounter such days more frequently. The study highlights the interconnectedness of poor air quality, environmental degradation and mental health emphasizing the need for more sustainable economic growth in the region.

Boris Kovalerchuk, Brent D. Fegley

Difficult decision-making problems abound in various disciplines and domains. The proliferation of generative techniques, especially large language models (LLMs), has excited interest in using them for decision support. However, LLMs cannot yet resolve missingness in their training data, leading to hallucinations. Retrieval-Augmented Generation (RAG) enhances LLMs by incorporating external information retrieval, reducing hallucinations and improving accuracy. Yet, RAG and related methods are only partial solutions, as they may lack access to all necessary sources or key missing information. Even everyday issues often challenge LLMs' abilities. Submitting longer prompts with context and examples is one approach to address knowledge gaps, but designing effective prompts is non-trivial and may not capture complex mental models of domain experts. For tasks with missing critical information, LLMs are insufficient, as are many existing systems poorly represented in available documents. This paper explores how LLMs can make decision-making more efficient, using a running example of evaluating whether to respond to a call for proposals. We propose a technology based on optimized human-machine dialogue and monotone Boolean and k-valued functions to discover a computationally tractable personal expert mental model (EMM) of decision-making. Our EMM algorithm for LLM prompt engineering has four steps: (1) factor identification, (2) hierarchical structuring of factors, (3) generating a generalized expert mental model specification, and (4) generating a detailed generalized expert mental model from that specification.

Manoj K. Pandey, Harsiddhi Thakral, Prabha Mishra et al.

This review article delves into the multifaceted realm of threesomes, examining their impact on societal norms and providing insight into the evolving landscape of human sexuality. It investigates various aspects including prevalence, motivations, configurations, and impacts, while emphasizing the significance of cultural acceptance and implications for relational well-being. Notable gender differences in participation and fantasies are explored, with an emphasis on how diverse configurations challenge established norms. Motivations for engaging in threesomes span from curiosity to intimacy enhancement, reflecting evolving attitudes toward sexual behaviors. The potential influence of threesomes on monogamous relationships is discussed, highlighting the importance of clear communication and safe sex practices. Additionally, the role of female agency and pornography in shaping perceptions and experiences is examined. Recommendations for researchers and mental health professionals underscore the need to address societal stigmas, explore cultural contexts, promote comprehensive sex education, empower female agency, and enhance communication skills. Understanding threesomes contributes to a deeper understanding of human sexuality, fostering inclusivity toward diverse sexual experiences and relationships. Further research is advocated to bridge existing gaps and cultivate a more empathetic society, recognizing threesomes as a complex aspect of human intimacy deserving of nuanced discussions and inclusive research.

Sarah Ballif, Robert Oehler, Catherine Kelly et al.

ABSTRACT Childhood emotional maltreatment is related to an increase in negative psychological outcomes in adulthood, such as psychopathology; however, less research has examined how emotional maltreatment leads to a decrease in positive outcomes, such as flourishing. This study examines psychological flexibility, which is operationalized as the ability to overcome negative emotions to accomplish valued goals, as a potential mediator and moderator in the relationship between emotional maltreatment in childhood and flourishing. College student participants (N = 262) were given the Personalized Psychological Flexibility Index (PPFI), the emotional abuse and neglect subscales of the Childhood Trauma Questionnaire (CTQ), and the Flourishing Scale (FS). Psychological flexibility was found to be a mediator and moderator in the relationship between emotional maltreatment and flourishing. The specific subscales of the PPFI were examined and acceptance and lack of avoidance were significant moderators in the relationship between emotional maltreatment and flourishing, while harnessing was not. Identified goals were examined but did not have a significant effect on flourishing. Therapies that emphasize psychological flexibility, such as Acceptance and Commitment Therapy (ACT), can be an effective treatment to reduce the effect of emotional maltreatment on an individual's ability to flourish.

Rohan Jayasuriya, Shehan Williams, Ruwanthi Perera et al.

The study aims were firstly to identify potential traumatic events (PTE) and stressors faced by the population in the districts of Northern Sri Lanka exposed to the decades-long conflict and secondly to predict Post Traumatic Stress Disorder (PTSD) based on conceptually derived composites of the PTE. https://www.editorialmanager.com/SSMMH/Data for this study were collected in 2015. The sample consisted of 1526 individuals in northern Sri Lanka who were all exposed to the prolonged war and who had all previously participated in a baseline study in 2014. Four composites of PTEs were identified: extreme violence; traumatic losses; exposure to conflict and ongoing stressors. The data were analyzed using path analysis and mediation models. The results revealed that these stressors explained 40% of the variance in PTSD. The direct path from “extreme violence” had the highest effect on PTSD, even six years after exposure. Mediation analysis identified that ongoing stressors mediated the relationship of exposure to conflict with PTSD, lending support to the “daily stressors” model among internally displaced persons (IDPs). The results suggest that early specific intervention will benefit those exposed to extreme violence and broader Mental Health and Psychosocial Support (MHPSS) approaches for ongoing stressors to reduce mental distress of this population.

Ruiyi Wang, Stephanie Milani, Jamie C. Chiu et al.

Mental illness remains one of the most critical public health issues. Despite its importance, many mental health professionals highlight a disconnect between their training and actual real-world patient practice. To help bridge this gap, we propose PATIENT-Ψ, a novel patient simulation framework for cognitive behavior therapy (CBT) training. To build PATIENT-Ψ, we construct diverse patient cognitive models based on CBT principles and use large language models (LLMs) programmed with these cognitive models to act as a simulated therapy patient. We propose an interactive training scheme, PATIENT-Ψ-TRAINER, for mental health trainees to practice a key skill in CBT -- formulating the cognitive model of the patient -- through role-playing a therapy session with PATIENT-Ψ. To evaluate PATIENT-Ψ, we conducted a comprehensive user study of 13 mental health trainees and 20 experts. The results demonstrate that practice using PATIENT-Ψ-TRAINER enhances the perceived skill acquisition and confidence of the trainees beyond existing forms of training such as textbooks, videos, and role-play with non-patients. Based on the experts' perceptions, PATIENT-Ψ is perceived to be closer to real patient interactions than GPT-4, and PATIENT-Ψ-TRAINER holds strong promise to improve trainee competencies. Our code and data are released at \url{https://github.com/ruiyiw/patient-psi}.

Amelie Gyrard, Seyedali Mohammadi, Manas Gaur et al.

Sustainable Development Goals (SDGs) give the UN a road map for development with Agenda 2030 as a target. SDG3 "Good Health and Well-Being" ensures healthy lives and promotes well-being for all ages. Digital technologies can support SDG3. Burnout and even depression could be reduced by encouraging better preventive health. Due to the lack of patient knowledge and focus to take care of their health, it is necessary to help patients before it is too late. New trends such as positive psychology and mindfulness are highly encouraged in the USA. Digital Twins (DTs) can help with the continuous monitoring of emotion using physiological signals (e.g., collected via wearables). DTs facilitate monitoring and provide constant health insight to improve quality of life and well-being with better personalization. Healthcare DTs challenges are standardizing data formats, communication protocols, and data exchange mechanisms. As an example, ISO has the ISO/IEC JTC 1/SC 41 Internet of Things (IoT) and DTs Working Group, with standards such as "ISO/IEC 21823-3:2021 IoT - Interoperability for IoT Systems - Part 3 Semantic interoperability", "ISO/IEC CD 30178 - IoT - Data format, value and coding". To achieve those data integration and knowledge challenges, we designed the Mental Health Knowledge Graph (ontology and dataset) to boost mental health. As an example, explicit knowledge is described such as chocolate contains magnesium which is recommended for depression. The Knowledge Graph (KG) acquires knowledge from ontology-based mental health projects classified within the LOV4IoT ontology catalog (Emotion, Depression, and Mental Health). Furthermore, the KG is mapped to standards when possible. Standards from ETSI SmartM2M can be used such as SAREF4EHAW to represent medical devices and sensors, but also ITU/WHO, ISO, W3C, NIST, and IEEE standards relevant to mental health can be considered.

Jian-Qiao Zhu, Haijiang Yan, Thomas L. Griffiths

Simulating sampling algorithms with people has proven a useful method for efficiently probing and understanding their mental representations. We propose that the same methods can be used to study the representations of Large Language Models (LLMs). While one can always directly prompt either humans or LLMs to disclose their mental representations introspectively, we show that increased efficiency can be achieved by using LLMs as elements of a sampling algorithm. We explore the extent to which we recover human-like representations when LLMs are interrogated with Direct Sampling and Markov chain Monte Carlo (MCMC). We found a significant increase in efficiency and performance using adaptive sampling algorithms based on MCMC. We also highlight the potential of our method to yield a more general method of conducting Bayesian inference \textit{with} LLMs.

Birger Moell

Background: Rapid advancements in natural language processing have led to the development of large language models with the potential to revolutionize mental health care. These models have shown promise in assisting clinicians and providing support to individuals experiencing various psychological challenges. Objective: This study aims to compare the performance of two large language models, GPT-4 and Chat-GPT, in responding to a set of 18 psychological prompts, to assess their potential applicability in mental health care settings. Methods: A blind methodology was employed, with a clinical psychologist evaluating the models' responses without knowledge of their origins. The prompts encompassed a diverse range of mental health topics, including depression, anxiety, and trauma, to ensure a comprehensive assessment. Results: The results demonstrated a significant difference in performance between the two models (p > 0.05). GPT-4 achieved an average rating of 8.29 out of 10, while Chat-GPT received an average rating of 6.52. The clinical psychologist's evaluation suggested that GPT-4 was more effective at generating clinically relevant and empathetic responses, thereby providing better support and guidance to potential users. Conclusions: This study contributes to the growing body of literature on the applicability of large language models in mental health care settings. The findings underscore the importance of continued research and development in the field to optimize these models for clinical use. Further investigation is necessary to understand the specific factors underlying the performance differences between the two models and to explore their generalizability across various populations and mental health conditions.

Akshat Choube, Vedant Das Swain, Varun Mishra

Advances in mobile and wearable technologies have enabled the potential to passively monitor a person's mental, behavioral, and affective health. These approaches typically rely on longitudinal collection of self-reported outcomes, e.g., depression, stress, and anxiety, to train machine learning (ML) models. However, the need to continuously self-report adds a significant burden on the participants, often resulting in attrition, missing labels, or insincere responses. In this work, we introduce the Scale Scores Simulation using Mental Models (SeSaMe) framework to alleviate participants' burden in digital mental health studies. By leveraging pre-trained large language models (LLMs), SeSaMe enables the simulation of participants' responses on psychological scales. In SeSaMe, researchers can prompt LLMs with information on participants' internal behavioral dispositions, enabling LLMs to construct mental models of participants to simulate their responses on psychological scales. We demonstrate an application of SeSaMe, where we use GPT-4 to simulate responses on one scale using responses from another as behavioral information. We also evaluate the alignment between human and SeSaMe-simulated responses to psychological scales. Then, we present experiments to inspect the utility of SeSaMe-simulated responses as ground truth in training ML models by replicating established depression and anxiety screening tasks from a previous study. Our results indicate SeSaMe to be a promising approach, but its alignment may vary across scales and specific prediction objectives. We also observed that model performance with simulated data was on par with using the real data for training in most evaluation scenarios. We conclude by discussing the potential implications of SeSaMe in addressing some challenges researchers face with ground-truth collection in passive sensing studies.

Nikol Figalová, Hans Joachim Bieg, Michael Schulz et al.

Drivers' role changes with increasing automation from the primary driver to a system supervisor. This study investigates how supervising an SAE L2 and L3 automated vehicle (AV) affects drivers' mental workload and sleepiness compared to manual driving. Using an AV prototype on a test track, the oscillatory brain activity of 23 adult participants was recorded during L2, L3, and manual driving. Results showed decreased mental workload and increased sleepiness in L3 drives compared to L2 and manual drives, indicated by self-report scales and changes in the frontal alpha and theta power spectral density. These findings suggest that fatigue and mental underload are significant issues in L3 driving and should be considered when designing future AV interfaces.

Stemberger Gerhard

Around 100 years ago, Max Wertheimer’s famous work “Untersuchungen zur Lehre von der Gestalt II” was published, in which he first presented what has since been widely referred to as the “Gestalt laws” (albeit not always appropriately). What is less well known is that Wertheimer at the same time dictated the fundamental theses on the development and healing of mental disorders to the German psychiatrist Heinrich Schulte for an article that can be regarded as the cornerstone for Gestalt psychological psychopathology. A comparison of the two studies shows that Wertheimer pursued the same far-reaching project in both works, namely, to decipher the “inner structural laws” of Gestalten – in the first work for the area of simple instances of seeing, in the other for the area of community in human life and its role in mental health. This shows that Wertheimer’s project of exploring the “structural laws of Gestalt” had more and something different as its goal than the often-simplified understanding of the “Gestalt laws” in textbooks and on the Internet would suggest.

Li C, Wang Y

Chen Li,1 Yangyang Wang2 1School of Media and Communication, Shanghai Jiao Tong University, Shanghai, 200240, People’s Republic of China; 2School of Communication, Soochow University, Suzhou, 215123, People’s Republic of ChinaCorrespondence: Yangyang Wang, School of Communication, Soochow University, Suzhou Industrial Park, No. 1 Wenjing Road, Suzhou, 215123, People’s Republic of China, Email yywang2023@suda.edu.cnObjective: This study constructed a theoretical model based on the social compensation theory and used it to investigate the effects of short-form video applications usage on depressive symptoms among functionally dependent adults.Methods: An empirical analysis was conducted based on a national sample of 8752 adults aged 45+ from China Family Panel Studies (CFPS) wave 2020. This study examined the effects of short-form video applications usage on depressive symptoms in functionally dependent adults by constructing linear regression models. Further, the mediating effect of interpersonal relationship, and the moderating effect of video games were then sequentially analyzed with the help of macro PROCESS4.0 tool.Results: Results showed that: (1) short-form video applications usage significantly reduced the level of depressive symptoms among functionally dependent adults; (2) interpersonal relationship exerted a mediating effect of 10.36% in the process of short-form video applications usage reducing the level of depressive symptoms among functionally dependent adults; (3) video games attenuated the healing effect of short-form video applications usage on the level of depressive symptoms in functionally dependent adults, but not significantly in the functionally dependent adults aged 60+.Conclusion: New electronic media, represented by short-form video applications, have the potential to intervene in the mental health of functionally dependent adults. Social policymakers should consider adopting relevant e-healing measures to enhance the well-being of vulnerable groups.Keywords: short-form video applications usage, functionally dependent adults, depressive symptoms, interpersonal relationship, video games