Burn injuries are under-appreciated injuries that are associated with substantial morbidity and mortality. Burn injuries, particularly severe burns, are accompanied by an immune and inflammatory response, metabolic changes and distributive shock that can be challenging to manage and can lead to multiple organ failure. Of great importance is that the injury affects not only the physical health, but also the mental health and quality of life of the patient. Accordingly, patients with burn injury cannot be considered recovered when the wounds have healed; instead, burn injury leads to long-term profound alterations that must be addressed to optimize quality of life. Burn care providers are, therefore, faced with a plethora of challenges including acute and critical care management, long-term care and rehabilitation. The aim of this Primer is not only to give an overview and update about burn care, but also to raise awareness of the ongoing challenges and stigmata associated with burn injuries. Burn injuries, particularly severe burns, are accompanied by a metabolic, immune and inflammatory response that can be challenging to manage, often leading to multiple organ failure or even death. This Primer discusses aspects of burn injury, from prevention to care of patients from both an acute and a long-term perspective.
Abeer Badawi, Md Tahmid Rahman Laskar, Elahe Rahimi
et al.
The escalating global mental health crisis, marked by persistent treatment gaps, availability, and a shortage of qualified therapists, positions Large Language Models (LLMs) as a promising avenue for scalable support. While LLMs offer potential for accessible emotional assistance, their reliability, therapeutic relevance, and alignment with human standards remain challenging to address. This paper introduces a human-grounded evaluation methodology designed to assess LLM generated responses in therapeutic dialogue. Our approach involved curating a dataset of 500 mental health conversations from datasets with real-world scenario questions and evaluating the responses generated by nine diverse LLMs, including closed source and open source models. More specifically, these responses were evaluated by two psychiatric trained experts, who independently rated each on a 5 point Likert scale across a comprehensive 6 attribute rubric. This rubric captures Cognitive Support and Affective Resonance, providing a multidimensional perspective on therapeutic quality. Our analysis reveals that LLMs provide strong cognitive reliability by producing safe, coherent, and clinically appropriate information, but they demonstrate unstable affective alignment. Although closed source models (e.g., GPT-4o) offer balanced therapeutic responses, open source models show greater variability and emotional flatness. We reveal a persistent cognitive-affective gap and highlight the need for failure aware, clinically grounded evaluation frameworks that prioritize relational sensitivity alongside informational accuracy in mental health oriented LLMs. We advocate for balanced evaluation protocols with human in the loop that center on therapeutic sensitivity and provide a framework to guide the responsible design and clinical oversight of mental health oriented conversational AI.
Mehrnoosh Sadat Shirvani, Jackie Liu, Thomas Chao
et al.
Mental health conversational agents have the potential to deliver valuable therapeutic impact, but low user engagement remains a critical barrier hindering their efficacy. Existing therapeutic approaches have leveraged clients' internal dialogues (e.g., journaling, talking to an empty chair) to enhance engagement through accountable, self-sourced support. Inspired by these, we designed novel AI-driven self-clone chatbots that replicate users' support strategies and conversational patterns to improve therapeutic engagement through externalized meaningful self-conversation. Validated through a semi-controlled experiment (N=180), significantly higher emotional and cognitive engagement was demonstrated with self-clone chatbots than a chatbot with a generic counselor persona. Our findings highlight self-clone believability as a mediator and emphasize the balance required in maintaining convincing self-representation while creating positive interactions. This study contributes to AI-based mental health interventions by introducing and evaluating self-clones as a promising approach to increasing user engagement, while exploring implications for their application in mental health care.
The impact of culture on how people express distress in online support communities is increasingly a topic of interest within Computer Supported Cooperative Work (CSCW) and Human-Computer Interaction (HCI). In the United States, distinct cultures have emerged from each of the two dominant political parties, forming a primary lens by which people navigate online and offline worlds. We examine whether partisan culture may play a role in how U.S. Republican and Democrat users of online mental health support communities express distress. We present a large-scale observational study of 2,184,356 posts from 8,916 statistically matched Republican, Democrat, and unaffiliated online support community members. We utilize methods from causal inference to statistically match partisan users along covariates that correspond with demographic attributes and platform use, in order to create comparable cohorts for analysis. We then leverage methods from natural language processing to understand how partisan expressions of distress compare between these sets of closely matched opposing partisans, and between closely matched partisans and typical support community members. Our data spans January 2013 to December 2022, a period of both rising political polarization and mental health concerns. We find that partisan culture does play into expressions of distress, underscoring the importance of considering partisan cultural differences in the design of online support community platforms.
Conceptual design is a cognitively complex task, especially in the engineering design of products having relative motion between components. Designers prefer sketching as a medium for conceptual design and use gestures and annotations to represent such relative motion. Literature suggests that static representations of motion in sketches may not achieve the intended functionality when realised, because it primarily depends on the designers' mental capabilities for motion simulation. Thus, it is important to understand the cognitive phenomena when designers are exploring concepts of articulated products. The current work is an attempt to understand design neurocognition by categorising the tasks and measuring the mental effort involved in these tasks using EEG. The analysis is intended to validate design intervention tools to support the conceptual design involving motion exploration. A novel EEG-based metric, inter-Band Relative Power Difference (inter-BRPD), is introduced to quantify mental effort. A design experiment is conducted with 32 participants, where they have to perform one control task and 2 focus tasks corresponding to the motion exploration task (MET) and the concept generation task (CGT), respectively. EEG data is recorded during the 3 tasks, cleaned, processed and analysed using the MNE library in Python. It is observed from the results that inter-BRPD captures the essence of mental effort with half the number of conventionally used parameters. The reliability and efficacy of the inter-BRPD metric are also statistically validated against literature-based cognitive metrics. With these new insights, the study opens up possibilities for creating support for conceptual design and its evaluation.
Gourav Siddhad, Anurag Singh, Rajkumar Saini
et al.
Driver drowsiness is a leading cause of traffic accidents, necessitating real-time, reliable detection systems to ensure road safety. This study proposes a Modified TSception architecture for robust assessment of driver fatigue and mental workload using Electroencephalography (EEG). The model introduces a five-layer hierarchical temporal refinement strategy to capture multi-scale brain dynamics, surpassing the original TSception's three-layer approach. Key innovations include the use of Adaptive Average Pooling (ADP) for structural flexibility across varying EEG dimensions and a two-stage fusion mechanism to optimize spatiotemporal feature integration for improved stability. Evaluated on the SEED-VIG dataset, the Modified TSception achieves 83.46% accuracy, comparable to the original model (83.15%), but with a significantly reduced confidence interval (0.24 vs. 0.36), indicating better performance stability. The architecture's generalizability was further validated on the STEW mental workload dataset, achieving state-of-the-art accuracies of 95.93% and 95.35% for 2-class and 3-class classification, respectively. These results show that the proposed modifications improve consistency and cross-task generalizability, making the model a reliable framework for EEG-based safety monitoring.
Chronic wounds, including diabetic foot ulcers which affect up to one-third of people with diabetes, impose a substantial clinical and economic burden, with U.S. healthcare costs exceeding 25 billion dollars annually. Current wound assessment remains predominantly subjective, leading to inconsistent classification and delayed interventions. We present WoundNet-Ensemble, an Internet of Medical Things system leveraging a novel ensemble of three complementary deep learning architectures: ResNet-50, the self-supervised Vision Transformer DINOv2, and Swin Transformer, for automated classification of six clinically distinct wound types. Our system achieves 99.90 percent ensemble accuracy on a comprehensive dataset of 5,175 wound images spanning diabetic foot ulcers, pressure ulcers, venous ulcers, thermal burns, pilonidal sinus wounds, and fungating malignant tumors. The weighted fusion strategy demonstrates a 3.7 percent improvement over previous state-of-the-art methods. Furthermore, we implement a longitudinal wound healing tracker that computes healing rates, severity scores, and generates clinical alerts. This work demonstrates a robust, accurate, and clinically deployable tool for modernizing wound care through artificial intelligence, addressing critical needs in telemedicine and remote patient monitoring. The implementation and trained models will be made publicly available to support reproducibility.
Introduction: In this case, an endodontic microsurgery of the left mandibular first premolar adjacent to the mental foramen was successfully performed with the assistance of dynamic navigation system, which enhanced apical positioning accuracy, shortened the operation time, and avoided the occurrence of nerve injury. Case description: The patient presented with chronic apical periodontitis of the left mandibular first premolar. Due to calcified and non-negotiable root canals, conventional endodontic treatment was unfeasible. Radiographic examination revealed the buccal bone plate in the apical lesion area was intact and only 3 mm away from the mental foramen. After flap reflection, we used the dynamic navigation system to precisely locate the apex, perform osteotomy, and resect 3 mm of the root apex. No postoperative neurosensory disturbances were observed. A 34-months follow-up confirmed complete healing of the periapical lesion. Discussion: In this case, the intact buccal bone overlying the periapical lesion posed challenges for root apex localization, while the lesion's close proximity to the mental foramen increased the risk of nerve injury. The application of dynamic navigation technology significantly improved the surgical precision, reduced operative time, and effectively protect the mental nerve, thereby preventing postoperative neurological complications—particularly beneficial for less-experienced clinicians. Conclusion/clinical significance: The application of dynamic navigation system in endodontic microsurgery ensured superior positioning accuracy and shorter operative time, especially for surgeries involving critical anatomical structures. This technology effectively prevented iatrogenic nerve injuries, enhancing surgical safety.
Catherine E. Draper, Claire Hart, Nosibusiso Tshetu
et al.
This qualitative study explored how mature defence mechanisms support positive deviance in health and wellbeing among young women facing adversity in Soweto, South Africa. Drawing from the Bukhali randomized controlled trial in Soweto, which targets improved health trajectories for young women, this study focused on a group of participants who exhibited positive deviance in the trial by being employed or studying, engaging actively in the trial, and showing favourable physical and mental health indicators despite living in a context marked by poverty, inequality, and trauma. Eight in-depth interviews were conducted with participants who met selection criteria, and data were analysed using a codebook thematic approach, incorporating a psychoanalytic framework of defensive functioning. Participants demonstrated frequent use of high adaptive defences, such as anticipation, self-observation, sublimation, and self-assertion, which enabled emotional regulation, agency, and healthy coping. Self-isolation and low affiliation, often seen as withdrawal, were reframed as protective strategies when balanced with meaningful social connections. These findings offer a psychologically rich understanding of how young women in challenging environments navigate complex social landscapes. By integrating positive deviance with defensive functioning, the study extends psychoanalytic theory to marginalized contexts, revealing how mature defence mechanisms contribute to resilience. The insights have implications for designing strength-based mental health interventions tailored to the realities and psychological capacities of marginalized populations.
Ion conductive hydrogels (ICHs) have attracted great interest in the application of ionic skin because of their superior characteristics. However, it remains a challenge for ICHs to achieve balanced properties of high strength, large fracture strain, self-healing and freezing tolerance. In this study, a strong, stretchable, self-healing and antifreezing ICH was demonstrated by rationally designing a multiphysically cross-linked network structure consisting of the hydrophobic association, metal-ion coordination and chain entanglement among poly(acrylic acid) (PAA) polymer chains. The deliberately designed Brij S 100 acrylate (Brij-100A) micelle cross-linker can effectively dissipate energy and endow hydrogels with desirable stretchability. The self-healing ability of hydrogels originates from the reversible hydrophobic association in micelles and Fe3+-COO- coordination. After the addition of NaCl, the chain-entangled physical network caused by the salting-out effect can both enhance mechanical strength and promote electron transport. With the synergy of hydrophobic association, mental-ligand coordination and chain entanglement, the PAA/Brij-100A/Fe3+/NaCl (PAA/BA/Fe3+/NaCl) hydrogels exhibited a high tensile strain of 1140%, a tensile strength of 0.93 MPa and a toughness of 3.48 MJ m-3. Besides, the PAA/BA/Fe3+/NaCl hydrogels exhibited a high conductivity of 0.43 S m-1 and good freezing resistance. The ionic skin based on the PAA/BA/Fe3+/NaCl hydrogels showed high sensitivity (GF = 5.29), wide strain range (0-950%), fast response time (220 ms) and good stability. Also, the self-healing ability of the ionic skin can significantly prolong its service time, and the antifreezing property can broaden its applicable temperature. This study offers new insight into the design of multifunctional ionic skin for wearable electronics.
At present, China's rail transportation industry is in a rapid development stage, and the work pressure of rail transportation workers is increasing, and mental health problems are becoming more and more prominent. This study adopts the questionnaire research method to investigate the main factors affecting the mental health of the employees of five rail transit enterprises in Guangdong Province, China and their attitudes towards art healing, and constructs a factor model to conduct a comprehensive analysis by using the SPSS software, which reveals that the work intensity, work pressure and life pressure are the main factors affecting the mental health of the rail transit employees, and that most of the rail transit employees are willing to accept the art healing and believe that art healing is a positive and effective way to solve the mental health problems. Most of the rail transit workers are willing to accept art healing and believe that art healing is a positive and effective way to solve mental health problems. When designing art healing programs, it is necessary to give full consideration to the high work intensity of rail transit workers, and in the case of fewer art healing programs in the existing rail transit industry, it is necessary to actively explore leisure and relaxing art healing programs for the workers in the rail transit industry in order to alleviate and heal the psychological problems of rail transit workers, and to assist in the healthy development of the rail transit industry. The healthy development of rail transportation industry.
Patrick Song, Jayshree Sarathy, Michael Shoemate
et al.
Differential privacy (DP) is a promising framework for privacy-preserving data science, but recent studies have exposed challenges in bringing this theoretical framework for privacy into practice. These tensions are particularly salient in the context of open-source software libraries for DP data analysis, which are emerging tools to help data stewards and analysts build privacy-preserving data pipelines for their applications. While there has been significant investment into such libraries, we need further inquiry into the role of these libraries in promoting understanding of and trust in DP, and in turn, the ways in which design of these open-source libraries can shed light on the challenges of creating trustworthy data infrastructures in practice. In this study, we use qualitative methods and mental models approaches to analyze the differences between conceptual models used to design open-source DP libraries and mental models of DP held by users. Through a two-stage study design involving formative interviews with 5 developers of open-source DP libraries and user studies with 17 data analysts, we find that DP libraries often struggle to bridge the gaps between developer and user mental models. In particular, we highlight the tension DP libraries face in maintaining rigorous DP implementations and facilitating user interaction. We conclude by offering practical recommendations for further development of DP libraries.
Jill Locke, Cathy M. Corbin, Vaughan K. Collins
et al.
Background Few “intervention agnostic” strategies have been developed that can be applied to the broad array of evidence-based practices (EBPs) in schools. This paper describes two studies that reflect the initial iterative redesign phases of an effective leadership-focused implementation strategy—Leadership and Organizational Change for Implementation (LOCI)—to ensure its acceptability, feasibility, contextual appropriateness, and usability when used in elementary schools. Our redesigned strategy—Helping Educational Leaders Mobilize Evidence (HELM)—is designed to improve principals’ use of strategic implementation leadership to support the adoption and high-fidelity delivery of a universal EBP to improve student outcomes. Method In Study 1, focus groups were conducted ( n = 6) with 54 district administrators, principals, and teachers. Stakeholders provided input on the appropriateness of original LOCI components to maximize relevance and utility in schools. Transcripts were coded using conventional content analysis. Key themes referencing low appropriateness were summarized to inform LOCI adaptations. We then held a National Expert Summit (Study 2) with 15 research and practice experts. Participants provided feedback via a nominal group process (NGP; n = 6 groups) and hackathon ( n = 4 groups). The research team rated each NGP suggestion for how actionable, impactful/effective, and feasible it was. We also coded hackathon notes for novel ideas or alignment with LOCI components. Results Study 1 suggestions included modifications to LOCI content and delivery. Study 2's NGP results revealed most recommendations to be actionable, impactful/effective, and feasible. Hackathon results surfaced two novel ideas (distributed leadership teams and leaders’ knowledge to support educators EBP use) and several areas of alignment with LOCI components. Conclusion Use of these iterative methods informed the redesign of LOCI and the development of HELM. Because it was collaboratively constructed, HELM has the potential to be an effective implementation strategy to support the use of universal EBP in schools.
Developing specialized dialogue systems for mental health support requires multi-turn conversation data, which has recently garnered increasing attention. However, gathering and releasing large-scale, real-life multi-turn conversations that could facilitate advancements in mental health support presents challenges in data privacy protection and the time and cost involved in crowdsourcing. To address these challenges, we introduce SMILE, a single-turn to multi-turn inclusive language expansion technique that prompts ChatGPT to rewrite public single-turn dialogues into multi-turn ones. Our work begins by analyzing language transformation and validating the feasibility of our proposed method. We conduct a study on dialogue diversity, including lexical features, semantic features, and dialogue topics, demonstrating the effectiveness of our method. Further, we employ our method to generate a large-scale, lifelike, and diverse dialogue dataset named SMILECHAT, consisting of 55k dialogues. Finally, we utilize the collected corpus to develop a mental health chatbot, MeChat. To better assess the quality of SMILECHAT, we collect a small-scale real-life counseling dataset conducted by data anonymization. Both automatic and human evaluations demonstrate significant improvements in our dialogue system and confirm that SMILECHAT is high-quality. Code, data, and model are publicly available at https://github.com/qiuhuachuan/smile.
Virtual Mental Health Assistants (VMHAs) are seeing continual advancements to support the overburdened global healthcare system that gets 60 million primary care visits, and 6 million Emergency Room (ER) visits annually. These systems are built by clinical psychologists, psychiatrists, and Artificial Intelligence (AI) researchers for Cognitive Behavioral Therapy (CBT). At present, the role of VMHAs is to provide emotional support through information, focusing less on developing a reflective conversation with the patient. A more comprehensive, safe and explainable approach is required to build responsible VMHAs to ask follow-up questions or provide a well-informed response. This survey offers a systematic critical review of the existing conversational agents in mental health, followed by new insights into the improvements of VMHAs with contextual knowledge, datasets, and their emerging role in clinical decision support. We also provide new directions toward enriching the user experience of VMHAs with explainability, safety, and wholesome trustworthiness. Finally, we provide evaluation metrics and practical considerations for VMHAs beyond the current literature to build trust between VMHAs and patients in active communications.
Boris Budosan, Jorge Castro, Pavla Kortusova
et al.
The Czech government, the Czech Ministry of Health (MoH) and the Czech Ministry of Interior (MoI) have acknowledged mental health and psychosocial support (MHPSS) for refugees from Ukraine as an important component of the humanitarian response. Despite their support to refugees from Ukraine in providing them with the essential basic services such as accommodation, livelihood, health services and education (social determinants of mental health), MHPSS response is still facing some challenges. Main challenges are related to the healthcare delivery system and low mental health awareness among refugees from Ukraine. Shortage of mental health (MH) professionals from Ukraine with the license to practice in Czechia and overwhelmed national healthcare system makes it more difficult for refugees from Ukraine to get adequate and timely MHPSS. Language barriers, low demand for MHPSS among refugees from Ukraine, not fully functional intra and inter-sectoral MHPSS referral system, need for responsible staff care, monitoring, evaluation and reporting of results of MHPSS and introduction of international guidelines into the national MHPSS response are identified as important challenges. Recommendations and solutions to overcome these challenges and improve the outcomes of MHPSS response for the refugee population from Ukraine in Czechia draw from local, regional and global experiences.
R. Maria del Rio-Chanona, Alejandro Hermida-Carrillo, Melody Sepahpour-Fard
et al.
To study the causes of the 2021 Great Resignation, we use text analysis to investigate the changes in work- and quit-related posts between 2018 and 2021 on Reddit. We find that the Reddit discourse evolution resembles the dynamics of the U.S. quit and layoff rates. Furthermore, when the COVID-19 pandemic started, conversations related to working from home, switching jobs, work-related distress, and mental health increased. We distinguish between general work-related and specific quit-related discourse changes using a difference-in-differences method. Our main finding is that mental health and work-related distress topics disproportionally increased among quit-related posts since the onset of the pandemic, likely contributing to the Great Resignation. Along with better labor market conditions, some relief came beginning-to-mid-2021 when these concerns decreased. Our study validates the use of forums such as Reddit for studying emerging economic phenomena in real time, complementing traditional labor market surveys and administrative data.
Tessa Ubels, Sara Kinsbergen, Jochem Tolsma
et al.
Policymakers, practitioners and academics expect mental health and psychosocial support (MHPSS) interventions to have social outcomes. Surprisingly, the existing academic literature on the effectiveness of MHPSS has focused almost exclusively on clinical outcomes. The evidence base of MHPSS interventions is in that way limited. To feed the research agenda on MHPSS (i.e., MHPSS-SET2), this scoping review analyses the presence and understanding of social outcomes in the grey literature. Open-access documents were systematically searched from various online grey literature databases and websites of organisations. Documents which describe psychosocial programming in low- and middle-income countries for people affected by humanitarian emergencies were included. Data characteristics were extracted, such as the type of document, intervention and outcome. A textual analysis of social outcomes was conducted to categorise the descriptions of these outcomes.A total number of 95 grey literature documents were included in the review. It was found that in the vast majority of the reviewed documents, social outcomes are being described. However, social outcomes have been poorly conceptualised both theoretically and methodologically, meaning that most documents lack definitions of theoretical concepts and measurement instruments. Mechanisms relating interventions to social outcomes have remained implicit. These findings are interpreted in light of key developments in the field of MHPSS, in particular the introduction of the Inter-Agency Standing Committee (IASC) guidelines, and the review traces the underexposed position of social outcomes back to the clinical historical roots of the field.In conclusion, those who develop and evaluate interventions should focus more structural attention on social outcomes to fully understand the possible impact of psychosocial interventions. Further harmonisation between academic research and practice is necessary, by drawing from practice-based insights on social outcomes as found in the grey literature, and using methods and measurement instruments from social sciences in MHPSS research.