Understanding how individuals navigate mental health challenges over time is critical yet methodologically challenging. Traditional approaches analyze community-level snapshots, failing to capture dynamic individual recovery trajectories. We introduce a novel framework applying Topological Data Analysis (TDA) specifically persistent homology to model users' longitudinal posting histories as trajectories in semantic embedding space. Our approach reveals topological signatures of trajectory patterns: loops indicate cycling back to similar states (stagnation), while flares suggest exploring new coping strategies (growth). We propose Semantic Recovery Velocity (SRV), a novel metric quantifying the rate users move away from initial distress-focused posts in embedding space. Analyzing 15,847 r/depression trajectories and validating against multiple proxies, we demonstrate topological features predict self-reported improvement with 78.3% accuracy, outperforming sentiment baselines. This work contributes: (1) a TDA methodology for HCI mental health research, (2) interpretable topological signatures, and (3) design implications for adaptive mental health platforms with ethical guardrails.
Kate Bradley, Jo-anne Hughson, Irene Blackberry
et al.
Optimising brain health for older Aboriginal and Torres Strait Islander peoples is important given the high rates of cognitive impairment and dementia (CI/D) in this population. To achieve this, effective models of care for the primary care setting are needed. This paper reports on the process evaluation of a stepped-wedge cluster randomised controlled trial conducted with 12 Aboriginal Community Controlled Health Services (ACCHSs) across four states of Australia. The study implemented a culturally responsive, co-designed best-practice model of CI/D care for Aboriginal and Torres Strait Islander peoples. Utilising the integrated-Promoting Action on Research Implementation in Health Services (i-PARIHS) Framework, the process evaluation aimed to identify the components of a “successful implementation” for this type of intervention. Qualitative and quantitative data collected included interviews, workshop evaluation forms, implementation checklists, and researcher observational notes. Fidelity to the intervention (scored as low, medium or high) was medium overall. Dose delivered across ACCHSs and intervention activities varied markedly. The project's reach was high and ACCHS staff demonstrated high engagement. Major themes derived from the qualitative data were: 1. ‘Aboriginal health and diverse environmental ecosystems’; 2. ‘Reciprocal relationships built on collaboration and cultural responsiveness’; 3. ‘Community knowledges and understandings of memory and thinking problems’. Despite encountering several challenges, the intervention improved management of dementia, and had high uptake and acceptability among ACCHS staff. Identified factors affecting the intervention, notably related to context, will inform future initiatives to improve dementia care in primary care settings.
Giorgia Buracchio, Ariele Callegari, Massimo Donini
et al.
The paper presents an experiment on the effects of adaptive emotional alignment between agents, considered a prerequisite for empathic communication, in Human-Robot Interaction (HRI). Using the NAO robot, we investigate the impact of an emotionally aligned, empathic, dialogue on these aspects: (i) the robot's persuasive effectiveness, (ii) the user's communication style, and (iii) the attribution of mental states and empathy to the robot. In an experiment with 42 participants, two conditions were compared: one with neutral communication and another where the robot provided responses adapted to the emotions expressed by the users. The results show that emotional alignment does not influence users' communication styles or have a persuasive effect. However, it significantly influences attribution of mental states to the robot and its perceived empathy
Background: Adolescence is a critical period of brain maturation and heightened vulnerability to cognitive and mental health disorders. Sleep plays a vital role in neurodevelopment, yet the mechanisms linking insufficient sleep to adverse brain and behavioral outcomes remain unclear. The glymphatic system (GS), a brain-wide clearance pathway, may provide a key mechanistic link. Methods: Participants from the Adolescent Brain Cognitive Development (ABCD) Study (n =6,800; age ~ 11 years) were categorized into sleep-sufficient (>=9 h/night) and sleep-insufficient (<9 h/night) groups. Linear models tested associations among sleep, PVS burden, brain volumes, and behavioral outcomes. Mediation analyses evaluated whether PVS burden explained sleep-related effects. Results: Adolescents with insufficient sleep exhibited significantly greater PVS burden, reduced cortical, subcortical, and white matter volumes, poorer cognitive performance across multiple domains (largest effect in crystallized intelligence), and elevated psychopathology (largest effect in general problems). Sleep duration and quality were strongly associated with PVS burden. Mediation analyses revealed that PVS burden partially mediated sleep effects on cognition and mental health, with indirect proportions up to 10.9%. Sequential models suggested a pathway from sleep -> PVS -> brain volume -> behavior as the most plausible route. Conclusions: Insufficient sleep during adolescence is linked to glymphatic dysfunction, reflected by increased PVS burden, which partially accounts for adverse effects on brain structure, cognition, and mental health. These findings highlight the GS as a potential mechanistic pathway and imaging biomarker, underscoring the importance of promoting adequate sleep to support neurodevelopment and mental health.
Dong Whi Yoo, Jiayue Melissa Shi, Violeta J. Rodriguez
et al.
Recent advancements in LLMs enable chatbots to interact with individuals on a range of queries, including sensitive mental health contexts. Despite uncertainties about their effectiveness and reliability, the development of LLMs in these areas is growing, potentially leading to harms. To better identify and mitigate these harms, it is critical to understand how the values of people with lived experiences relate to the harms. In this study, we developed a technology probe, a GPT-4o based chatbot called Zenny, enabling participants to engage with depression self-management scenarios informed by previous research. We used Zenny to interview 17 individuals with lived experiences of depression. Our thematic analysis revealed key values: informational support, emotional support, personalization, privacy, and crisis management. This work explores the relationship between lived experience values, potential harms, and design recommendations for mental health AI chatbots, aiming to enhance self-management support while minimizing risks.
Vasudha Varadarajan, Hui Xu, Rebecca Astrid Boehme
et al.
Recent advances in large language models (LLMs) offer new opportunities for scalable, interactive mental health assessment, but excessive querying by LLMs burdens users and is inefficient for real-world screening across transdiagnostic symptom profiles. We introduce MAQuA, an adaptive question-asking framework for simultaneous, multidimensional mental health screening. Combining multi-outcome modeling on language responses with item response theory (IRT) and factor analysis, MAQuA selects the questions with most informative responses across multiple dimensions at each turn to optimize diagnostic information, improving accuracy and potentially reducing response burden. Empirical results on a novel dataset reveal that MAQuA reduces the number of assessment questions required for score stabilization by 50-87% compared to random ordering (e.g., achieving stable depression scores with 71% fewer questions and eating disorder scores with 85% fewer questions). MAQuA demonstrates robust performance across both internalizing (depression, anxiety) and externalizing (substance use, eating disorder) domains, with early stopping strategies further reducing patient time and burden. These findings position MAQuA as a powerful and efficient tool for scalable, nuanced, and interactive mental health screening, advancing the integration of LLM-based agents into real-world clinical workflows.
The transition to autonomous material systems necessitates adaptive control methodologies to maximize structural longevity. This study frames the self-healing process as a Reinforcement Learning (RL) problem within a Markov Decision Process (MDP), enabling agents to autonomously derive optimal policies that efficiently balance structural integrity maintenance against finite resource consumption. A comparative evaluation of discrete-action (Q-learning, DQN) and continuous-action (TD3) agents in a stochastic simulation environment revealed that RL controllers significantly outperform heuristic baselines, achieving near-complete material recovery. Crucially, the TD3 agent utilizing continuous dosage control demonstrated superior convergence speed and stability, underscoring the necessity of fine-grained, proportional actuation in dynamic self-healing applications.
Parental verbal aggression has long been assumed to be detrimental in terms of parent-child relationships. However, little is known about how it affects the relationship with psychological well-being, the more specific thought on maternal and paternal verbal aggression, and more acknowledgment regarding the implication of aggressive words as injurious to children. The critical goal of the study was to investigate whether participants reporting experiences on their maternal and paternal verbal aggression impact their psychological well-being. Data for the analysis were from the college students of PHINMA- University of Pangasinan (n = 219). A purposive sampling method was employed to select participants. Results showed that high psychological well-being is not significant, with both paternal and maternal verbal aggression jointly affecting it. However, maternal verbal aggression is independently associated with lower psychological well-being.
Electroencephalography (EEG) is widely researched for neural decoding in Brain Computer Interfaces (BCIs) as it is non-invasive, portable, and economical. However, EEG signals suffer from inter- and intra-subject variability, leading to poor performance. Recent technological advancements have led to deep learning (DL) models that have achieved high performance in various fields. However, such large models are compute- and resource-intensive and are a bottleneck for real-time neural decoding. Data distribution shift can be handled with the help of domain adaptation techniques of transfer learning (fine-tuning) and adversarial training that requires model parameter updates according to the target domain. One such recent technique is Parameter-efficient fine-tuning (PEFT), which requires only a small fraction of the total trainable parameters compared to fine-tuning the whole model. Therefore, we explored PEFT methods for adapting EEG-based mental imagery tasks. We considered two mental imagery tasks: speech imagery and motor imagery, as both of these tasks are instrumental in post-stroke neuro-rehabilitation. We proposed a novel ensemble of weight-decomposed low-rank adaptation methods, EDoRA, for parameter-efficient mental imagery task adaptation through EEG signal classification. The performance of the proposed PEFT method is validated on two publicly available datasets, one speech imagery, and the other motor imagery dataset. In extensive experiments and analysis, the proposed method has performed better than full fine-tune and state-of-the-art PEFT methods for mental imagery EEG classification.
Pedro Guillermo Feijóo-García, Chase Wrenn, Alexandre Gomes de Siqueira
et al.
Virtual humans (i.e., embodied conversational agents) have the potential to support college students' mental health, particularly in Science, Technology, Engineering, and Mathematics (STEM) fields where students are at a heightened risk of mental disorders such as anxiety and depression. A comprehensive understanding of students, considering their cultural characteristics, experiences, and expectations, is crucial for creating timely and effective virtual human interventions. To this end, we conducted a user study with 481 computer science students from a major university in North America, exploring how they co-designed virtual humans to support mental health conversations for students similar to them. Our findings suggest that computer science students who engage in co-design processes of virtual humans tend to create agents that closely resemble them demographically--agent-designer demographic similarity. Key factors influencing virtual human design included age, gender, ethnicity, and the matching between appearance and voice. We also observed that the demographic characteristics of virtual human designers, especially ethnicity and gender, tend to be associated with those of the virtual humans they designed. Finally, we provide insights concerning the impact of user-designer demographic similarity in virtual humans' effectiveness in promoting mental health conversations when designers' characteristics are shared explicitly or implicitly. Understanding how virtual humans' characteristics serve users' experiences in mental wellness conversations and the similarity-attraction effects between agents, users, and designers may help tailor virtual humans' design to enhance their acceptance and increase their counseling effectiveness.
Mental health is an increasing concern around the world, but there is a substantial gap in terms of access to quality mental healthcare between Western and non-Western countries. To help close this gap and improve the delivery of mental health and psychosocial support services (MHPSS), the UN’s 2016 Grand Bargain declared a new approach of prioritising the localisation of these services. This paper examines the effects of the Grand Bargain on the localisation of mental health and psychosocial support services in non-Western countries, as a means to decolonise mental health. An outcome evaluation to measure the amount of funding received by local and national agencies that provide MHPSS services in less economically developed countries was carried out. All data were gathered from the UN Financing Track System (FTS), and looked at financial contributions over time in six humanitarian sectors: health; water, sanitation and hygiene (WASH); gender-based violence; nutrition; protection and shelter. The results show that only 3% of international donors’ MHPSS-related humanitarian funding is received by local and national agencies between 2017 and 2021. Most of the localised MHPSS-related funding is driven by country-based pooled funds, with Middle Eastern countries as the primary beneficiaries, and localised MHPSS funding predominantly went to the health, WASH and protection sectors. This study found that limited localisation of MHPSS services in less economically developed countries, and a limited focus on community capacity building through associated humanitarian sectors. Based on this study, it is recommended that humanitarians should advocate for increased localisation and culturally competent practices in the MHPSS space.
Samuel Adesina Okueso, Victor Olusegun Adefarasin, Ademola Ridwan Adekola
Background: Insects’ stings and bites are common phenomenon among school children in rural areas primarily due to closeness to bushes. Knowledge of teachers on allergies and anaphylaxis due to insect bites and stings is important to promote healthy school living; hence, this study investigated the knowledge of primary school teachers on stinging insects and allergic reactions.
Methods: This was a descriptive survey research with 120 participants. Knowledge of insect stings prevention questionnaire (KISPQ r = 8.27) and knowledge of insect stings first aid treatment questionnaire (KISFATQ, r = 7.68) were the main instruments used for the study. Mean and standard deviation were used to answer the four research questions while linear regression analysis was used to test the two postulated hypotheses at 0.05 alpha levels.
Results: Findings revealed that teachers were not significantly knowledgeable about insects’ stings regarding allergies and anaphylactic reactions. This was because only about 50% of the teachers could recognize the insects causing allergies and anaphylactic reactions. The result of F-value of 81.760 whose probability was close to zero percent showed that, statistically, the teachers' knowledge of the prevention and first aid treatment of allergy due to insect bite had a significant influence on prevention of anaphylaxis. The result of F-value of 110.618 whose probability was close to zero percent indicated that teachers' knowledge of aetiology and prevention of allergy regarding insect bite had a significant influence on first aid skills.
Conclusion: There is lack of knowledge in these areas at primary school level and that allergies and anaphylactic prevention should be added to health education curriculum content in school to improve knowledge of both the teachers and the pupils. Therefore, this study focuses on training teachers regarding prevention of insect stings and improving first aid treatment skills for stings and bites.
Communities. Classes. Races, Social pathology. Social and public welfare. Criminology
Markelle Kelly, Aakriti Kumar, Padhraic Smyth
et al.
Improving our understanding of how humans perceive AI teammates is an important foundation for our general understanding of human-AI teams. Extending relevant work from cognitive science, we propose a framework based on item response theory for modeling these perceptions. We apply this framework to real-world experiments, in which each participant works alongside another person or an AI agent in a question-answering setting, repeatedly assessing their teammate's performance. Using this experimental data, we demonstrate the use of our framework for testing research questions about people's perceptions of both AI agents and other people. We contrast mental models of AI teammates with those of human teammates as we characterize the dimensionality of these mental models, their development over time, and the influence of the participants' own self-perception. Our results indicate that people expect AI agents' performance to be significantly better on average than the performance of other humans, with less variation across different types of problems. We conclude with a discussion of the implications of these findings for human-AI interaction.
Resilience is a concept relating to a system’s ability to ‘bounce back’ from disturbance and can be conceptualised as the boundaries (or thresholds) around an equilibrium state. This paper traces the connection between ecological change and mental health, arguing that both are seeing increasing dysregulation. Climate change, as is occurring under ‘business-as-usual conditions’, is likely to breach the resilience of the Earth System (ES), tipping the world into ‘Hothouse Earth’. Equally, mental health, envisaged also using a resilience (or ‘Window of Tolerance’) framework, is affected by climate change – tipping people from a steady to dysregulated state of emotionality. These states are borne from both direct climate crisis and indirect ‘eco-anxieties’ emanating from the mere fact of impending climate change. Societally, our level of current dysregulation and dissociation can be seen from the way we treat the indigenous and natural world. Coping mechanisms like addictions or denial can be easy to seek out to soothe upset feelings, but this paper argues that to truly re-regulate, true emotional expression of grief followed by mindful equipoise would be more desirable, both for healing ourselves and for healing the planet.
Despite the wide applications of neural networks, there have been increasing concerns about their vulnerability issue. While numerous attack and defense techniques have been developed, this work investigates the robustness issue from a new angle: can we design a self-healing neural network that can automatically detect and fix the vulnerability issue by itself? A typical self-healing mechanism is the immune system of a human body. This biology-inspired idea has been used in many engineering designs but is rarely investigated in deep learning. This paper considers the post-training self-healing of a neural network, and proposes a closed-loop control formulation to automatically detect and fix the errors caused by various attacks or perturbations. We provide a margin-based analysis to explain how this formulation can improve the robustness of a classifier. To speed up the inference of the proposed self-healing network, we solve the control problem via improving the Pontryagin Maximum Principle-based solver. Lastly, we present an error estimation of the proposed framework for neural networks with nonlinear activation functions. We validate the performance on several network architectures against various perturbations. Since the self-healing method does not need a-priori information about data perturbations/attacks, it can handle a broad class of unforeseen perturbations.
Prathamesh Muzumdar, Ganga Prasad Basyal, Piyush Vyas
Student's mental health problems have been explored previously in higher education literature in various contexts including empirical work involving quantitative and qualitative methods. Nevertheless, comparatively few research could be found, aiming for computational methods that learn information directly from data without relying on set parameters for a predetermined equation as an analytical method. This study aims to investigate the performance of Machine learning (ML) models used in higher education. ML models considered are Naive Bayes, Support Vector Machine, K-Nearest Neighbor, Logistic regression, Stochastic Gradient Descent, Decision Tree, Random Forest, XGBoost (Extreme Gradient Boosting Decision Tree), and NGBoost (Natural) algorithm. Considering the factors of mental health illness among students, we follow three phases of data processing: segmentation, feature extraction, and classification. We evaluate these ML models against classification performance metrics such as accuracy, precision, recall, F1 score, and predicted run time. The empirical analysis includes two contributions: 1. It examines the performance of various ML models on a survey-based educational dataset, inferring a significant classification performance by a tree-based XGBoost algorithm; 2. It explores the feature importance [variables] from the datasets to infer the significant importance of social support, learning environment, and childhood adversities on a student's mental health illness.
When people think of everyday things like an egg, they typically have a mental image associated with it. This allows them to correctly judge, for example, that "the yolk surrounds the shell" is a false statement. Do language models similarly have a coherent picture of such everyday things? To investigate this, we propose a benchmark dataset consisting of 100 everyday things, their parts, and the relationships between these parts, expressed as 11,720 "X relation Y?" true/false questions. Using these questions as probes, we observe that state-of-the-art pre-trained language models (LMs) like GPT-3 and Macaw have fragments of knowledge about these everyday things, but do not have fully coherent "parts mental models" (54-59% accurate, 19-43% conditional constraint violation). We propose an extension where we add a constraint satisfaction layer on top of the LM's raw predictions to apply commonsense constraints. As well as removing inconsistencies, we find that this also significantly improves accuracy (by 16-20%), suggesting how the incoherence of the LM's pictures of everyday things can be significantly reduced.