Search Results (910)

Education is crucial for addressing the global biodiversity crisis and encouraging behaviours that support sustainable resource use and biodiversity protection. Species identification skills are an important part of biodiversity education, but research shows that educational practitioners have limited species knowledge and preferences for certain biodiversity. This study compares UK practitioners’ knowledge, awareness, and perceptions regarding four biodiversity categories (invertebrates, mammals, birds, flowering plants). UK schoolteachers in primary education, secondary science, and geography (n = 192) completed an online survey, comprising an identification test, free listing exercise, Likert scale, and closed and open-text questions. Knowledge was poor overall but highest for birds and mammals, followed by plants and lastly invertebrates. Few respondents correctly identified all six plant species, and none correctly identified all six invertebrates. Identification knowledge was positively associated with age, nature connectedness, and type of university degree. Relative awareness was high for mammals, similar for trees, flowers and birds, and low for invertebrates and other vertebrate groups. Respondents perceived colourful flying species as attractive but species with stinging structures as unattractive. Approximately half the respondents thought it was important for teachers to possess identification skills and two thirds thought that children had poor identification skills. The potential impacts of low invertebrate knowledge and awareness on environmental education are discussed and solutions proposed for teacher training, support, and classroom interventions. Full article

Large Language Model (LLM)-driven social simulation offers a controllable approach for analyzing crisis responses, but existing work on online crises often emphasizes either user engagement prediction or opinion and eWOM evolution. This separation is insufficient for online firestorms, where crisis impact emerges from the coupling between what users express and how participation expands over time. In such events, responsibility attribution, KOL influence, electronic Word of Mouth (eWOM), and user engagement jointly shape collective reactions. To address this gap, we introduce the Crisis Response Interaction Simulation Pipeline (CRISP), a training-free LLM-driven multi-agent framework for online firestorm simulation. CRISP integrates an eWOM Perception module for responsibility attribution and attitude formation with an Engagement Mechanism for predicting participation under evolving KOL influence. Experiments on four heterogeneous Weibo online firestorms across beverage, automobile manufacturing, food service, and education domains show that CRISP reproduces major eWOM and engagement trajectories across different activity scales and interaction structures. Counterfactual interventions on emotional composition and responsibility attribution further produce directionally consistent responses, suggesting mechanism-level validity beyond trajectory fitting. These findings indicate that CRISP provides a framework for analyzing online firestorm evolution and evaluating crisis communication strategies in controllable simulation environments. Full article

In biodiversity-rich borderlands, some humanitarian settlements are rapidly expanding. This creates a profound conflict: refugees need a place to live, and ecosystems need protection. However, how settlement growth spatially affects the ecology surrounding protected areas remains understudied. This study takes as an example the city of Teknaf in Bangladesh, one of the world’s largest refugee gathering areas, to explore how settlement expansion changes the ecological structure and function of protected area boundaries, with a focus on two questions: Are there critical spatial thresholds? What is the role of climate feedback mechanisms? We build an analysis framework that integrates several types of data: multitemporal remote sensing images, land-use changes, ecological indicators (NDVI, LST, HQ), landscape pattern indices, gradient analysis, and 2036 simulations based on the business-as-usual scenario. Through this framework, we identify the ecological threshold at the junction of settlements and forests within the Teknaf Wildlife Sanctuary. The expansion of settlements has turned the landscape, which was originally dominated by vegetation, into fragmented hard patches. At the same time, the habitat is severely degraded, and heat stress intensifies. Notably, a critical transition zone emerges at approximately 300–500 m from the protected area boundary, where landscape fragmentation intensifies, habitat quality declines, and heat stress reaches its peak, highlighting a spatial hotspot of ecological vulnerability. If there are no intervention measures, future scenario simulations show that the continued expansion of settlements will only isolate protected areas and accelerate ecological degradation. On the basis of gradient analysis for spatial diagnosis, we propose a zoning management framework and regeneration landscape strategy with the direct goal of coordinating ecological protection and humanitarian needs in crisis-prone border areas. Full article

Background: Nursing faculty shortages, burnout, and high turnover represent an escalating workforce crisis in Saudi governmental colleges of nursing. The Job Demands–Resources (JD-R) model offers a theoretically grounded framework for examining how occupational demands are associated with reduced well-being and how resources moderate these effects. Objective: This study aimed to examine the direct associations between job demands and stress outcomes and the moderating roles of job and personal resources among nursing faculty in Saudi Arabia, accounting for gender and nationality as structural covariates. Methods: A quantitative cross-sectional survey was conducted with 268 nursing faculty members from five governmental colleges using a voluntary survey of all eligible faculty (response rate: 51.1%). Theory-driven hierarchical regression analyses examined direct and moderating effects within the health-impairment pathway of the JD-R model. Results: Job demands significantly predicted all three burnout dimensions, reduced mental well-being, and job dissatisfaction. Trait emotional intelligence moderated the demand–exhaustion (delta-R² = 0.031, p = 0.006) and demand–job satisfaction (delta-R² = 0.028, p = 0.009) relationships. Job resources moderated the demand–mental well-being (delta-R² = 0.024, p = 0.018) and demand–professional efficacy links (delta-R² = 0.021, p = 0.029). Conclusions: Job demands are the primary predictor of burnout and occupational stress. Gender and nationality were associated with systematic differences in stress outcomes, suggesting that interventions should be culturally responsive and account for structural inequities. Full article

Extreme opinion propagation in social information networks often appears as a low-frequency but high-impact process, in which abnormal activity becomes synchronized across structurally related users or communities during crisis periods. Conventional correlation-based methods mainly describe average co-movement and may therefore miss dependence patterns that emerge only in the tail regime. To address this issue, this paper proposes a community-structured extremal dependence framework for social opinion propagation risk analysis. A tail pairwise dependence matrix (TPDM) is used to construct a weighted extremal dependence network, on which node-level risk scoring, community detection, and community-level intervention analyses are performed. The proposed risk score integrates degree centrality, betweenness centrality, tail exposure, and community embedding strength, while the intervention component is formulated as a minimum cut problem on the induced community graph. The framework is evaluated on a controlled synthetic social discussion network with 100 nodes. The experiment is intended as a methodological proof of concept rather than as a real-platform empirical validation. The results show that the TPDM-based network produces a structured representation with two dominant coupled communities, several peripheral singleton nodes, concentrated high-risk nodes, and one principal source–target interface in the community graph. These findings indicate that extremal dependence can provide a useful representation of candidate risk-coupling structures under the synthetic setting. However, the inferred edges should not be interpreted as causal propagation paths, and the minimum cut result should be understood as a candidate intervention interface rather than as a guarantee of complete diffusion blockage. Future work should validate the framework on real social media traces, incorporate temporal causal information, and examine robustness under multi-channel diffusion and adaptive user behavior. Full article

Background: Congenital heart defects (CHD) are prevalent, affecting 1% of live births globally. Despite improved survival rates, adults with CHD face increased risks of psychological distress and neurocognitive deficits. The P-BAHn study (P-BAHn = “Psyche Bei Angeborenen Herzfehlern”, Psyche for congenital heart defects) evaluates the mental health status and psychosocial challenges of German children and adolescents with CHD, focusing on retrospectively assessed COVID-19-related burden and patient-/parent-rated experiences with psychological, psychotherapeutic, or psychiatric treatment (PST). Methods: A cross-sectional, online-based survey was conducted using the National Register for Congenital Heart Defects (NRCHD). The final dataset comprised 1567 respondent-level records from 1310 families, including 992 parent reports and 575 self-reports from children/adolescents aged 6 to <18 years. The survey assessed mental health, emotional well-being, psychosocial status, demographics, medical history, and psychological treatment. Data were analyzed descriptively using chi-square tests and t-tests for exploratory unadjusted group comparisons. In addition, exploratory multivariable logistic regression analyses were performed for selected key outcomes. Results: School-related stress was common in young CHD patients (45.3%) and was associated with older age and female sex (51.5% female vs. 35.6% male) in adjusted analyses. Overall, 17.0% of patients reported having a mental illness, most commonly anxiety (6.8%), eating disorders (5.6%), and depression (4.7%); neither sex nor CHD severity was significantly associated with self-reported mental illness in adjusted analyses. Less good/poor self-rated health was associated with older age and complex CHD in both patient and parent reports. Retrospectively assessed pandemic-related changes were perceived as quite or extremely stressful by 23.9% of patients. High COVID-19-related burden was associated with female sex, whereas CHD severity was not significant after adjustment. Among patients with previous or current PST, patient- and parent-rated treatment benefit varied by patient sex and CHD complexity. Previous/current PST was reported by 25.9% of patients and 23.8% of parents and was associated with older age in both respondent groups and with complex CHD in parent reports. Among patients with previous/current PST, 56.4% reported high perceived support. Conclusions: The P-BAHn study highlights the need for targeted psychosocial support for children and adolescents with CHD, including female patients, those with complex conditions, and patients reporting school- or crisis-related burden. Retrospectively reported pandemic-related burden underscores the importance of integrating crisis-sensitive strategies into psychosocial care frameworks. Longitudinal studies are essential to understand mental health trajectories and to evaluate the sustained patient- and parent-perceived benefit as well as clinical effectiveness of PST use. Enhancing support services and refining intervention models will improve the well-being and quality of life for young CHD patients. Full article

This narrative review examines research on artificial intelligence (AI), including rule-based systems, natural language processing models, and large language models, in relation to well-being, social connection, and personal growth. After briefly tracing the history of AI, we review evidence from AI-facilitated well-being interventions, educational applications, interpersonal skill development, AI-mediated communication, and AI companionship. In clinical and nonclinical settings, structured AI applications show some short-term benefits for anxiety, stress, loneliness, self-esteem, learning, and social confidence, while emerging evidence suggests that AI companions may provide temporary emotional support and a sense of connection. However, findings across these domains are not consistent and appear to depend on how AI is used, the structure of the interaction, the type of feedback provided, and the broader context. Important risks include emotional dependence, overreliance, reduced human connection, weakened authenticity in communication, cognitive or socioemotional skill erosion, bias, and poor crisis response. Preliminary findings suggest that AI may be most beneficial when used to support, rather than replace, human capacities and relationships. Future research should examine long-term outcomes, individual differences, real-world use of publicly available AI systems, and the conditions under which AI strengthens or undermines well-being, relationships, and personal growth. Full article

The COVID-19 pandemic intensified concerns about burnout in healthcare leadership, yet evidence specific to nurse managers remains fragmented. This integrative review synthesized recent research, organized burnout-associated factors using the Job Demands–Resources (JD–R) model, and examined pre-pandemic and pandemic-era shifts in research focus. Following Whittemore and Knafl’s methodology, four databases (Ichushi-Web, PubMed, CINAHL, and MEDLINE) were searched for peer-reviewed quantitative, qualitative, and mixed-methods studies published from 1 April 2019 to 31 August 2025 that examined burnout levels, prevalence, or related factors among nurse managers. Extracted findings were mapped to Job demands, Job resources, and Personal resources and compared according to the data-collection period. Twenty-five studies were included, with substantial heterogeneity in burnout instruments and cutoff values. Core job demands related to managerial responsibility, workload, and resource management were identified throughout the literature, while pandemic-era studies additionally highlighted frequent protocol changes, heightened uncertainty, and fear of infection. Key resources included organizational support, positive team communication, peer support, and adequate workload and material resources, and resilience was more frequently reported in pandemic-era studies. Overall, the findings demonstrate how crisis-related shifts in demands and resources shape burnout risk among nurse managers and support the application of JD–R–informed, context-adaptive prevention strategies. They also underscore the need for standardized burnout assessment and more robust interventional and longitudinal research. Full article

This study investigates the emergence of psychosocial risks during the COVID-19 pandemic in the architecture, engineering, and construction (AEC) industry. It aims to enhance mental health outcomes for project professionals by identifying pandemic-related stressors, evaluating the role of organisational interventions, and developing a practical framework for psychosocial risk management. Guided by Job Demands–Resources (JDR) theory, the research involved a literature review, expert consultations, and a structured survey targeting AEC project managers. The findings reveal that COVID-19-related psychosocial risks such as work overload, isolation, job insecurity, and blurred work–life boundaries were negatively associated with mental health. Organisational interventions were positively associated with improved mental health. However, the moderating effect of organisational intervention on the relationship between psychosocial risks and mental health was not statistically significant. This study proposes a framework to guide AEC organisations in integrating proactive mental health strategies into everyday project practices. While the data are sector-specific and collected during a crisis period, the implications extend to broader project-based settings. This research offers practical insights for AEC firms, policymakers, and industry stakeholders on supporting workforce well-being through targeted interventions. It also contributes conceptually by linking pandemic-induced stressors to established theoretical models of occupational stress, highlighting the need for sector-specific strategies in promoting psychological safety in high-demand work environments. Full article

Solid waste management (SWM) is a major global challenge for environmental sustainability and public health. This study analyzed SWM perceptions and practices before and during the COVID-19 pandemic in Playa Boca Chica, Tecpan de Galeana, Guerrero, Mexico, using a descriptive and quantitative approach. Data was collected from 60 households between September and October 2022 and analyzed using SPSS statistical software version 26; reliability was confirmed with Cronbach’s Alpha; and the generation-associated greenhouse gas (GHG) emissions were estimated using the SWM-GHG climate calculator. This study explored socio-environmental dynamics in informal coastal settlements through a case study in the Global South. The results showed that waste generation remained stable during the pandemic (3.25 kg/day; p = 0.116), suggesting a pattern of behavioral rigidity in which entrenched waste management practices persisted despite the global health crisis, likely due to the absence of structural environmental interventions and policy-driven behavioral incentives. The climate calculator estimated GHG emissions of 92 and 99 tons of CO₂-eq/year before and during the pandemic, respectively. Residents highlighted the need for improved infrastructure, recycling, and composting, while 97% emphasized environmental education and waste separation. The absence of a local waste management policy contributes to persistent emissions, underscoring the need for integrated and sustainable SWM strategies. Full article

In recent years, there has been an unprecedented upsurge in opioid prescriptions for pain management. Consequently, the widespread availability of these medicines has led to an increase in misuse and abuse. This has led to a greater number of overdose-related deaths. The high prevalence of drug misuse was born of multiple and complex societal factors. However, from a medical perspective, critical contributors to the dire consequences of the crisis have been the need for chronic pain relief, as well as mental health issues within communities. Chronic pain coupled with psychological distress exacerbates patients’ predicaments and thus further fuels the crisis. Anxiety and depression have bidirectional and complex relationships with pain. The somatic symptoms associated with anxiety potentially worsen pain, whilst pain emanating from a chronic condition worsens anxiety. The same relational dynamic applies to depression and pain. Thus, these psychopathological states may be major contributors to the opioid abuse epidemic. Thus, psychosocial management as a first-line treatment instead of starting with drug treatments seems an enlightened approach to this problem. Cognitive behavioral therapy (CBT) has been proven to be effective in managing specific symptoms associated with chronic pain. Similarly, patient education has been shown to be a viable alternative to drugs for certain aspects of chronic pain treatment. We consider that the opioid crisis could be addressed with a greater reliance and emphasis on non-pharmacological approaches to managing chronic pain patients. This mini-review examines non-pharmaceutical and monitoring-based interventions to reduce opioid misuse risk among adults prescribed opioids for chronic non-cancer pain. Studies were identified through PubMed/MEDLINE, Scopus, and Google Scholar using terms related to chronic pain, prescription opioid misuse, opioid use disorder, cognitive behavioral therapy, patient education, prescription drug monitoring programs, digital health, telehealth, and non-pharmacological interventions. Studies were included if they focused on adults with chronic pain who were prescribed opioids or at risk of misuse, and evaluated interventions aimed at reducing unsafe opioid use, misuse risk, or opioid-related harm. Evidence was synthesized narratively to identify key intervention approaches, limitations, and clinical implications. Full article

Background: Diabetes is a growing public health crisis across the Arab region, where rapid urbanization, dietary transitions, and physical inactivity have contributed to some of the highest diabetes rates globally. Despite a growing recognition of the problem, most diabetes prevention efforts in the region remain small-scale or insufficiently adapted to the sociocultural realities of adults living in the UAE. Evidence-based diabetes prevention strategies, such as the United States’ Centers for Disease Control Diabetes Prevention Program (DPP), reduce the risk of developing diabetes but remain underutilized. Methods: The objectives of this study were to (1) describe the systematic cultural adaptation of the Evidence-based Intervention for Diabetes Prevention (EID) using the Framework for Reporting Adaptations and Modifications–Expanded (FRAME), and (2) assess the preliminary acceptability of the adapted materials through formative focus groups. Results: Materials were culturally tailored to address both deep and surface structures. Deep structure adaptations incorporated Arab cultural values, social norms, and religious practices, including Ramadan-specific content. The original 26-session curriculum was condensed to 12 weekly sessions based on prior research and stakeholder input. Surface-level adaptations included translation into Arabic and development of culturally relevant educational videos. Three formative focus groups (n = 7 total participants) provided preliminary findings of strong acceptability of simplified, culturally relevant, and digitally supported materials. Conclusions: This work will inform the adaptation of an evidence-based lifestyle change program aimed at preventing type 2 diabetes in high-risk individuals to better meet the needs of adults living in the UAE. While some countries have created their own national diabetes prevention efforts, like the United Kingdom, there is notably no similar program in the Arab world. Full article

To address the “greenwashing” trust crisis induced by information asymmetry in sustainable supply chains, this study develops a comprehensive game-theoretic model integrating Stackelberg and evolutionary game theories (EGT). We quantitatively investigate the dynamic interactions among multi-dimensional consumer preferences, blockchain implementation costs, and boundedly rational government interventions. Our analysis yields three core contributions. First, we analytically reveal the “double-edged sword effect” of blockchain adoption. While structural transparency unlocks a trust dividend, exorbitant technological costs trigger a “budget crowding-out effect.” Quantitative results demonstrate that breaching the absolute Feasibility Threshold completely cannibalizes the environmental budget, driving substantive green investments strictly to zero. Second, EGT analysis proves that isolated punitive carbon taxes trap supply chains in a suboptimal “shallow greening” equilibrium. A composite tax-subsidy policy is structurally required to expand the feasible cost space and hedge against technological risks. Finally, we formulate a dynamic policy exit mechanism. As blockchain infrastructure matures and the endogenous green premium effectively offsets implementation costs, regulators must systematically phase out subsidies and converge toward a single-taxation regime to prevent corporate policy arbitrage and alleviate long-term public financial burdens. Full article

Background: Generative AI (GenAI) mental health chatbots are increasingly being developed to help address persistent barriers to mental healthcare. Unlike earlier rule-based and retrieval-based systems, GenAI chatbots generate open-ended outputs that can be inaccurate and unsafe. Documented harms from general-purpose GenAI chatbots have highlighted the need for purpose-built interventions with dedicated safeguards, yet how safety is implemented in such interventions remains poorly understood. Methods: This scoping review followed the Joanna Briggs Institute methodology and PRISMA-ScR guidelines, with a prospectively registered and peer-reviewed protocol. A systematic search of seven academic databases and search engines including MEDLINE, Scopus, PsycINFO, ACM Digital Library, IEEE Xplore, Google Scholar and Consensus was conducted in July 2025. Two reviewers independently screened records and extracted data. Safety mechanisms and risk mitigation strategies were narratively synthesised across three pre-specified domains: technical safeguards, pre-deployment safety considerations, and delivery-phase risk mitigation strategies. Results: Twenty-one studies across 11 countries were included. Most interventions incorporated at least one technical safety mechanism, most commonly fine-tuning and prompt engineering. A smaller subset implemented layered safety architectures combining retrieval systems, content filters or risk classifiers, and rule-based algorithms. Pre-deployment safeguards included clinical expert and user co-design approaches, research ethics procedures, and data privacy measures. During intervention delivery, detailed onboarding with role clarification was common, but human oversight was limited. Crisis referral protocols varied in rigour but were mostly underdeveloped, and systematic adverse event monitoring was sparse. Documented safety failures included missed suicidal ideation and provision of inaccurate clinical information. Conclusions: GenAI chatbot interventions require a robust sociotechnical approach that integrates technical safeguards with user co-design, procedural controls, and human oversight. Future research is needed to evaluate efficacy, improve safeguards and standardise safety outcome measurement. Regulatory oversight proportional to the risks these systems carry is required to enable integration into stepped or blended mental healthcare. Full article

Falls in older adults and individuals with balance impairments remain a major concern because they are closely associated with injury, reduced mobility, and loss of independence. This study presents a preclinical proof-of-concept for a cognitive smart walker architecture that combines user-compatible walking assistance with active safety intervention. The system integrates a 2D LiDAR sensor for contactless lower-limb monitoring, a six-degree-of-freedom (6-DOF) force/torque sensor to measure user–walker interaction, and an inertial measurement unit (IMU) for dynamic monitoring, with all data processed in real time on a Raspberry Pi/ROS-based platform. Normal walking assistance is provided through a command-level variable admittance-based controller that converts interaction forces into a smoothed signed duty-cycle command rather than a rigid speed-control signal. Safety decisions are managed by a Hierarchical State Machine (HSM). Early-risk conditions are handled through motor-based dynamic braking, whereas severe physical crises additionally deploy lateral support legs to enlarge the base of support. Within this framework, the system can detect and manage foot entanglement, grip loss, forward fall, vertical collapse, lateral fall, successive crises, and recovery-abort events. In experiments across multiple scenarios, the system correctly detected all 50 crisis cases and did not issue unnecessary interventions in 30 non-crisis cases. These findings show that the proposed architecture can preserve transparent walking assistance during normal gait while providing graded, context-sensitive active safety when risk emerges. Full article