Search Results (36,667)

Background: Population ageing, increasing care demands, and rapid advances in artificial intelligence and robotics have intensified interest in care robots as potential tools to support independent living and complement human caregiving. However, the successful implementation of robotic technologies depends largely on public acceptance, which is influenced by functional, psychological, ethical, cultural, and generational factors. Objective: This study examined generational differences in the acceptance of care robots among Portuguese adults by integrating the Almere Model of technology acceptance with the Katz Index of Activities of Daily Living (ADL) and the Lawton–Brody Instrumental Activities of Daily Living (IADL) Scale. The research sought to determine whether acceptance varies according to generation and the type of caregiving activity performed by the robot. Methods: A cross-sectional quantitative study was conducted using an online questionnaire administered to a purposive sample of 235 adults residing primarily in the Lisbon Metropolitan Area, Portugal. The questionnaire combined constructs from the Almere Model with perceptions of robotic assistance for ADLs and IADLs. Principal Component Analysis, reliability analysis, descriptive statistics, and inferential analyses were performed to examine differences across generational groups. Results: Acceptance of care robots was strongly task-dependent. Participants expressed significantly greater acceptance of robots assisting with instrumental activities, including housekeeping, shopping, transportation, meal preparation, and medication management, than with intimate personal care activities such as bathing, dressing, toileting, feeding, and continence care. Contrary to common assumptions regarding digital natives, Generation Z reported higher levels of fear, discomfort, and perceived intimidation than Generation X and Baby Boomers. Older generations generally demonstrated more pragmatic acceptance of robotic assistance, particularly regarding future support needs associated with ageing. Across generations, respondents preferred robots with more human-like appearances; however, emotional trust remained substantially lower than perceived functional usefulness. Conclusions: The findings suggest that acceptance of care robots is conditional rather than universal and is shaped by the nature of the caregiving task, generational differences, and broader emotional and cultural perceptions of care. Integrating the Almere Model with established ADL and IADL frameworks provides a novel perspective by linking technology acceptance to specific functional domains of caregiving. The results support the view that care robots are more likely to be accepted as complementary tools that enhance human-centred care rather than as substitutes for professional or family caregivers. Given the purposive and geographically limited sample, the findings should be interpreted cautiously and not generalised to the wider Portuguese population. They nevertheless provide valuable implications for the design of socially assistive robots, healthcare practice, and public policy in ageing societies. Full article

Globally, respiratory diseases such as asthma, chronic obstructive pulmonary disease (COPD) and pneumonia affect populations significantly, requiring early and accurate diagnosis for effective clinical management. Manual auscultation and expert interpretation are the common shortcomings in conventional diagnostic approaches, as they lead to time-consuming and inconsistent analysis. To address these limitations, an artificial intelligence-driven framework for respiratory disease classification using multi-spectral feature extraction and deep learning architectures is proposed to classify four different respiratory conditions: Asthma, COPD, Pneumonia and Healthy. The dataset is collected from Kaggle’s respiratory sound database and the COUGHVID V3 database, which together contain 322 Asthma signals, 746 COPD signals, 323 Pneumonia signals and 174 Healthy signals. Subsequently, the features are extracted using four different feature extraction techniques—Constant Q Transform (CQT), a Gammatone spectrogram, Mel-Frequency Cepstral Coefficients (MFCC) and Perceptual Linear Prediction (PLP)—and these extracted spectral representations are provided as inputs to various deep learning models such as a Deep Convolutional Neural Network (Deep CNN), a Temporal Attention Network (TAN) and an Autoencoder for automated feature learning and disease classification. The proposed framework is evaluated using several performance metrics, and the experimental results clearly indicate that the performance of the proposed classification framework strongly depends on the selection of spectral feature extraction techniques and deep learning models. Among all the evaluated combinations, it is evident that the Autoencoder model integrated with CQT features exhibited the best classification performance, with an accuracy of 98.72%, precision of 98.74%, recall of 98.72%, Matthews correlation coefficient (MCC) of 98.11%, Cohen’s kappa value of 98.10% and the least log loss of 0.025. The proposed artificial intelligence (AI)-enabled respiratory disease classification framework has demonstrated the ability to produce a reliable computer-aided diagnostic system which is suitable for smart healthcare applications and automated pulmonary disease screening. Full article

Breast cancer constitutes one of the oncological diagnoses with the greatest impact on women’s lives, with consequences that extend beyond active treatment into a survivorship period marked by profound transformations in identity, relationships, and well-being. This study aimed to explore the impacts that breast cancer produces on the everyday lives of survivor women in the municipality of Villarrica, La Araucanía Region. A qualitative methodology with a phenomenological orientation was employed, based on discourse analysis of three focus groups with 20 female breast cancer survivors between 35 and 70 years old. The analysis identified five categories: impact on everyday life and work, management of uncertainty and fear, transformation of self-care and life priorities, support networks and community, and barriers to accessing the healthcare system. The findings demonstrate the coexistence of posttraumatic growth and persistent psychological distress, together with structural inequities that limit access to comprehensive care during the survivorship period. It is concluded that cancer survivorship demands public policy responses that are continuous, multilevel, and integrative of a gender perspective, articulating individual, family, and community interventions from primary healthcare. Full article

Background: Small-scale illegal gold mining, or “Galamsey”, is an important livelihood in Ghana but exposes workers to serious occupational health risks. This study aimed to explore the lived experiences, perceptions of health hazards, and coping strategies of workers from illegal small-scale gold mines in the Atwima Mponua District to inform context-specific interventions and policies. Methods: This qualitative study employed a phenomenological design to explore the occupational health experiences of workers from illegal small-scale gold mines in the Atwima Mponua District. A total of 16 purposively selected workers, ten men and six women, aged 27 to 44 years, with mining experience ranging from 3 to 12 years, working across roles including excavators, washers/panners, chemical processors, ore grinders, and support workers, were interviewed in depth, using a semi-structured interview guide. Thematic analysis was used to analyse the data according to Braun and Clarke’s model. Findings: There were four dominant themes: (1) normalization of risk and fatalistic attitudes about mining risks; (2) Respiratory Manifestations and Dust-Related Ailments; (3) chemical exposures and dermatological conditions; and (4) traumatic injuries from pit collapses, mining equipment accidents, and heavy physical labour. Participants demonstrated low levels of knowledge about long-term health outcomes and reported substantial obstacles to accessing healthcare, including fear of legal action, financial constraints, and geographic isolation. Conclusions: Workers from illegal small-scale gold mines in the Atwima Mponua District bear a serious, hidden occupational health burden normalized by poverty and criminalization. Urgent, comprehensive interventions are needed that address legal protection, economic alternatives, accessible healthcare, and harm reduction. Full article

Breast cancer is a leading public health concern among low- and middle-income countries, primarily due to inadequate knowledge and lack of early detection practices. This study aimed to assess knowledge about the signs and symptoms, risk factors, and screening procedures for breast cancer, and barriers and perceptions of screening, among female university students of Pakistan to explore the relationship between socio-demographic variables and levels of knowledge about breast cancer. A total of 413 participants took part in a cross-sectional survey and completed a self-administrative online questionnaire. The results reflected an overall poor knowledge among female students, with only 3.9% demonstrating adequate knowledge about risk factors and 7.7% correctly recognizing 80% or more relevant signs and symptoms. Embarrassment (58.8%) and lack of confidence in communicating symptoms with healthcare providers (50.6%) were the most commonly reported barriers for breast cancer screening. Young age and income status below PKR 50,000 per month (OR = 3.47, 95%CI: 1.20–10.07 rel. to highest income) were significantly associated with poor breast cancer knowledge. In conclusion, this study reports sub-optimal breast cancer awareness levels in the target population, indicating the need for awareness programs and policy-driven interventions to reduce barriers, improve knowledge, and improve adherence to screening recommendations. Full article

Background: Upper airway tract infections (UATIs) are among the most common infectious diseases worldwide, accounting for an estimated 12.8 billion episodes annually and more than 8 million disability-adjusted life years (DALYs). Despite their generally self-limiting nature, their cumulative clinical, socioeconomic, and public health burden remains substantial, particularly in children, older adults, and low- and middle-income countries (LMICs). Methods: We performed a structured narrative review of the peer-reviewed literature and reports from major international health organizations to summarize the current evidence on the epidemiology, etiology, clinical impact, socioeconomic burden, prevention strategies, and future challenges associated with UATIs. Particular attention was given to the influence of antimicrobial resistance, vaccination policies, and lessons learned from the COVID-19 pandemic. Results: UATIs remain one of the leading causes of healthcare utilization worldwide. Children experience the highest incidence, averaging 6–8 episodes annually, whereas vulnerable populations are at increased risk of complications and hospitalization. Marked geographical disparities persist, with LMICs experiencing a disproportionate burden due to limited healthcare access, lower vaccination coverage, and higher complication rates. Inappropriate antibiotic prescribing continues to accelerate antimicrobial resistance, while the COVID-19 pandemic profoundly altered the epidemiology of respiratory infections and demonstrated the effectiveness of non-pharmaceutical interventions. Advances in vaccination, antimicrobial stewardship, rapid diagnostics, and novel therapeutic strategies offer promising opportunities to reduce disease burden. Conclusions: Reducing the global burden of UATIs requires integrated public health strategies combining equitable vaccine access, effective antimicrobial stewardship, strengthened healthcare systems, and sustained surveillance. Lessons learned from the COVID-19 pandemic provide a unique opportunity to improve preparedness for future respiratory outbreaks while addressing persistent regional inequalities in prevention and care. Full article

Background/Objectives: Psychological safety is increasingly recognized as a key factor in promoting communication, learning, and teamwork in healthcare, with implications for patient safety culture. Although psychological safety and patient safety culture have been reviewed separately, evidence on how nurse managers contribute to both constructs within nursing teams remains fragmented. This scoping review aimed to map the available evidence on the role of nurse managers in promoting psychological safety and patient safety culture in nursing teams, identify leadership behaviors, strategies, and competencies, and highlight facilitating factors and barriers. This review provides an integrated synthesis of evidence on how nurse managers contribute to fostering both psychological safety and patient safety culture within nursing teams, addressing a gap in the existing literature. Methods: A scoping review was conducted following established methodological frameworks and prospectively registered in the Open Science Framework. Systematic searches were conducted in MEDLINE (PubMed), CINAHL, Psychology and Behavioral Sciences Collection, Scopus, BVS, and WorldCat for gray literature, with the final search performed on 11 May 2026. A total of 229 records were identified, with 112 duplicates removed. The remaining 117 records were screened, resulting in 25 studies included for analysis. Data were extracted and synthesized using thematic analysis. Results: Twenty-five sources were included, comprising predominantly quantitative (mainly cross-sectional) studies, together with qualitative studies, systematic reviews, conceptual papers, expert opinion papers, and gray literature. The findings highlight a close and interdependent relationship between psychological safety and patient safety culture. Nurse managers’ relational leadership behaviors—such as visible presence, active listening, emotional support, and constructive feedback—emerged as key strategies for fostering psychologically safe environments. The reviewed evidence suggests that psychological safety may mediate the relationship between leadership practices and communication, speaking up, incident reporting, and organizational learning. Facilitators include supportive leadership and a just culture, while barriers encompass punitive environments, hierarchical structures, workload pressures, and resource constraints. Conclusions: The findings suggest that psychological safety may be an important mechanism through which nurse managers contribute to patient safety culture. However, leadership alone is insufficient, requiring alignment with organizational conditions and system-level support. These findings support strategies to strengthen safety culture and guide leadership development. Full article

Background: Adolescent pregnancy is a public health problem in Latin America, with a higher prevalence in rural areas. This population faces barriers to accessing education and healthcare services, increasing the risk of maternal and neonatal complications. Methods: This study employed a cross-sectional analytical design and included 72 adolescents aged 12 to 17 years in Lonya Grande. The associations between structural and intermediate social determinants of health and the prevalence of adolescent pregnancy were assessed. Results: Among the structural determinants, extreme poverty (PR: 2.48; 95% CI: 1.23–5.03; p = 0.004), type of public health insurance (PR: 3.05; 95% CI: 1.21–7.72; p = 0.005), and parental educational level were associated with a higher prevalence of adolescent pregnancy. After adjustment, having public health insurance (aPR: 0.49; 95% CI: 0.36–0.66; p < 0.001) and having parents who had completed primary school (aPR: 1.67; 95% CI: 1.15–2.42; p = 0.01) were associated with a higher prevalence of adolescent pregnancy. Among the intermediate determinants, alcohol consumption (PR: 2.77; 95% CI: 1.85–4.16; p < 0.001), knowledge about contraceptives (PR: 0.21; 95% CI: 0.03–0.85; p = 0.04), and being 17 years old (PR: 4.44; 95% CI: 1.48–13.39; p = 0.01) were associated with adolescent pregnancy. In the adjusted model, adolescents aged 15 years (aPR: 3.15; 95% CI: 1.04–9.52; p = 0.04) maintained a higher prevalence compared with those aged 14 years. Conclusions: An association was found between structural and intermediate social determinants of health and adolescent pregnancy in rural settings. These findings suggest the need for additional studies to better understand interventions targeting these associated factors. Full article

Hospital buildings (HBs) are among the most energy-intensive public buildings, yet their life-cycle carbon characteristics, emission drivers, and long-term mitigation potential remain insufficiently quantified. This study establishes a comprehensive life-cycle carbon assessment framework for HBs based on life cycle assessment (LCA), using a Grade-A tertiary hospital in Shenzhen, China, as a case study. The framework quantifies carbon emissions across the materialization, operation, and demolition stages, and estimates operational emissions from public hospital buildings at the city scale. Logarithmic Mean Divisia Index (LMDI) decomposition and Long-range Energy Alternatives Planning (LEAP) modeling were subsequently applied to identify historical drivers and evaluate future mitigation pathways. The results show that the case hospital generated approximately 0.57 Mt CO₂e of gross life-cycle carbon emissions over a 50-year service life, with the operational stage dominating approximately 88% of net emissions. Electricity consumption accounted for 94% of operational energy-related emissions, while HVAC systems and the Diagnostic departments were identified as major carbon hotspots. At the city scale, the gross operational emissions of 73 public hospitals in Shenzhen were estimated at approximately 0.74 Mt CO₂e in 2020 within the defined accounting boundary. For the broader citywide hospital sector, LMDI analysis revealed that annual operational emissions increased from approximately 0.25 Mt CO₂e in 2006 to 0.91 Mt CO₂e in 2020, primarily driven by healthcare service demand and hospital infrastructure expansion, whereas the declining operational carbon emission coefficient provided a partial offset. LEAP scenario analysis further demonstrated that net operational emissions peaked in 2050 under BS and in 2030 under SI and SII. Under SIII, emissions declined continuously from the 2020 base-year level to approximately 0.29 Mt CO₂e in 2060, representing a reduction of approximately 68%. These findings highlight the necessity of coordinate building energy optimization, healthcare infrastructure development, and energy system decarbonization for low-carbon transformation of hospital buildings. Full article

As knowledge graphs are increasingly applied in sensitive domains such as healthcare, ensuring data confidentiality and fine-grained access control over outsourced graph data has become critical. In this paper, we propose EFKG, an Efficient and Fine-grained Access Control Encrypted Knowledge Graph construction scheme that simultaneously achieves data confidentiality, fine-grained access control, and high-performance multi-hop search over encrypted knowledge graphs. Compared with existing approaches, EFKG not only supports efficient single-hop and multi-hop retrieval with $O\left(1\right)$ complexity per hop, but also satisfies fine-grained access control requirements in multi-user settings. Regarding security, we rigorously prove that EFKG achieves $\mathcal{L}$-adaptive security under the standard leakage function paradigm. Extensive experiments on real-world datasets confirm that EFKG achieves microsecond-level single-hop search and scalable multi-hop traversal, offering a superior trade-off between efficiency, security, and functionality. Full article

Taiwan has one of the highest dialysis prevalences worldwide, making safe and reliable hemodialysis monitoring a critical sensor-based healthcare challenge. Modern hemodialysis machines integrate heterogeneous multimodal sensors (pressure, flow, conductivity, temperature, and cardiovascular signals), but differences in machine brands, data formats, and privacy constraints hinder centralized learning and robust complication prediction. This work proposes a Medical IoT-oriented federated learning framework, PSOFed-HD, that performs dual-layer Particle Swarm Optimization (PSO) to enhance heterogeneous sensor fusion for predicting dialysis-related hypotension and discomfort events. The events are defined as abnormal blood-pressure states, defined as systolic blood pressure <90 mmHg. Each hemodialysis machine is paired with an edge gateway acting as an FL client, where local PSO optimizes CNN feature weights over non-IID sensor subsets, while the central server applies PSO-driven aggregation to adaptively weight client models according to validation performance. Experiments on real-world hemodialysis datasets with 17 most commonly seen HD physiological features demonstrate that standard FedAvg yields an accuracy of 65.24% and F1-score of 0.5318, server-side PSO improves accuracy to 75.11%, and client-side PSO further raises accuracy to 81.97%. The proposed dual-layer PSO framework achieves the best performance, with 90.56% accuracy and an F1-score of 0.8533, along with superior ROC characteristics (AUC = 0.908) and stable cross-validation across 11 folds. State-of-the-art federated learning techniques for non-IID data such as SCAFFOLD and FedProx are also examined using the same heterogeneous HD dataset. The performance is close to our client-only PSO techniques, proving the merits of our dual-layer PSO architecture. These results confirm that jointly optimizing local feature representations and global aggregation weights enables effective fusion of heterogeneous hemodialysis sensor data under privacy-preserving Medical IoT constraints, providing a practical decision-support approach for real-time complication prediction in dialysis units. Future work will incorporate temporal models such as LSTM or Transformer architectures to achieve early event prediction. Full article

Background/Objectives: Occupational contact dermatitis (OCD) is the most common work-related skin disease, accounting for roughly 90–95% of occupational dermatoses and falling predominantly on the hands. It is rarely dangerous yet imposes a substantial burden through impaired quality of life, lost productivity, and premature exit from affected trades. The COVID-19 pandemic intensified this burden among healthcare workers, in whom the pooled one-year prevalence of self-reported hand eczema reaches around 27%; meta-analytic data link the increased risk principally to frequent handwashing and wet work rather than to alcohol-based hand rub. Methods: This narrative review, which follows a non-systematic, thematically organised search strategy rather than PRISMA methodology, integrates current evidence on the epidemiology, pathophysiology, diagnosis, prevention, and management of OCD, with particular emphasis on the self-reinforcing cycle linking skin barrier disruption, microbiome dysbiosis, and antimicrobial-peptide dysregulation to inflammation. Results: We critically appraise the prevention evidence, foregrounding the low certainty of the existing trial base and the tension between the randomised trials of primary and secondary prevention, which have been null, and the encouraging but uncontrolled results of structured tertiary-prevention programmes. We summarise recent therapeutic advances, including topical delgocitinib, and situate the field within the World Health Organisation’s 2025 recognition of skin diseases as a global public health priority. Established evidence and hypotheses are kept separate throughout: we additionally advance, explicitly as a conjecture rather than as a demonstrated mechanism, a conceptual trans-kingdom dialogue model in which protease-generated LL-37 fragments may modulate staphylococcal quorum sensing, and each step of that model is labelled according to whether the supporting evidence is direct, extrapolated, or as yet untested. Conclusions: We argue that the prevention failure is less one of biology than of trial design and measurement, and outline the research needed to close the gap. Full article

Although high-quality healthcare requires both technical expertise and soft skills, the precise connections between emotional intelligence (EI), interpersonal skills (IS), and communication skills (CS) remain insufficiently explored. This lack of research creates an educational gap in training students to effectively manage clinical anxiety. This study aims to analyze the predictive and mediational role of IS between EI and CS in healthcare undergraduates. A quantitative, cross-sectional study was conducted with 816 Spanish undergraduate healthcare students recruited between October and December 2024. The inclusion criteria required participants to be actively enrolled in a healthcare degree and provide written informed consent. Data from four standardized self-reports were analyzed using Structural Equation Modeling (SEM) estimated via Unweighted Least Squares (ULS) to account for non-normal data distributions. The structural model showed that EI positively predicted IS (β = 0.69) and directly predicted CS (β = 0.35). Crucially, IS strongly predicted CS (β = 0.69), acting as a key psychological mediator. Together, EI and IS explained 91% of the variance in communication skills (R² = 0.91). These findings imply that interpersonal skills structurally translate internal emotional regulation into effective outward communication. For educators and professionals, this underscores the need for healthcare training to move beyond basic communication protocols to integrate formal emotional intelligence and social skills programs to enhance clinical patient safety and prevent student burnout. Full article

This article describes a new hardware/software alert system designed to assist elderly people and prevent falls due to bed-exit events at night. To assist the elderly during the night, it is important to implement automatic lighting activation in the bedroom. This helps reduce the risk of falls when they need to, for example, go to the bathroom. The caregiver can be alerted immediately via Wi-Fi, during the night, providing immediate assistance to the elderly person. The developed HW/SW system combines a passive infrared motion sensing device, a light sensor, and dedicated hardware based on the ESP32-C6 RISC-V microcontroller that communicates via Wi-Fi with a developed Android dedicated App, which the caregiver can access using a tablet or smartphone. Nighttime falls remain one of the most serious health problems for older people. The main innovative contribution of this work is the development of a low-cost preventive battery-free assistive system that does not require an internet access contract, preserves the elderly person’s privacy, and promptly alerts the caregiver whenever the elderly person gets out of bed during the night. The system is directly integrated with automated lighting, preventing the elderly person from walking in the dark and without appropriate aid. The system also supports the caregiver by generating alerts through an open-source mobile App. Full article

Relational databases power high-stakes decisions in lending, healthcare, and justice, yet SQL lacks native constructs for causal and counterfactual reasoning. Prior SQL-based causal systems address parts of this gap but do not unify treatment-effect estimation with counterfactual generation in a single, composable SQL surface. We present a system that extends the SQL dialect with two declarative operators: EXPLAIN_CAUSALLY_WHY ($\psi$) for estimating average and conditional treatment effects via meta-learners, and EXPLAIN_COUNTERFACTUAL ($\phi$) for generating diverse, constraint-respecting alternatives via a hybrid KD-tree/LSH pipeline. Both operators consume standard SQL relations (joins, filters, projections) and return table-valued results with optional diagnostics, confidence intervals, and feasibility metrics. We formalize the operators in relational algebra, and describe our prototype system called PsiQL. On four evaluation datasets, PsiQL recovers a protective TWINS treatment effect, returns a non-significant COMPAS point ATE with imbalance diagnostics, flags HMDA covariate imbalance via built-in SMD checks, and generates constraint-respecting counterfactuals; a synthetic Census run serves as a balanced pipeline proof-of-concept alongside a real ACS diagnostic under severe imbalance. Full article