Search Results (377)

Background: The legal dimensions of nursing practice have become increasingly complex, yet the concept of legal literacy in clinical nurses remains insufficiently defined. Existing studies use terms such as legal knowledge, legal awareness, legal cognition, and law-based practice capacity inconsistently, which hinders conceptual clarity, valid measurement, and targeted educational intervention. This study aimed to clarify the conceptual boundaries, defining attributes, antecedents, consequences, empirical referents, and operational definition of legal literacy in clinical nurses. Methods: A concept analysis was conducted using Walker and Avant’s eight-step method. A systematic literature search was performed across six databases and supplemented by searches in JSTOR and HeinOnline for non-clinical uses of the concept. The search covered database inception to December 2024. Screening and reporting followed PRISMA 2020 guidelines. Fifty-six papers were included. Data extraction and analysis were conducted using content analysis with independent dual-reviewer coding. Results: Legal literacy in clinical nurses was distinguished from four related concepts: legal knowledge, legal awareness, legal cognition, and medical ethics. Three defining attributes were identified: normative understanding, value internalization oriented toward rights and responsibilities, and law-based situational practice. Antecedents were identified at macro, meso, and micro levels, while consequences were observed for individual nurses, healthcare organizations, and patient rights. Analysis of empirical referents revealed a persistent gap between conceptualization and measurement, particularly in assessing law-based situational practice. An operational definition was developed accordingly. Conclusions: Legal literacy in clinical nurses is a multidimensional professional competency integrating legal understanding, rights- and responsibility-oriented value internalization, and the ability to translate these into lawful clinical action. The findings provide a conceptual basis for future instrument development and targeted educational and management interventions. Full article

Digital trust in online interactions is commonly established through mechanisms such as decentralized identifiers (DIDs), verifiable credentials (VCs), and digital wallets. While these technologies support the correctness of individual components, they do not by themselves establish that an interaction as a whole is trustworthy. This limitation arises because real-world interactions consist of sequences of dependent steps, where inconsistencies may arise even when each step is locally valid. In this paper, we introduce the concept of executable trust, which models trust as a verifiable property of execution across complete interaction sequences. We formalize interactions as chains of TrustEvidence objects that capture step-level validity, constraint satisfaction, and cross-step dependencies. Based on this model, we show that step-level correctness alone is insufficient to characterize interaction-level trust under the stated execution assumptions. We further clarify the definition-induced modular structure of interaction-level trust and use a local failure-witness characterization to connect the formal model with scenario-based validation. We also present the Executable Trust Architecture (ETA), a five-layer architecture that operationalizes the proposed model through components for evidence generation, constraint enforcement, secure communication, and auditability. The feasibility of the approach is examined through scenario-based evaluation covering key trust properties—authenticity, integrity, privacy, and accountability—across nine scenarios comprising 68 test cases. The evaluation illustrates cases in which cross-step violations that pass conventional step-level verification are reflected as failures of ETA’s sequence-aware trust conditions under the evaluated assumptions. Full article

Animal abuse, encompassing active cruelty and neglect, is an underreported animal welfare and public safety concern. In Hungary, the parallel administrative and criminal law definitions of animal cruelty create additional uncertainty for citizens, professionals, and authorities, particularly regarding which institution should receive and evaluate a report. Existing reporting pathways are unstructured, and rarely produce documentation that is directly usable in subsequent administrative or criminal proceedings. This study presents the concept design of a structured online citizen-reporting interface developed for the Hungarian regulatory context. The interface functions as a structured intake tool: it guides non-expert reporters through standardised, category-based data entry; supports the submission of contextual evidence, including photographs, videos and location data; and prepares structured case files for transmission to the competent authority. The concept was shaped by a preliminary stakeholder needs assessment, in which people knowledgeable in animal welfare issues and members of the general public participated. The system does not perform legal or veterinary welfare assessment; instead, it standardises the information available to the responsible administrative, investigative or expert veterinary actor. Anticipated benefits include improved completeness of initial reports, clearer routing between administrative and criminal pathways, support for reporting, and a documentation format compatible with downstream expert evaluation. Full article

Background/Objectives: SARS-CoV-2 infection and COVID-19 vaccination have both been linked to post-acute alopecia, yet prevalence, patterns, and correlates remain poorly defined. We aimed to determine the prevalence and patterns of alopecia after SARS-CoV-2 infection and COVID-19 vaccination, and to identify independent correlates among Saudi adults. Methods: A cross-sectional study of 1261 Saudi adults (≥18 years) was conducted nationwide between March and July 2025 using a structured, self-administered, bilingual (Arabic/English) online questionnaire distributed via social media. Eligible participants had received at least one COVID-19 vaccine dose. Data captured demographics, vaccination type and dose history, prior SARS-CoV-2 infection, self-reported alopecia subtype guided by plain-language definitions, comorbidities, and nutritional status. Hair loss and subtypes were entirely self-reported and not clinically or dermoscopically confirmed. Chi-square tests assessed univariate associations, and multivariable logistic regression identified independent predictors of any self-reported hair loss. Results: Mean age was 28.0 ± 9.6 years; 62.1% were female, 97.7% vaccinated (76.0% Pfizer-BioNTech first dose, 58.1% three doses), and 53.6% reported prior COVID-19 infection. Overall self-reported hair loss prevalence was 62.4%, with hair loss reported by 57.5% of vaccinated and 49.7% of COVID-positive participants. Self-reported telogen effluvium was the predominant pattern (56.3%), followed by androgenetic alopecia (39.3%) and alopecia areata (2.7%). Independent correlates were female sex (aOR 4.89; 95% CI 3.78–6.32), vitamin/mineral deficiency (aOR 2.31; 1.73–3.09), iron deficiency anemia (aOR 1.89; 1.29–2.78), prior SARS-CoV-2 infection (aOR 1.31; 1.02–1.68), and COVID-19 vaccination (aOR 7.45; 2.51–22.12). Conclusions: Self-reported hair loss was reported by nearly two-thirds of Saudi adults after COVID-19 vaccination or infection. Female sex and correctable nutritional deficiencies are the strongest modifiable correlates and warrant targeted screening and counseling. Full article

Background: Retinal and optic nerve disorders remain major causes of visual morbidity worldwide. Ocular fundus flavoprotein fluorescence (FPF) imaging has emerged as a potential noninvasive biomarker of mitochondrial dysfunction for earlier detection and evaluation of disease severity. Methods: We conducted a Systematic Scoping Review of the diagnostic and correlational utility of quantitative FPF parameters in retinal and optic nerve diseases compared with healthy controls. Following PRISMA-ScR guidelines, we searched MEDLINE, Web of Science, Scopus, and CENTRAL for peer-reviewed human studies available online before 31 December 2025. Results: Seventeen studies were included, encompassing 1914 eyes and 1339 participants, and were predominantly cross-sectional. In healthy eyes, mean macular and optic nerve head FPF intensity were reported as 24.1 ± 12.2 gsu and 30.6 ± 14.6 gsu, respectively. Higher signals were reported in several disorders, including diabetes mellitus (76.0 [67.0–92.0] gsu), neovascular age-related macular degeneration (67.47 ± 17.77 gsu), and retinitis pigmentosa (50.5 ± 12.2 gsu). However, lower, unchanged, or stage-dependent signals were also observed within the same disease categories. Interpretation across studies was limited by substantial heterogeneity in patient selection, disease definitions, imaging protocols, control groups, and FPF outcome metrics. The precise cellular and sublayer origin of the detected signal also remains challenging to determine. Conclusions: Ocular fundus FPF imaging provides promising metabolic insight into retinal and optic nerve diseases. However, current evidence remains heterogeneous and largely cross-sectional, limiting clinical interpretability and generalizability. Longitudinal studies, technical standardization, and multimodal integration are needed to define reproducible disease-specific FPF profiles and improve translational applicability. Full article

Effective laboratory training is essential in engineering education, yet conventional on-site instruction is often constrained by time, accessibility, and safety considerations. To address these challenges, this study presents the design, implementation, and evaluation of a web-based virtual reality (WebVR) representation of a large-scale engineering laboratory constructed from massive colorized point cloud data. This study proposes a novel WebVR approach that integrates Unity and Potree for high-fidelity point-cloud visualization combined with advanced interactive capabilities in a browser-based virtual laboratory. It supports immersive first-person exploration, guided navigation, interactive hotspots conveying equipment and safety information, and emergency evacuation simulations. The usability, usefulness, and acceptance of the virtual laboratory were evaluated through an anonymous questionnaire administered to students and laboratory staff. User evaluation results indicated consistently positive feedback, with 100% of respondents rating the interface/navigation and visual/interactive content as good or excellent, 88.6% identifying scene realism as the biggest system strength (the most frequently selected), 74.3% reporting significantly higher engagement compared with traditional online laboratory training, and 82.9% indicating they would definitely recommend the system as a learning resource. In addition, a thematic analysis of qualitative feedback was performed to inform future enhancements of the WebVR environment. Overall, the findings demonstrate that the WebVR-based virtual laboratory can effectively complement conventional on-site laboratory instruction, offering a scalable, accessible, and low-risk platform that enhances learning experiences in engineering education. Full article

Systems that farm non-typical (wild) species for human consumption are on the rise globally, in contrast to more typical livestock production. In some instances, wildlife farming may arguably help alleviate poverty, provide sustainable animal protein, and be a useful strategy for conservation through reducing wildlife poaching or breeding some animals on farms for reintroduction. However, it is unclear whether farming non-typical species within variable and often unregulated systems truly offers these benefits or outweighs the costs including animal welfare implications, public health concerns, and normalising or intensifying the consumption of wild animals. A previous study proposed a sentiocentric ethical decision-making framework for the farming of wild species. In the present study we invited academic ‘key informants’ with specialised knowledge about farming non-typical species to pilot the framework via an online survey using a species of their choice and requested their feedback on its strengths and weaknesses. Thirteen respondents applied ten different mammalian, reptilian, insect, and avian species to the framework, spanning all continents. Ultimately, the framework outcome for 11 appraisals was that the chosen species may be suitable for farming. However, erroneous responses were likely in places, and there was some uncertainty over definitions of framework terminology. We publish resultant amendments to the ethical framework to clarify meaning and suggest that it can be applied proactively or reactively by different stakeholders (e.g., governments, businesses, and NGOs). We reflect our informants’ views, acknowledging the need to solicit expertise from additional stakeholders (e.g., farmers) and the role of cultural significance and rural communities when considering farming non-typical species. Full article

This study empirically analysed the class ruling at two South African universities. The principles underpinning the Canons of Taxation, Consumption Theory, and the Principle of Neutrality were reviewed as analytical benchmarks. The literature review synthesised prior studies that examined the ruling or explored apportionment practices within universities. A sequential mixed-methods approach was adopted, beginning with a quantitative phase followed by a qualitative phase. Quantitative data were collected from thirty (37) university staff members through an online questionnaire, and descriptive statistical analysis was conducted using SPSS (version 29). The qualitative phase involved online interviews with ten (10) tax and finance professionals engaged in apportionment practices at universities, capturing their experiences, perspectives, and insights. The data were analysed using thematic and transcript analysis with the aid of NVivo (version 20). The findings indicate that respondents believe the South African Revenue Service should revisit and improve the existing ruling. Concerns were raised regarding the lack of continuous training at universities, cost implications, and the complexity of Value-Added Tax apportionment. In the context of a rapidly evolving higher education sector, the VAT Act and the definition of educational services appear to require reform. Based on these findings, the study recommends that SARS consider revising the ruling by removing a prescribed apportionment rate and allowing universities to adopt methods that are practical and aligned with their operational contexts. Consistent with prior research, the study also finds that the input-based method remains complex, and that the definition of Value-Added Tax within the educational sector is overly broad. Full article

Conventional web analytics treats the human user as its fundamental unit of analysis, assuming stable preferences, identifiable intentions, and behavioral patterns that unfold over time. That assumption is under strain. Crawlers and traditional bots already account for a substantial fraction of online interactions, and autonomous AI agents are emerging as a further class of actors layered on top of this automated traffic. Unlike either, these agents do not possess persistent identities or psychologically grounded motivations. They are task-specific, dynamically instantiated processes whose behaviors are contingent and often orchestrated by external systems. Their presence weakens the interpretive value of core metrics, including sessions, engagement, conversion, and retention. A click may reflect an optimization routine, a proxy objective, or a recursive agent-to-agent exchange rather than meaningful human intent, and traditional inference frameworks cannot reliably distinguish among these possibilities. This is a position paper. It synthesizes literature across bot and agent detection, agent architecture, web measurement validity, governance of automated systems in adjacent sectors, and the epistemology of digital trace data, and it argues that web analytics should supplement, and in places replace, its human-centered model with an agent-aware model focused on interaction dynamics within hybrid ecosystems of human and non-human actors. The paper develops a working taxonomy of crawlers, traditional bots, AI agents, LLM-powered agents, and autonomous agents; identifies three properties of LLM agents (identity discontinuity by design, task-based instantiation, agent-to-agent loops) that distinguish the present challenge from prior bot-detection problems; examines opaque agent objectives, synthetic traffic loops, and the indistinguishability between human-originated and agent-mediated signals; and proposes five candidate measurement primitives (task chain, actor class, interaction provenance, objective alignment, signal authenticity) with explicit operational definitions. Governance machinery from energy systems and critical infrastructure offers a partial template, and we delimit which dimensions transfer and which do not. The contribution is conceptual and programmatic, presenting a vocabulary, set of candidate primitives, and research agenda for a field whose foundational unit of analysis is becoming unreliable. Full article

Background and Objectives: Artificial intelligence (AI) and extended reality (XR) are increasingly being integrated into telemedicine, yet clinician adoption depends not only on perceived utility but also on digital preparedness and technology-related burden. This study compared clinician acceptance of AI-only, XR-only, and combined AI+XR telemedicine scenarios and examined whether AI literacy, health literacy, technostress, age, and sex explained variability in acceptance. Materials and Methods: We conducted a cross-sectional, anonymous online vignette survey among 117 resident physicians and nurses from a tertiary-care hospital and affiliated outpatient clinics in Western Romania. Participants evaluated three randomized telemedicine scenarios (AI-only, XR-only, and AI+XR) using a 3-item Acceptance Index scored from 1 to 4. Additional measures included a study-developed AI literacy quiz, the Romanian-validated HLS-EU-Q16, an adapted brief technostress scale, prior AI/XR exposure, perceived accessibility, perceived value, privacy concern, and demographic variables. Results: Acceptance was highest for the combined AI+XR scenario (3.74 ± 0.49), followed by AI-only (3.64 ± 0.56) and XR-only (3.49 ± 0.64). AI+XR acceptance was significantly higher than AI-only and XR-only acceptance (both p < 0.001), although the absolute between-scenario differences were modest. Residents reported consistently higher acceptance than nurses across all scenarios, whereas sex differences were small and non-significant; younger age showed only weak inverse associations with acceptance. AI+XR acceptance correlated positively with AI literacy (ρ = 0.60) and health literacy (ρ = 0.23), and negatively with technostress (ρ = −0.47). In multivariable analysis, higher AI literacy (β = 0.603, p < 0.001) and health literacy (β = 0.241, p < 0.001) independently predicted higher AI+XR acceptance, whereas technostress was inversely associated (β = −0.212, p < 0.001). Conclusions: In this sample, clinicians rated the integrated AI+XR vignette most favorably, but the observed differences between scenarios should be interpreted cautiously and as hypothesis-generating rather than definitive evidence of superiority. Acceptance appeared to depend more on digital readiness and technostress than on age or sex, supporting implementation strategies focused on literacy-building, workflow fit, and burden reduction. Full article

Background/Objectives: There is a seemingly high prevalence of dietary supplement use in the kidney stone population. We aimed to understand patients’ perceptions of dietary supplements and their role in the management of kidney stones. Methods: We performed a standardized survey of patients presenting for management of kidney stones. We investigated the knowledge and use of apple cider vinegar (ACV), turmeric, and cranberry extract, as well as opinions on the safety of dietary supplements and sources of information. Descriptive statistics were used to summarize demographic and survey data. Results: Ninety-five patients were surveyed; 18 (18.9%) patients reported using ACV, 19 (20%) reported using cranberry extract, and 11 (11.6%) reported using turmeric. Similar numbers reported having heard of these dietary supplements being used for kidney stone prevention and/or treatment. Most patients believed these dietary supplements to be probably (ACV, n = 61, 64.2%; cranberry, n = 62, 65.3%; turmeric, n = 61, 64.2%) or definitely (ACV, n = 22, 23.2%; cranberry, n = 28; 29.5%; turmeric, n = 22, 23.2%) safe. For those who had heard about these supplements being used to treat or prevent kidney stones, friends/family (n = 25, 26.3%), online websites (n = 21, 22.1%), and social media (n = 15, 15.8%) were the most common sources of information. Conclusions: Apple cider vinegar, turmeric and cranberry extract have unknown risks or benefits in the management of kidney stones. Furthermore, their impact on stone pathophysiology remains unclear; however, many of our surveyed patient population uses them. Our study provides insight into patients’ use and perception of dietary supplements that clinicians should consider in the management of kidney stones. Further studies are needed to better counsel patients on the safety and efficacy of dietary supplements. Full article

Background: Metabolic dysfunction-associated steatotic liver disease (MASLD) is the most prevalent chronic liver disease worldwide and a major contributor to the global cardiometabolic burden. Early identification of patients at risk of metabolic dysfunction-associated steatohepatitis (MASH) and advanced fibrosis is essential to prevent liver-related and cardiovascular complications. In Spain, the burden of MASLD is increasing, yet information on routine clinical management by gastroenterologists remains limited. Methods: A nationwide cross-sectional online survey was conducted among members of the Spanish Society of Digestive Diseases (SEPD). The questionnaire explored five domains: MASLD knowledge, use of non-invasive biomarkers and imaging, awareness and implementation of clinical guidelines, cardiometabolic and alcohol-related risk assessment, and coordination with primary care. Results: A total of 429 specialists responded, 33.1% reported more than 20 years of practice and most worked in public hospitals, including 29.2% in large tertiary centers. Awareness of the MASLD definition was high, and 91.2% identified fibrosis as the main prognostic determinant. Non-invasive fibrosis biomarkers were widely used, whereas steatosis biomarkers were less frequently applied. Elastography was available to 96.1%. Guideline knowledge was reported by 80.4%, although implementation was lower. Cardiovascular risk evaluation varied: 75.1% reported systematic screening. Alcohol consumption was usually assessed. Coordination with primary care was limited: 91.1% expressed concerns regarding physicians’ familiarity with MASLD classification, and only 31.1% reported shared protocols. Conclusions: Spanish gastroenterologists show high awareness of MASLD and broad access to non-invasive diagnostic tools. However, important gaps remain in cardiovascular and alcohol risk assessment, guideline implementation, and coordination with primary care. Full article

This paper proposes a robust navigation method based on a robust square-root cubature Kalman filter (RSRCKF) to address the accuracy divergence of integrated navigation systems caused by drilling-induced slippage and the mismatch between the tail-cable encoder and the robot motion during operations of a seafloor drilling robot in deep-sea soft sedimentary layers. Considering the large-deformation mechanical characteristics of the seabed under drilling conditions, a unified state-space model incorporating a time-varying odometer scale-factor error is first established. To alleviate the numerical instability of the nonlinear system in the presence of non-Gaussian noise, a square-root cubature Kalman filter (SRCKF) framework is employed, in which the positive definiteness of the error covariance matrix is dynamically preserved via QR decomposition. Subsequently, an online fault detection mechanism based on a modified chi-square test is developed. By introducing a two-segment IGG (a classical robust weighting scheme) weighting function, an adaptive variance inflation factor is constructed to enable real-time identification and down-weighting of abnormal observations induced by slippage. Field experiments, including drilling and turning tests conducted on tidal mudflats off the coast of Zhoushan, demonstrate that the proposed method can effectively mitigate the impact of “false displacement” disturbances caused by typical soft clay slippage conditions through enhanced statistical robustness. Taking the conventional SINS/OD integration scheme as the baseline, the proposed method achieves an approximate 82.4% reduction in positioning error. These results verify the robustness and engineering applicability of the proposed algorithm in complex seabed environments. Full article

Background: Long COVID presents a novel and emerging public health challenge. As the first point of contact, general practitioners (GPs) play a key role in diagnosing and coordinating the care of patients presenting with post-acute sequelae of COVID-19 (PASC), despite a lack of experience. This study aimed to identify the main difficulties encountered by GPs in Franche-Comté, France, in managing adult outpatients with long COVID. Methods: We conducted a cross-sectional survey using an anonymous online questionnaire, which contained 21 questions and was distributed to GPs in Franche-Comté, France. The survey assessed definition, diagnostic and therapeutic challenges in managing long COVID. Results: Among the 410 questionnaires distributed, 90 general practitioners (GPs) responded (response rate: 21.9%). The mean age of participants was 34 ± 10 years, and 64.4% were women (n = 58). Regarding knowledge of long COVID, three participants (3.3%) did not recognize it as a distinct clinical entity, while more than half (58.9%, n = 53) reported insufficient knowledge. The main challenges identified were therapeutic management (76.7%, n = 69) and diagnosis (75.6%, n = 68). Only 4.5% of respondents (n = 4) reported no difficulty in defining post-acute sequelae of SARS-CoV-2 infection (PASC). The most frequently reported diagnostic difficulty was distinguishing long COVID from differential diagnoses (93.3%, n = 83/89), particularly fibromyalgia (94.3%, n = 83/88). Only 37.1% of participants (n = 33/89) reported actively following up patients with PASC. During initial management, the main challenge was the difficulty in objectively assessing patients’ complaints using available diagnostic tools (80.7%, n = 67/83). Additionally, a large majority of GPs reported difficulties in addressing patients’ questions (86.7%, n = 72/83) and managing associated anxiety disorders (75.9%, n = 63/83). Conclusions: These findings highlight the immediate need to enhance GP training in Franche-Comté, France, in dealing with long COVID. Improvements such as harmonizing long COVID definitions, testing diagnoses, and strengthening interdisciplinary coordination are essential to provide coherent and patient-centered care for this disease. Full article

Student attrition remains a persistent challenge in higher education and is shaped by interacting socioeconomic, academic, institutional, and wellbeing-related mechanisms. Although learning analytics and educational data mining increasingly support early-warning and intervention workflows, dataset reuse is often limited by incomplete documentation and inconsistent variable definitions. This Data Descriptor presents a structured cross-sectional survey dataset on factors influencing student persistence at a Colombian public university campus (La Paz). Data were collected between August and December 2025 through an online questionnaire and subsequently cleaned to remove duplicate entries and personally identifiable information. The released dataset contains 333 student records and 33 variables covering demographics (e.g., age, gender, first-generation status), socioeconomic conditions (e.g., residential stratum, housing, financial aid), academic experience and satisfaction (multiple 1–5 Likert items), perceived dropout intention across personal/socioeconomic/academic domains, thematically coded open-ended items describing challenges and motives, and a self-allocation of 0–100 weights across three dropout-factor domains. We provide a machine-readable codebook, a transparent preprocessing description, and technical validation checks (value ranges, category consistency, and composite-score integrity). The dataset is intended to support reproducible retention research, equity-oriented analyses, and benchmarking of predictive models, while encouraging responsible reuse through privacy-preserving release practices and FAIR-aligned metadata, repository deposition, and versioning. Full article