Hasil untuk "Mental healing"

Farzana Habib

Abstract This study focuses on the treatment-seeking patterns of first-generation Bangladeshi migrants for mental health conditions. Data were collected during the Covid-19 pandemic through online semi-structured interviews in Eastbourne, UK, as no previous research has been conducted in this location where a group of Bangladeshi migrants reside. Bangladeshi migrants voluntarily relocated to the UK for economic or familial reasons, and their descendants continue to reside in the country. Upon migration, they retain their beliefs and values, including their practice of medical pluralism. The most prevalent mental health treatment-seeking behaviours include religious healing practices, obtaining preferred medications from different suitable sources, and travelling to holy sites for remedies while simultaneously utilising UK psychiatric care. The findings of this article demonstrate that the mental health-seeking behaviour of Bangladeshi migrants is deeply rooted in religious or faith healing practices that are transported from Bangladesh to the UK through the lenses of medical pluralism resulting in a unique phenomenon of travelling medicine and travelling treatment.

Qasim Bin Saeed, Ijaz Ahmed

The increasing prevalence of mental health disorders, such as depression, anxiety, and bipolar disorder, calls for immediate need in developing tools for early detection and intervention. Social media platforms, like Reddit, represent a rich source of user-generated content, reflecting emotional and behavioral patterns. In this work, we propose a multi-modal deep learning framework that integrates linguistic and temporal features for early detection of mental health crises. Our approach is based on the method that utilizes a BiLSTM network both for text and temporal feature analysis, modeling sequential dependencies in a different manner, capturing contextual patterns quite well. This work includes a cross-modal attention approach that allows fusion of such outputs into context-aware classification of mental health conditions. The model was then trained and evaluated on a dataset of labeled Reddit posts preprocessed using text preprocessing, scaling of temporal features, and encoding of labels. Experimental results indicate that the proposed architecture performs better compared to traditional models with a validation accuracy of 74.55% and F1-Score of 0.7376. This study presents the importance of multi-modal learning for mental health detection and provides a baseline for further improvements by using more advanced attention mechanisms and other data modalities.

Nur Shazwani Kamarudin, Ghazaleh Beigi, Lydia Manikonda et al.

There is an increasing number of virtual communities and forums available on the web. With social media, people can freely communicate and share their thoughts, ask personal questions, and seek peer-support, especially those with conditions that are highly stigmatized, without revealing personal identity. We study the state-of-the-art research methodologies and findings on mental health challenges like depression, anxiety, suicidal thoughts, from the pervasive use of social media data. We also discuss how these novel thinking and approaches can help to raise awareness of mental health issues in an unprecedented way. Specifically, this chapter describes linguistic, visual, and emotional indicators expressed in user disclosures. The main goal of this chapter is to show how this new source of data can be tapped to improve medical practice, provide timely support, and influence government or policymakers. In the context of social media for mental health issues, this chapter categorizes social media data used, introduces different deployed machine learning, feature engineering, natural language processing, and surveys methods and outlines directions for future research.

Qineng Wang, Baiqiao Yin, Pingyue Zhang et al.

Can Vision-Language Models (VLMs) imagine the full scene from just a few views, like humans do? Humans form spatial mental models naturally, internal representations of unseen space, to reason about layout, perspective, and motion. Our MindCube benchmark with 21,154 questions across 3,268 images exposes this critical gap, where existing VLMs exhibit near-random performance. Using MindCube, we systematically evaluate how well VLMs build robust spatial mental models through representing positions (cognitive mapping), orientations (perspective-taking), and dynamics (mental simulation for "what-if" movements). We then explore three approaches to help approximate spatial mental models in VLMs, focusing on incorporating unseen intermediate views, natural language reasoning chains, and cognitive maps. The significant improvement comes from a synergistic approach, "map-then-reason", that jointly trains the model to first generate a cognitive map and then reason upon it. By training models to reason over these internal maps, we boosted accuracy from 37.8% to 57.8% (+20.0%). Adding reinforcement learning pushed performance even further to 61.3% (+23.5%). Our key insight is that such scaffolding of spatial mental models, actively constructing and utilizing internal structured spatial representations with flexible reasoning processes, significantly improves understanding of unobservable space.

Hoyun Song, Huije Lee, Jisu Shin et al.

The detection of mental health problems from social media and the interpretation of these results have been extensively explored. Research has shown that incorporating clinical symptom information into a model enhances domain expertise, improving its detection and interpretation performance. While large language models (LLMs) are shown to be effective for generating explanatory rationales in mental health detection, their substantially large parameter size and high computational cost limit their practicality. Reasoning distillation transfers this ability to smaller language models (SLMs), but inconsistencies in the relevance and domain alignment of LLM-generated rationales pose a challenge. This paper investigates how rationale quality impacts SLM performance in mental health detection and explanation generation. We hypothesize that ensuring high-quality and domain-relevant rationales enhances the distillation. To this end, we propose a framework that selects rationales based on their alignment with expert clinical reasoning. Experiments show that our quality-focused approach significantly enhances SLM performance in both mental disorder detection and rationale generation. This work highlights the importance of rationale quality and offers an insightful framework for knowledge transfer in mental health applications.

Diji Yang, Minghao Liu, Chung-Hsiang Lo et al.

Vision-language models (VLMs) have shown strong performance on text-to-image retrieval benchmarks. However, bridging this success to real-world applications remains a challenge. In practice, human search behavior is rarely a one-shot action. Instead, it is often a multi-round process guided by clues in mind. That is, a mental image ranging from vague recollections to vivid mental representations of the target image. Motivated by this gap, we study the task of Mental Image Retrieval (MIR), which targets the realistic yet underexplored setting where users refine their search for a mentally envisioned image through multi-round interactions with an image search engine. Central to successful interactive retrieval is the capability of machines to provide users with clear, actionable feedback; however, existing methods rely on indirect or abstract verbal feedback, which can be ambiguous, misleading, or ineffective for users to refine the query. To overcome this, we propose GenIR, a generative multi-round retrieval paradigm leveraging diffusion-based image generation to explicitly reify the AI system's understanding at each round. These synthetic visual representations provide clear, interpretable feedback, enabling users to refine their queries intuitively and effectively. We further introduce a fully automated pipeline to generate a high-quality multi-round MIR dataset. Experimental results demonstrate that GenIR significantly outperforms existing interactive methods in the MIR scenario. This work establishes a new task with a dataset and an effective generative retrieval method, providing a foundation for future research in this direction

Vimaleswar A, Prabhu Nandan Sahu, Nilesh Kumar Sahu et al.

Mental health plays a crucial role in the overall well-being of an individual. In recent years, digital platforms have increasingly been used to expand mental health and emotional support. However, there are persistent challenges related to limited user accessibility, internet connectivity, and data privacy, which highlight the need for an offline, smartphone-based solutions. To address these challenges, we propose EmoSApp (Emotional Support App): an entirely offline, smartphone-based conversational app designed to provide mental health and emotional support. EmoSApp leverages a language model, specifically the LLaMA-3.2-1B-Instruct, which is fine-tuned and quantized on a custom-curated ``Knowledge Dataset'' comprising 14,582 mental health QA pairs along with multi-turn conversational data, enabling robust domain expertise and fully on-device inference on resource-constrained smartphones. Through qualitative evaluation with students and mental health professionals, we demonstrate that EmoSApp has the ability to respond coherently and empathetically, provide relevant suggestions to user's mental health problems, and maintain interactive dialogue. Additionally, quantitative evaluations on nine commonsense and reasoning benchmarks, along with two mental health specific datasets, demonstrate EmoSApp's effectiveness in low-resource settings. By prioritizing on-device deployment and specialized domain-specific adaptation, EmoSApp serves as a blueprint for future innovations in portable, secure, and highly tailored AI-driven mental health support.

Lauren A. Magee, Jennifer Leaño, Beatrice Beverly et al.

Firearm injury is the leading cause of death among youth in the US direct and indirect exposure is associated with increased mental health needs, particularly depression, yet few community-based interventions led by credible messengers exist to address co-occurring violence and depression among youth. This paper describes a pilot study to adapt a digital intervention for youth exposed to firearm violence. iDOVE3.0 was adapted from an evidence-based emergency department intervention for youth (ages 13–17) into a community setting (defined as a community-based organization outside an institutional setting) in Indianapolis, Indiana. This single-arm pilot study aimed to recruit 20 youth between September 1, 2024 and December 31, 2025. Participant recruitment is ongoing, and to-date we have screened 16 youth for mild to moderate depression and violence exposure and enrolled five youth. Violence patterns and depressive symptoms were assessed at baseline and follow-up at 2, 4 and 8 months. This descriptive study offers insights into the adaptation of a clinical intervention and implementation process into a community setting. Understanding how digital-based interventions can expand community violence interventions and how credible messengers can improve the acceptability of digital interventions are promising approaches to address co-occurring depression and violence among youth in need. Future studies will examine feasibility, acceptability and preliminary efficacy among the pilot study cohort.

Emel Genç

This qualitative study explores how couples raising a child with special needs manage caregiving-related stress and sustain their psychological and relational well-being. While prior research has predominantly focused on the psychological burden and dysfunction experienced by such families, this study adopts a resilience-oriented perspective grounded in the Family Resilience Framework. Using phenomenological research design, semi-structured interviews were conducted with 10 married individuals from 10 different couples to uncover the lived experiences and adaptive strategies employed in the face of ongoing caregiving challenges. Thematic analysis revealed three overarching themes: 1) Mutual Spousal Support, including shared caregiving responsibilities and emotional responsiveness; 2) Social Network, comprising support from extended family, community, and social media; and 3) Religious Belief in Coping, which involved framing hardship through spiritual narratives and deriving strength from worship. Findings highlight the internal and external resources that foster resilience and underscore the significance of meaning-making, relational rituals, and community-based support in enhancing family well-being. This study contributes to the literature by shifting the focus from pathology to strength, offering practical insights for family-centered interventions, mental health services, and policy frameworks that aim to empower caregiving couples.

Robert Tanguay, Nickie Mathew

Kimberly J. Mitchell, Victoria Banyard, Michele L. Ybarra et al.

ABSTRACT Suicidal behavior is a critical mental health problem in the United States, and this is particularly true for youth with social identities that are historically minoritized and discriminated against. There is also a growing awareness of the influence of social determinants of health (SDOH) on mental health. The current study examines links between one's own thoughts of suicide and the dose of exposure to other people's suicidal thoughts, often labeled contagion, within the context of different minoritized identity groups and SDOH deficits. Project Lift Up is a national longitudinal study of youth aged 13–22 years designed to understand exposure to suicidal thoughts and behaviors in social networks. A cohort of 4981 adolescents and young adults was recruited online via social media between June 13, 2022, and October 30, 2023. Youth who knew one person with suicidal thoughts were 1.75 times (p = 0.002) more likely than those without such exposure to self‐report recent thought of suicide and those who knew between 2 and 4 people were 1.81 times more likely (p < 0.001). These odds increased to 3.47 (p < 0.001) if the youth knew five or more people with thoughts of suicide. Youth who identified with a social identity group that experiences marginalization and systemic oppression (based on race, ethnicity, disability status, gender, and sexual identity) and exposure to suicidal thoughts had higher odds of recent thoughts of suicide compared to non‐minoritized and non‐exposed youth. SDOH also explained unique variance in self‐reported ideation. Exposure to other people's suicidal thoughts is associated with one's own thoughts of suicide and the number of people exposed to amplifies this effect, especially for individuals also experiencing adversity burden from SDOH. Results add to the extant literature documenting the higher odds of suicidal ideation that minoritized youth face.

Haider Raza, Mohsin Ali, Vishal Krishna Singh et al.

Precise identification and detection of the Mental Foramen are crucial in dentistry, impacting procedures such as impacted tooth removal, cyst surgeries, and implants. Accurately identifying this anatomical feature facilitates post-surgery issues and improves patient outcomes. Moreover, this study aims to accelerate dental procedures, elevating patient care and healthcare efficiency in dentistry. This research used Deep Learning methods to accurately detect and segment the Mental Foramen from panoramic radiograph images. Two mask types, circular and square, were used during model training. Multiple segmentation models were employed to identify and segment the Mental Foramen, and their effectiveness was evaluated using diverse metrics. An in-house dataset comprising 1000 panoramic radiographs was created for this study. Our experiments demonstrated that the Classical UNet model performed exceptionally well on the test data, achieving a Dice Coefficient of 0.79 and an Intersection over Union (IoU) of 0.67. Moreover, ResUNet++ and UNet Attention models showed competitive performance, with Dice scores of 0.675 and 0.676, and IoU values of 0.683 and 0.671, respectively. We also investigated transfer learning models with varied backbone architectures, finding LinkNet to produce the best outcomes. In conclusion, our research highlights the efficacy of the classical Unet model in accurately identifying and outlining the Mental Foramen in panoramic radiographs. While vital, this task is comparatively simpler than segmenting complex medical datasets such as brain tumours or skin cancer, given their diverse sizes and shapes. This research also holds value in optimizing dental practice, benefiting practitioners and patients.

Wenhao Lu, Xufeng Zhao, Josua Spisak et al.

Can emergent language models faithfully model the intelligence of decision-making agents? Though modern language models exhibit already some reasoning ability, and theoretically can potentially express any probable distribution over tokens, it remains underexplored how the world knowledge these pretrained models have memorized can be utilized to comprehend an agent's behaviour in the physical world. This study empirically examines, for the first time, how well large language models (LLMs) can build a mental model of agents, termed agent mental modelling, by reasoning about an agent's behaviour and its effect on states from agent interaction history. This research may unveil the potential of leveraging LLMs for elucidating RL agent behaviour, addressing a key challenge in eXplainable reinforcement learning (XRL). To this end, we propose specific evaluation metrics and test them on selected RL task datasets of varying complexity, reporting findings on agent mental model establishment. Our results disclose that LLMs are not yet capable of fully mental modelling agents through inference alone without further innovations. This work thus provides new insights into the capabilities and limitations of modern LLMs.

Anshuman Pasupalak, Zeng Wu, Massimo Pica Ciamarra

In processes such as embryo shaping, wound healing, and malignant cell invasion, epithelial cells transition between dispersed phases, where the cells move independently, and condensed phases, where they aggregate and deform to close gaps, forming confluent tissues. Understanding how cells regulate these transitions and how these transitions differ from those of inert particles remains an open challenge. Addressing these questions requires linking the macroscopic properties of tissues to the mechanical characteristics and active responses of individual cells, driven by sub-cellular processes. Here, we introduce a computational model that incorporates key factors such as cell deformability, lamellipodium-driven dynamics, cell-junction-mediated adhesion, and contact inhibition of locomotion (CIL)-a process where cells alter their motion upon contact with others. We demonstrate how these factors, along with cell density, regulate the dynamical and mechanical properties of tissues. We show that CIL imparts unique living-like behaviors to cells and tissues by reducing density fluctuations. This reduction in fluctuations affects the dynamics: it inhibits cell motion in steady states but promotes it in the presence of gaps, accelerating wound healing. Furthermore, the stabilization of tensile states by CIL, which would otherwise fracture, enables the formation of tensile force chains.

Ramin Safa, S. A. Edalatpanah, Ali Sorourkhah

Mental disorders such as depression and suicidal ideation are hazardous, affecting more than 300 million people over the world. However, on social media, mental disorder symptoms can be observed, and automated approaches are increasingly capable of detecting them. The considerable number of social media users and the tremendous quantity of user-generated data on social platforms provide a unique opportunity for researchers to distinguish patterns that correlate with mental status. This research offers a roadmap for analysis, where mental state detection can be based on machine learning techniques. We describe the common approaches for predicting and identifying the disorder using user-generated content. This research is organized according to the data collection, feature extraction, and prediction algorithms. Furthermore, we review several recent studies conducted to explore different features of candidate profiles and their analytical methods. Following, we debate various aspects of the development of experimental auto-detection frameworks for identifying users who suffer from disorders, and we conclude with a discussion of future trends. The introduced methods can help complement screening procedures, identify at-risk people through social media monitoring on a large scale, and make disorders easier to treat in the future.

Shaoxiong Ji, Tianlin Zhang, Kailai Yang et al.

Large Language Models (LLMs) have become valuable assets in mental health, showing promise in both classification tasks and counseling applications. This paper offers a perspective on using LLMs in mental health applications. It discusses the instability of generative models for prediction and the potential for generating hallucinatory outputs, underscoring the need for ongoing audits and evaluations to maintain their reliability and dependability. The paper also distinguishes between the often interchangeable terms ``explainability'' and ``interpretability'', advocating for developing inherently interpretable methods instead of relying on potentially hallucinated self-explanations generated by LLMs. Despite the advancements in LLMs, human counselors' empathetic understanding, nuanced interpretation, and contextual awareness remain irreplaceable in the sensitive and complex realm of mental health counseling. The use of LLMs should be approached with a judicious and considerate mindset, viewing them as tools that complement human expertise rather than seeking to replace it.

Benedikt Janzen

This paper studies the short-term effects of ambient temperature on mental health using data on nearly half a million helpline calls in Germany. Leveraging location-based routing of helpline calls and random day-to-day weather fluctuations, I find a negative effect of temperature extremes on mental health as revealed by an increase in the demand for telephone counseling services. On days with an average temperature above 25°C (77°F) and below 0°C (32°F), call volume is 3.4 and 5.1 percent higher, respectively, than on mid-temperature days. Mechanism analysis reveals pronounced adverse effects of cold temperatures on social and psychological well-being and of hot temperatures on psychological well-being and violence. More broadly, the findings of this work contribute to our understanding of how changing climatic conditions will affect population mental health and associated social costs in the near future.

Eiji Yamamura, Youki Kosaka, Yoshiro Tsutsui et al.

Vaccination has been promoted to mitigate the spread of the coronavirus disease 2019 (COVID-19). Vaccination is expected to reduce the probability of and alleviate the seriousness of COVID-19 infection. Accordingly, this might significantly change an individuals subjective well-being and mental health. However, it is unknown how vaccinated people perceive the effectiveness of COVID-19 and how their subjective well-being and mental health change after vaccination. We thus observed the same individuals on a monthly basis from March 2020 to September 2021 in all parts of Japan. Then, large sample panel data (N=54,007) were independently constructed. Using the data, we compared the individuals perceptions of COVID-19, subjective well-being, and mental health before and after vaccination. Furthermore, we compared the effect of vaccination on the perceptions of COVID-19 and mental health for females and males. We used the fixed-effects model to control for individual time-invariant characteristics. The major findings were as follows: First, the vaccinated people perceived the probability of getting infected and the seriousness of COVID-19 to be lower than before vaccination. This was observed not only when we used the whole sample, but also when we used sub-samples. Second, using the whole sample, subjective well-being and mental health improved. The same results were also observed using the sub-sample of females, whereas the improvements were not observed using a sub-sample of males.

Young-Seok Kweon, Gi-Hwan Shin, Heon-Gyu Kwak

Sleep is an essential behavior to prevent the decrement of cognitive, motor, and emotional performance and various diseases. However, it is not easy to fall asleep when people want to sleep. There are various sleep-disturbing factors such as the COVID-19 situation, noise from outside, and light during the night. We aim to develop a personalized sleep induction system based on mental states using electroencephalogram and auditory stimulation. Our system analyzes users' mental states using an electroencephalogram and results of the Pittsburgh sleep quality index and Brunel mood scale. According to mental states, the system plays sleep induction sound among five auditory stimulation: white noise, repetitive beep sounds, rainy sound, binaural beat, and sham sound. Finally, the sleep-inducing system classified the sleep stage of participants with 94.7 percent and stopped auditory stimulation if participants showed non-rapid eye movement sleep. Our system makes 18 participants fall asleep among 20 participants.