Hasil untuk "Mental healing"

Pritam Sil, Durgaprasad Karnam, Vinay Reddy Venumuddala et al.

STEM Mental models can play a critical role in assessing students' conceptual understanding of a topic. They not only offer insights into what students know but also into how effectively they can apply, relate to, and integrate concepts across various contexts. Thus, students' responses are critical markers of the quality of their understanding and not entities that should be merely graded. However, inferring these mental models from student answers is challenging as it requires deep reasoning skills. We propose MMGrader, an approach that infers the quality of students' mental models from their multimodal responses using concept graphs as an analytical framework. In our evaluation with 9 openly available models, we found that the best-performing models fall short of human-level performance. This is because they only achieved an accuracy of approximately 40%, a prediction error of 1.1 units, and a scoring distribution fairly aligned with human scoring patterns. With improved accuracy, these can be highly effective assistants to teachers in inferring the mental models of their entire classrooms, enabling them to do so efficiently and help improve their pedagogies more effectively by designing targeted help sessions and lectures that strengthen areas where students collectively demonstrate lower proficiency.

E.M. Hoogerwerf, H. Riese, P. Spinhoven et al.

Background: : This study aims to gain more insight into participants with anxiety disorders by conducting network analysis with experience sampling methodology data (i.e. data measured multiple times a day via smartphone), comparing group networks of participants diagnosed with an anxiety disorder and healthy controls. We expect the networks of participants with an anxiety disorder to be more connected and to reveal relationships between symptoms that may perpetuate problems. Methods: 224 participants, 141 diagnosed with an anxiety disorder (Group A) at baseline (T0) and 83 healthy controls (Group B), from the Netherlands Study of Depression and Anxiety participated in ESM monitoring at 9-year follow-up (T1). Anxiety disorder status was assessed at T1 as well and used to group participants into group A-1, anxiety disorder at T0 and T1, and A-2, anxiety disorder at T0 but not at T1. 6 ESM items were used to conduct network analysis. The permutation test mnet was used to inspect the networks for significant differences. Results: The autoregression coefficients of ESM-items ‘worrying’ and ‘nervousness’ were larger in Group A compared to group B, as well as the temporal influence of worrying on tiredness. The autoregression coefficients of worrying and tiredness were larger in Group A-2 than in Group A-1, those of enthusiasm were larger in Group A-1. The influence of tiredness on apathy was greater in Group A-2. Conclusions: It seems likely that participants with chronic mood- and anxiety disorders are not distinct from each other on a level of symptomatic interconnectedness, even if they differ in diagnostic categories 9 years after baseline. These findings confirm the existing information regarding the chronic course that anxiety disorders often take.

Brianna M White, Rameshwari Prasad, Nariman Ammar et al.

This study presents a narrative review of the use of digital health technologies (DHTs) and artificial intelligence to screen and mitigate risks and mental health consequences associated with ACEs among children and youth. Several databases were searched for studies published from August 2017 to August 2022. Selected studies (1) explored the relationship between digital health interventions and mitigation of negative health outcomes associated with mental health in childhood and adolescence and (2) examined prevention of ACE occurrence associated with mental illness in childhood and adolescence. A total of 18 search papers were selected, according to our inclusion and exclusion criteria, to evaluate and identify means by which existing digital solutions may be useful in mitigating the mental health consequences associated with the occurrence of ACEs in childhood and adolescence and preventing ACE occurrence due to mental health consequences. We also highlighted a few knowledge gaps or barriers to DHT implementation and usability. Findings from the search suggest that the incorporation of DHTs, if implemented successfully, has the potential to improve the quality of related care provisions for the management of mental health consequences of adverse or traumatic events in childhood, including posttraumatic stress disorder, suicidal behavior or ideation, anxiety or depression, and attention-deficit/hyperactivity disorder. The use of DHTs, machine learning tools, natural learning processing, and artificial intelligence can positively help in mitigating ACEs and associated risk factors. Under proper legal regulations, security, privacy, and confidentiality assurances, digital technologies could also assist in promoting positive childhood experiences in children and young adults, bolstering resilience, and providing reliable public health resources to serve populations in need.

Omar Khan, JooYoung Seo

The proliferation of digital mental health (DMH) tracking services promises personalized support, yet accessibility barriers limit equal access. This study investigates blind community experiences with DMH tracking services across the United States as a step toward inclusive health technology design. Working with blind advocacy organizations, we distributed a cross-sectional observational survey (n = 93) and analyzed open-ended responses using Norman and Skinner's eHealth Literacy framework. Our findings reveal significant challenges in navigation, content interpretation, and overall user experience, which impede the blind community's effective engagement with DMH tools. Results highlight the need for adaptive interfaces, accessible tracking strategies, and voice-guided interactions. These insights inform design recommendations for developers and policymakers, promoting more inclusive mental health technologies. By prioritizing accessibility, we make forward progress in ensuring that DMH tracking services fulfill their potential to support mental well-being across diverse user groups, fostering digital equality in mental health care.

Jakub Onysk, Quentin J. M. Huys

Large Language Models (LLMs) may have an important role to play in mental health by facilitating the quantification of verbal expressions used to communicate emotions, feelings and thoughts. While there has been substantial and very promising work in this area, the fundamental limits are uncertain. Here, focusing on depressive symptoms, we outline and evaluate LLM performance on three critical tests. The first test evaluates LLM performance on a novel ground-truth dataset from a large human sample (n=770). This dataset is novel as it contains both standard clinically validated quantifications of depression symptoms and specific verbal descriptions of the thoughts related to each symptom by the same individual. The performance of LLMs on this richly informative data shows an upper bound on the performance in this domain, and allow us to examine the extent to which inference about symptoms generalises. Second, we test to what extent the latent structure in LLMs can capture the clinically observed patterns. We train supervised sparse auto-encoders (sSAE) to predict specific symptoms and symptom patterns within a syndrome. We find that sSAE weights can effectively modify the clinical pattern produced by the model, and thereby capture the latent structure of relevant clinical variation. Third, if LLMs correctly capture and quantify relevant mental states, then these states should respond to changes in emotional states induced by validated emotion induction interventions. We show that this holds in a third experiment with 190 participants. Overall, this work provides foundational insights into the quantification of pathological mental states with LLMs, highlighting hard limits on the requirements of the data underlying LLM-based quantification; but also suggesting LLMs show substantial conceptual alignment.

Guy Laban, Micol Spitale, Minja Axelsson et al.

Social robots are increasingly being explored as tools to support emotional wellbeing, particularly in non-clinical settings. Drawing on a range of empirical studies and practical deployments, this paper outlines six key insights that highlight both the opportunities and challenges in using robots to promote mental wellbeing. These include (1) the lack of a single, objective measure of wellbeing, (2) the fact that robots don't need to act as companions to be effective, (3) the growing potential of virtual interactions, (4) the importance of involving clinicians in the design process, (5) the difference between one-off and long-term interactions, and (6) the idea that adaptation and personalization are not always necessary for positive outcomes. Rather than positioning robots as replacements for human therapists, we argue that they are best understood as supportive tools that must be designed with care, grounded in evidence, and shaped by ethical and psychological considerations. Our aim is to inform future research and guide responsible, effective use of robots in mental health and wellbeing contexts.

Justin Cikuru, Justin Cikuru, Philippe Kaganda et al.

BackgroundOver the past two decades, armed conflicts have intensified globally, with Africa disproportionately affected. Since January 2025, renewed violence by the March 23 Movement (M23) in eastern Democratic Republic of Congo (DRC) has generated widespread trauma, displacement, and psychological distress. Beyond emotional suffering, trauma has been linked to altered body perception and reduced body satisfaction, particularly among women survivors of violence, adding further complexity to the psychological burden.ObjectivesThis study assessed the prevalence of post-traumatic stress disorder (PTSD) among hospital patients in Bukavu and examined associations with body-related symptoms and body satisfaction. It was hypothesized that many patients would meet PTSD criteria, that affected individuals would report lower body satisfaction and greater body-related distress, and that only a small minority would have accessed psychological care.Methods and materialsData were collected at Ruhigita Clinic, South Kivu. Adults aged 18–65 completed the PTSD Checklist (PCL-5, French version) and the Bruchon-Schweitzer Body Satisfaction Questionnaire. A PTSD score ≥32 indicated clinical symptoms; adequate body satisfaction was defined as ≥3. Interviews lasted 45–60 min and included demographic and trauma-related data.ResultsA total of 356 patients participated. The mean PTSD score (M = 23.49; SD = 19.90) was below the diagnostic threshold; however, 31.5% (n = 112) met PTSD criteria. Among them, 57.1% reported dissatisfaction with body appearance, compared to 32.4% of non-PTSD participants. PTSD was significantly associated with somatic symptoms such as hypertension, diabetes, stomach pain, insomnia, and cardiac complaints. Reported traumas included natural disasters (74.6%), interpersonal violence (73.7%), transport accidents (54.8%), and sexual assaults (54.1%). Natural disasters, particularly floods and wildfires, showed strong associations with PTSD onset. Despite 80% awareness of psychological services, only 9.8% had consulted a clinical psychologist. Gender differences emerged: women relied mainly on religious or spiritual support, while men favoured traditional practices.ConclusionThis study confirms a strong link between PTSD, body dissatisfaction, and somatic symptoms in a conflict-affected population. Despite high awareness of distress, mental health service use remains low. Findings highlight the need for integrative, culturally sensitive interventions that respect local understandings of trauma and healing while addressing urgent gaps in psychological care.

Jiachen Li, Justin Steinberg, Elizabeth Mynatt et al.

Mental health has become a growing concern among university students. While medication is a common treatment, understanding how university students manage their medication for mental health symptoms in real-world practice has not been fully explored. In this study, we conducted semi-structured interviews with university students to understand the unique challenges in the mental health medication management process and their coping strategies, particularly examining the role of various technologies in this process. We discovered that due to struggles with self-acceptance and the interdependent relationship between medication, symptoms, schedules, and life changes, the medication management process for students was a highly dynamic journey involving frequent dosage changes. Thus, students adopted flexible strategies of using minimal technology to manage their medication in different situations while maintaining a high degree of autonomy. Based on our findings, we propose design implications for future technologies to seamlessly integrate into their daily lives and assist students in managing their mental health medications.

Jinwen Tang, Yi Shang

This study introduces 'Psycho Analyst', a custom GPT model based on OpenAI's GPT-4, optimized for pre-screening mental health disorders. Enhanced with DSM-5, PHQ-8, detailed data descriptions, and extensive training data, the model adeptly decodes nuanced linguistic indicators of mental health disorders. It utilizes a dual-task framework that includes binary classification and a three-stage PHQ-8 score computation involving initial assessment, detailed breakdown, and independent assessment, showcasing refined analytic capabilities. Validation with the DAIC-WOZ dataset reveals F1 and Macro-F1 scores of 0.929 and 0.949, respectively, along with the lowest MAE and RMSE of 2.89 and 3.69 in PHQ-8 scoring. These results highlight the model's precision and transformative potential in enhancing public mental health support, improving accessibility, cost-effectiveness, and serving as a second opinion for professionals.

Ziyu Wang, Zhongqi Yang, Iman Azimi et al.

Mental health conditions, prevalent across various demographics, necessitate efficient monitoring to mitigate their adverse impacts on life quality. The surge in data-driven methodologies for mental health monitoring has underscored the importance of privacy-preserving techniques in handling sensitive health data. Despite strides in federated learning for mental health monitoring, existing approaches struggle with vulnerabilities to certain cyber-attacks and data insufficiency in real-world applications. In this paper, we introduce a differential private federated transfer learning framework for mental health monitoring to enhance data privacy and enrich data sufficiency. To accomplish this, we integrate federated learning with two pivotal elements: (1) differential privacy, achieved by introducing noise into the updates, and (2) transfer learning, employing a pre-trained universal model to adeptly address issues of data imbalance and insufficiency. We evaluate the framework by a case study on stress detection, employing a dataset of physiological and contextual data from a longitudinal study. Our finding show that the proposed approach can attain a 10% boost in accuracy and a 21% enhancement in recall, while ensuring privacy protection.

Rohan Kumar Gupta, Rohit Sinha

Self-supervised learning (SSL) has been investigated to generate task-agnostic representations across various domains. However, such investigation has not been conducted for detecting multiple mental disorders. The rationale behind the existence of a task-agnostic representation lies in the overlapping symptoms among multiple mental disorders. Consequently, the behavioural data collected for mental health assessment may carry a mixed bag of attributes related to multiple disorders. Motivated by that, in this study, we explore a task-agnostic representation derived through SSL in the context of detecting major depressive disorder (MDD) and post-traumatic stress disorder (PTSD) using audio and video data collected during interactive sessions. This study employs SSL models trained by predicting multiple fixed targets or masked frames. We propose a list of fixed targets to make the generated representation more efficient for detecting MDD and PTSD. Furthermore, we modify the hyper-parameters of the SSL encoder predicting fixed targets to generate global representations that capture varying temporal contexts. Both these innovations are noted to yield improved detection performances for considered mental disorders and exhibit task-agnostic traits. In the context of the SSL model predicting masked frames, the generated global representations are also noted to exhibit task-agnostic traits.

Ava Elizabeth Scott, Lev Tankelevitch, Sean Rintel

Ineffective meetings due to unclear goals are major obstacles to productivity, yet support for intentionality is surprisingly scant in our meeting and allied workflow technologies. To design for intentionality, we need to understand workers' attitudes and practices around goals. We interviewed 21 employees of a global technology company and identified contrasting mental models of meeting goals: meetings as a means to an end, and meetings as an end in themselves. We explore how these mental models impact how meeting goals arise, goal prioritization, obstacles to considering goals, and how lack of alignment around goals may create tension between organizers and attendees. We highlight the challenges in balancing preparation, constraining scope, and clear outcomes, with the need for intentional adaptability and discovery in meetings. Our findings have implications for designing systems which increase effectiveness in meetings by catalyzing intentionality and reducing tension in the organisation of meetings.

Kenji Phang, Siddharth Saarathi Pradhan, Chino Ikwuegbu et al.

Mental health is a pressing concern in today's digital age, particularly among youth who are deeply intertwined with technology. Despite the influx of technology solutions addressing mental health issues, youth often remain sidelined during the design process. While co-design methods have been employed to improve participation by youth, many such initiatives are limited to design activities and lack training for youth to research and develop solutions for themselves. In this case study, we detail our 8-week remote, collaborative research initiative called Youth WellTech, designed to facilitate remote co-design sprints aimed at equipping youth with the tools and knowledge to envision and design tech futures for their own communities. We pilot this initiative with 12 student technology evangelists across 8 countries globally to foster the sharing of mental health challenges and diverse perspectives. We highlight insights from our experiences running this global program remotely, its structure, and recommendations for co-research.

Sandeep Grover, Priyanka Pilania

Vaginismus is a complex condition to manage, and in the presence of a partner, management becomes more complex as it involves the engagement of both partners. Besides addressing vaginismus, the management may involve addressing additional issues such as marital disharmony, situational erectile dysfunction in the partner, infertility, mental morbidity, and other associated psychosocial problems. There is a lack of data on the management of vaginismus in a woman with a partner with a psychotic disorder. In this report, we describe a couple in which the female partner had vaginismus that led to a lack of consummation of marriage for six years, and the male partner had schizophrenia. We discuss the challenges faced in the successful management of vaginismus by using an eclectic approach.

Sena Akbay-Safi, Zeynep Simsek

As the Syrian crisis has reached its 10th year, this paper explores posttraumatic growth and the related factors among Syrian refugees in Turkey, with the aim of having a better understanding of the predictors of PTG and the risk factors that may play a role in the inhibition of the growth. In this cross-sectional study, 217 displaced Syrian refugees completed the Post Traumatic Growth Inventory (PTGI), Harvard Trauma Questionnaire (HTQ), Brief COPE, General Self-Efficacy Scale (GSES), PTSD Checklist for DSM-5 (PCL-5), and sociodemographic information form by a self-reported online survey. Risk and protective factors were associated in bivariate analysis with growth p<0.05 and were retained in multiple regression models to control the confounders. Participants’ 60.7% were females, and the mean age was 32.6± 9.4 years. Growth was found to be highest in the Personal Strengths, New Possibilities and Appreciation of Life, Relating to Others, and Spiritual Change. While the consistency of job education, economic status, education level, number of children, level of self-efficacy, level of Turkish, active coping style, religious coping, and self-distraction (p<0.05) were protective factors, the number of traumatic events, and the PTSD symptoms and self-blame were risk factors (p<0.05) in multiple analysis. However, no significant relationship was found for the variables of marital status, taking support, self-distraction, emotional and instrumental support, positive reframing, planning, cultural perception, working type, and age (p>0.05). In conclusion, the findings have provided valuable insight into the domains of the growth among Syrian refugees and discussed both clinical and research-based future recommendations that could be made to improve the mental health of the refugees based on the obtained results. Key implications for practice • Mental health practitioners should focus on interventions highlighting personal strengths that increase active coping skills and self-efficacy and reduce self-blame • Policymakers should take actions to prevent the retraumatization of the refugees by considering the psychological impact of the lack of the host country’s language and the inconsistency of the refugees’ work with their education • Mental health researchers should focus on factors that explain posttraumatic growth and developing intervention tools that promote growth

Hanwen Ye, Tatiana Moreno, Adrianne Alpern et al.

Mental health diseases affect children's lives and well-beings which have received increased attention since the COVID-19 pandemic. Analyzing psychiatric clinical notes with topic models is critical to evaluating children's mental status over time. However, few topic models are built for longitudinal settings, and most existing approaches fail to capture temporal trajectories for each document. To address these challenges, we develop a dynamic topic model with consistent topics and individualized temporal dependencies on the evolving document metadata. Our model preserves the semantic meaning of discovered topics over time and incorporates heterogeneity among documents. In particular, when documents can be categorized, we propose a classifier-free approach to maximize topic heterogeneity across different document groups. We also present an efficient variational optimization procedure adapted for the multistage longitudinal setting. In this case study, we apply our method to the psychiatric clinical notes from a large tertiary pediatric hospital in Southern California and achieve a 38% increase in the overall coherence of extracted topics. Our real data analysis reveals that children tend to express more negative emotions during state shutdowns and more positive when schools reopen. Furthermore, it suggests that sexual and gender minority (SGM) children display more pronounced reactions to major COVID-19 events and a greater sensitivity to vaccine-related news than non-SGM children. This study examines children's mental health progression during the pandemic and offers clinicians valuable insights to recognize disparities in children's mental health related to their sexual and gender identities.

Haijian Shao, Ming Zhu, Shengjie Zhai

Amid growing global mental health concerns, particularly among vulnerable groups, natural language processing offers a tremendous potential for early detection and intervention of people's mental disorders via analyzing their postings and discussions on social media platforms. However, ultra-sparse training data, often due to vast vocabularies and low-frequency words, hinders the analysis accuracy. Multi-labeling and Co-occurrences of symptoms may also blur the boundaries in distinguishing similar/co-related disorders. To address these issues, we propose a novel semantic feature preprocessing technique with a three-folded structure: 1) mitigating the feature sparsity with a weak classifier, 2) adaptive feature dimension with modulus loops, and 3) deep-mining and extending features among the contexts. With enhanced semantic features, we train a machine learning model to predict and classify mental disorders. We utilize the Reddit Mental Health Dataset 2022 to examine conditions such as Anxiety, Borderline Personality Disorder (BPD), and Bipolar-Disorder (BD) and present solutions to the data sparsity challenge, highlighted by 99.81% non-zero elements. After applying our preprocessing technique, the feature sparsity decreases to 85.4%. Overall, our methods, when compared to seven benchmark models, demonstrate significant performance improvements: 8.0% in accuracy, 0.069 in precision, 0.093 in recall, 0.102 in F1 score, and 0.059 in AUC. This research provides foundational insights for mental health prediction and monitoring, providing innovative solutions to navigate challenges associated with ultra-sparse data feature and intricate multi-label classification in the domain of mental health analysis.

Connor Gascoigne, Marta Blangiardo, Zejing Shao et al.

Factors contributing to social inequalities are also associated with negative mental health outcomes leading to disparities in mental well-being. We propose a Bayesian hierarchical model which can evaluate the impact of policies on population well-being, accounting for spatial/temporal dependencies. Building on an interrupted time series framework, our approach can evaluate how different profiles of individuals are affected in different ways, whilst accounting for their uncertainty. We apply the framework to assess the impact of the United Kingdoms welfare reform, which took place throughout the 2010s, on mental well-being using data from the UK Household Longitudinal Study. The additional depth of knowledge is essential for effective evaluation of current policy and implementation of future policy.

Kuan-Pin Su, MD, PhD

The increasing global burden calls for the development of novel approaches to tackle unmet needs in prevention and treatment of depression underlying biological, psychological and social dysregulations. Depressed patients with chronic low-grade inflammation might be classified as a subgroup of major depressive disorder (MDD); therefore, looking for antidepressant therapies from anti-inflammatory pathways could improve treatment effectiveness for this subgroup of patients. Omega-3 (or n-3) polyunsaturated fatty acids (PUFAs) are anti-inflammatory both in peripheral organs and central nervous systems and have clinically applied in the treatment and prevention of depression, cardiovascular diseases, dyslipidaemia, diabetes and arthritis. Anthropological studies suggest that human beings evolved to a modern diet with less than one-tenth of omega-3 to omega-6 PUFAs intake ratio, which leads to a constitutional bias toward chronic systemic inflammatory status to explain dramatically increasing of depression and chronic medical illnesses in modern world. The presentation is to provide our recent clinical and pre-clinical studies and an overview about the role of inflammation in “mind-body” comorbidity and present anti-inflammatory mechanisms by which n-3 PUFAs may orchestrate the molecular and cellular functions and facilitate the therapeutic pathways in chronic medical illnesses and depression.

Jane E. Cha, Karen E. Waldie, Denise Neumann et al.