Search Results (249)

Introduction: Compassion is defined as the emotional response that arises when an individual perceives another’s suffering and is motivated to alleviate it. Purpose: To explore levels of self-compassion among nurses working in pediatric hospitals and examine their associations with nurses’ characteristics. Materials and Methods: This cross-sectional study included a convenience sample of 208 nurses from a public pediatric hospital. Data were collected through interviews using the Neff Self-Compassion Scale (SCS) which includes the following subscales: Self-Kindness, Common Humanity, Mindfulness, Self-Judgment, Isolation, and Over-Identification. The Greek-validated version of the instrument was used with acceptable internal consistency in the present sample (Cronbach’s alpha = 0.849). Data analysis included descriptive statistics and inferential tests (non-parametric comparisons and multiple linear regression), with statistical significance defined as p < 0.05. Results: The mean total Self-Compassion score was 83.24 ± 12.6 (range: 26–130). Regarding family-related factors, total Self-Compassion (p = 0.029), Common Humanity (p = 0.033), and Over-Identification (p = 0.041) were associated with the number of children. In relation to age, Self-Kindness (p = 0.033), Isolation (p = 0.005), and Over-Identification (p = 0.005) showed significant associations. Professional factors were also relevant, as Isolation was associated with total years of nursing experience (p = 0.032) and choice of nursing as a profession (p = 0.004), while Over-Identification was associated with years of experience in pediatric settings (p = 0.004) and choice of nursing as a profession (p = 0.049). Additionally, marital status was associated with Over-Identification (p = 0.045). Conclusions: Demographic and professional characteristics appear to influence the expression of Self-compassion. Healthcare organizations should implement targeted training programs to individualize professional development. Future research should explore work-related and personal factors influencing self-compassion to improve care quality and outcomes. Full article

This study examines how assurance level and assurer type jointly influence non-professional investors’ reactions to sustainability disclosures in an emerging market context. It employs a controlled 2 × 2 mixed-design experiment that manipulates assurance level (limited vs. reasonable) and assurer type (audit firm vs. non-audit firm). Data were collected from MBA and DBA students in Egypt as proxies for non-professional investors. Investor reaction is captured through multiple measures, including perceived sustainability performance, reliance on sustainability information, investment intention, stock valuation, and decision confidence. Non-parametric statistical techniques are used to test hypotheses, complemented by exploratory machine learning using SHAP values. The results provide strong and consistent evidence that the assurance level is the dominant factor shaping investor reactions. Reasonable assurance significantly enhances investor judgments across all key measures, whereas the type of assurer does not have a statistically significant independent effect. Additional analyses reveal that reasonable assurance from a non-audit firm elicits more favorable reactions than limited assurance from an audit firm, underscoring the primacy of assurance strength over provider identity. Exploratory findings further indicate that assurance influences investment decisions primarily through perceived sustainability performance and reliance on information. This study contributes to the literature by clarifying the relative roles of assurance level and assurer type and providing novel evidence from an emerging market setting (i.e., Egypt). The findings offer important implications for firms, assurance providers, and regulators seeking to enhance the credibility and decision usefulness of sustainability reporting. Full article

As generative AI expands the technical frontiers of prediction, measurement, and design, a growing tension has emerged between algorithmic fluency and institutional trust. This conceptual article offers a narrative synthesis of recent work in learning analytics, educational data science, human–AI interaction, and AI governance to propose stewardship as a necessary fourth paradigm of educational data science. Stewardship represents the professional, epistemic, and institutional work of governing judgment in an environment where analytic systems are increasingly generative and persuasive. Rather than treating stewardship as a general ethics checklist, the article positions it as the governance of epistemic and pedagogical authority: who determines what counts as evidence, interpretation, and educational action when AI systems help produce those judgments. The synthesis suggests that while GenAI can support bounded analytic tasks, evidence for systemic educational transformation remains limited and uneven. The field’s primary challenge is therefore not technical performance alone, but the governance of interpretation, validation, delegation, and action. By centering provenance, uncertainty, accountable oversight, learner agency, and institutional learning, stewardship provides an actionable framework for anchoring analytic innovation in responsible educational improvement. Full article

Teacher professional development (TPD) is increasingly mediated by online platforms, yet emotion analysis in this context remains underdeveloped because teachers’ professional discourse is often reflective, evaluative, and shaped by professional norms. To address this challenge, this study proposes a fine-grained, low-intrusion affective-appraisal analysis framework for TPD-oriented online professional learning that integrates textual evidence with platform interaction logs. The framework retains pleasure, arousal, and dominance from the pleasure–arousal–dominance (PAD) model and introduces utility as an appraisal-related dimension, capturing teachers’ perceived usefulness, value judgment, and professional learning gain. Methodologically, it combines textual representations based on Bidirectional Encoder Representations from Transformers (BERT), intra-week long short-term memory (LSTM) aggregation, interpretable behavioral-log features, and feature-level fusion. Data were collected from an authentic TPD-oriented online course involving 107 pre-service teachers, yielding 1276 teacher-week samples from 4300 texts and 264,028 interaction records. Results show that intra-week sequential modeling improves the macro-averaged F1 score (Macro-F1) over both the term frequency–inverse document frequency plus support vector machine (TF-IDF+SVM) baseline and BERT-based weekly text concatenation, with statistically significant gains over the non-sequential BERT-concat model across all four dimensions. Adding interaction logs improves accuracy across all dimensions and provides complementary process-based evidence, especially for arousal and utility. By linking a four-dimensional affective-appraisal framework with text-log fusion, this study offers a scalable and context-sensitive approach to affective-appraisal analytics in pre-service teacher professional learning. Full article

Objectives: This study aims to assess the acceptance of vaccinations among pregnant women, particularly against influenza, pertussis, COVID-19, and RSV, and to identify factors influencing their willingness to get vaccinated. It also seeks to evaluate the impact of the COVID-19 pandemic on maternal attitudes and behaviors regarding vaccination. Methods: The analysis involved a review of existing literature and studies to evaluate the level of vaccine acceptance among pregnant women before and after the COVID-19 pandemic. Factors contributing to vaccine hesitancy, including misinformation, lack of knowledge, and the influence of healthcare professionals, were examined. Results: The findings indicated that, despite scientific evidence supporting the safety and efficacy of vaccines during pregnancy, public concerns remain about their impact on the developing fetus. The outbreak of the COVID-19 pandemic has increased awareness of the risk of infectious diseases, but at the same time, its impact on vaccination rates among pregnant women is ambiguous and geographically diverse. Misinformation and decreased access to healthcare during the pandemic negatively affected vaccine uptake. Trustworthy information provided by healthcare professionals emerged as a key factor in promoting vaccine acceptance. Conclusions: To improve vaccination rates among pregnant women, it is essential to provide clear, evidence-based information through healthcare professionals, particularly those directly caring for pregnant women. Educational campaigns should address concerns calmly and without judgment, emphasizing the safety and benefits of vaccinations. Enhanced access to healthcare and vaccinations, along with strategic information dissemination, can significantly improve vaccine acceptance during pregnancy. Lessons learned from past pandemics should be incorporated into the development of healthcare strategies aimed at implementing recommended vaccinations for pregnant women in the future. Full article

The pervasive integration of artificial intelligence across e-commerce ecosystems has fundamentally transformed the competitive landscape, rendering systematic and reproducible platform evaluation frameworks an operational necessity rather than an academic exercise. Conventional multi-criteria decision analysis approaches for e-commerce evaluation remain structurally constrained by their dependency on human expert panels, which introduce recruitment costs, cognitive biases, limited reproducibility, and the practical infeasibility of assembling genuinely multidisciplinary panels spanning e-commerce strategy, machine learning engineering, and financial technology simultaneously. This study proposes a novel decision support framework that integrates Large Language Model (LLM) multi-agent simulation with the Best–Worst Method (BWM) to derive reproducible priority weights for AI-driven e-commerce platform evaluation within a rigorous business intelligence architecture. Twelve domain-differentiated LLM agents—organized into three expertise groups representing e-commerce management, AI and machine learning technology, and digital payment systems—were instantiated with structured system prompts encoding professional domain knowledge and deployed across three independent simulation rounds to perform BWM pairwise comparisons across a comprehensive six-dimensional, 30-sub-criterion evaluation hierarchy. Inter-agent consensus was synthesized through geometric mean aggregation, with consistency verification conducted via BWM’s xi* indicator and inter-round stability assessed through coefficient of variation analysis. Results reveal that Transaction Security and Trust achieves the highest dimension-level weight (w = 0.248), followed by AI Recommendation Effectiveness (w = 0.213), with Personal Data Protection (G = 0.0750), Recommendation Accuracy (G = 0.0607), and Transaction Transparency (G = 0.0549) emerging as the three highest globally ranked sub-criteria. The aggregated consistency indicator xi* = 0.062 confirms logical coherence of the multi-agent judgment consensus, and all dimension weights exhibit CV values below 2.8%, demonstrating exceptional inter-round stability. Spearman rank correlations among the three domain-expertise groups exceed 0.92, confirming strong inter-group convergence. Sensitivity analysis under perturbations of ±10% and ±20% demonstrates that the top-five priority indicators are structurally stable. This study establishes LLM multi-agent BWM simulation as a methodologically rigorous, institutionally accessible, and computationally reproducible alternative to traditional expert elicitation for complex platform evaluation tasks. Full article

Artificial Intelligence (AI) is rapidly transitioning from a specialized technology to an everyday socio-technical infrastructure, yet public acceptance remains shaped by a trade-off between perceived benefits and risks. This study examines how individuals from varied demographic and professional backgrounds perceive, use, and evaluate AI-enabled systems using a mixed-method research design. A bilingual (English/Arabic) online survey ($N=115$) captured demographics, awareness, usage patterns, perceived impact, self-assessed understanding, domain-specific trust, concerns, and attitudes toward regulation, complemented by open-ended reflections. In parallel, semi-structured face-to-face interviews provided deeper insight into AI conceptualization, lived experiences, trust boundaries, and conditions for acceptable use. Quantitative results show frequent AI engagement embedded in daily life, with strong domain dependence in trust: education is the most trusted domain, whereas healthcare and finance attract substantially lower trust. Prominent concerns include overreliance (“brain rot”), privacy and data misuse, job displacement, and misinformation. Support for stronger AI regulation is high, indicating that governance is viewed as a prerequisite for sustainable adoption rather than a constraint on innovation. Qualitative findings triangulate these results, revealing a pattern of conditional acceptanceunderstood as the simultaneous valuation of AI’s practical utility alongside the imposition of explicit trust prerequisites whereby participants value AI for productivity and learning support while emphasizing confidentiality, transparency, human oversight in high-stakes contexts, and clear boundaries to mitigate misuse and erosion of human judgment. The study offers empirically grounded insights for policymakers, educators, and industry stakeholders into how AI acceptance is negotiated through utility, literacy, perceived risk, and expectations of accountability. Full article

Evidence-informed leadership in higher education has gained increasing prominence across Europe, responding to growing demands for accountability, transparency, and innovation in policy and practice. This paper critically reviews the conceptual foundations, mechanisms, and implications of evidence use in higher education leadership, drawing on the European and international literature. It examines both the potential of evidence to enhance decision-making and the persistent challenges that limit its effective integration into leadership practices. Despite the expansion of data systems and the growing use of analytics, the translation of evidence into meaningful leadership action remains uneven. Key barriers include fragmented data infrastructures, limited data literacy among leaders, tensions between managerial metrics and academic values, and resistance to externally imposed performance frameworks. Emerging developments, particularly the rise in artificial intelligence and algorithmic decision-making, further complicate the landscape by raising concerns about transparency, bias, and ethical responsibility. The paper argues for a more reflexive and context-sensitive approach to evidence-informed leadership. It highlights the need to move beyond technocratic models towards practices that value professional judgment, stakeholder engagement, and diverse forms of knowledge. Such an approach is essential for fostering ethically grounded and sustainable leadership capable of supporting transformative change in European higher education. Full article

Background: Digital health technologies are increasingly integral to healthcare delivery worldwide; however, successful adoption depends on more than technological availability. In nursing, readiness is particularly important because digital systems increasingly shape documentation, communication, decision support, and care delivery. Within the context of Saudi Arabia’s healthcare transformation, doctoral nursing students are positioned as future educators, clinicians, and leaders whose perceptions can provide insight into digital health readiness and preparation. Aim: This study aimed to explore doctoral nursing students’ perceptions of their readiness to adopt digital health technologies in Saudi Arabia, guided by the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). Methods: This exploratory, qualitative, descriptive study recruited 9 doctoral nursing students from a public university in Saudi Arabia using purposive sampling based on predefined eligibility criteria. Individual semi-structured interviews were conducted online and audio-recorded. Data were analyzed using a hybrid inductive–deductive thematic approach. UTAUT2 informed the deductive component of the analysis, while inductive coding and cross-case comparison supported theme generation. Results: Four interrelated themes were identified. First, readiness was positive but conditional, shaped by movement from openness to professional necessity, familiarity, workflow fit, and caution about the possible weakening of foundational or manual competence. Second, adoption depended on practical value and system credibility, including access, convenience, efficiency, safety, documentation integrity, accuracy, privacy, and reliability. Third, adoption was organizationally mediated through leadership, peer culture, infrastructure, implementation conditions, training, follow-up, and academic preparation. Fourth, digital health was understood as supporting, not substituting for, nursing work by reducing avoidable burden and creating more space for direct care while preserving human presence, communication, and clinical judgment. Conclusions: In this sample of doctoral nursing students, digital health readiness was positive but conditional. The findings suggest that readiness reflects a context-sensitive professional judgment shaped by educational preparation, organizational support, system credibility, workflow compatibility, and the perceived ability of digital technologies to enhance nursing work rather than replace it. Implications: The findings suggest that nursing education and practice should strengthen applied digital health competencies through simulation-based preparation, electronic documentation training, privacy and ethics education, workflow-aligned implementation, and sustained organizational support. Full article

Euthanasia in companion animal practice represents one of the most emotionally and ethically challenging decisions in veterinary medicine, requiring clinical judgment, effective communication, and sensitivity toward both animal welfare and pet owner well-being. This mixed-methods exploratory study investigated decision-making in small animal euthanasia from both pet owner and veterinarian perspectives. An online questionnaire completed by 228 pet owners from 17 countries was supplemented by two semi-structured interviews involving small animal veterinarians with different professional backgrounds. The qualitative interview component was exploratory and was used to contextualize the survey findings. Quantitative data were analyzed using descriptive and inferential statistics, while qualitative responses were examined thematically. Illness (63.3%) and age-related decline (31.6%) were the most frequently reported reasons for euthanasia. Most pet owners assessed their animal’s condition through personal observation (53.5%) or veterinary advice (39.2%), whereas structured quality-of-life tools were rarely used (7.3%). Emotional attachment represented the most influential factor in decision-making (69.3%). Pet owners who explicitly reported receiving emotional support from their veterinarian experienced significantly lower emotional burden (Holm-adjusted p = 0.002) and greater satisfaction with communication (Holm-adjusted p = 0.006) than those who explicitly reported no emotional support. Interview findings emphasized medical justification, individualized communication, and ethical responsibility. These findings highlight the central role of communication, emotional support, and structured end-of-life guidance in improving companion animal euthanasia decision-making. Because the study relied on voluntary online recruitment and included a limited qualitative sample, the findings should be interpreted as exploratory. Full article

Introduction: Neurosurgery has evolved from an anatomy-driven analog discipline into a digitally augmented field supported by multimodal imaging, neuronavigation, intraoperative imaging, neurophysiological monitoring, robotics, augmented reality, and artificial intelligence. Objective: To examine how this transition has altered professional responsibility, informed consent, training, and medico-legal accountability in neurosurgical practice. Methods: We performed a structured narrative review of the literature on digital neurosurgery and its ethical and professional implications, focusing on publications from 1990 onward and supplemented by landmark historical papers. Sources were selected for relevance to cranial, spinal, skull base, stereotactic, and neuro-oncological neurosurgery, and then synthesized into thematic domains including brain shift, eloquent cortex preservation, stereotactic accuracy, intraoperative neurophysiology, workflow integration, equity, and liability. Results: Digital systems improve lesion localization, function-preserving surgery, stereotactic precision, documentation, and training, but they also introduce new vulnerabilities related to registration error, brain shift, platform dependence, data overload, cost, cybersecurity, deskilling, and diffuse accountability. Conclusions: Digital augmentation expands rather than diminishes the neurosurgeon’s responsibility. The neurosurgeon remains accountable for surgical indication, interpretation of technology-generated information, intraoperative override, and communication of technology-specific risks. The central ethical challenge is to integrate digital tools without weakening patient-centered judgment. Full article

The study examines judicial practice regarding claims for damages and non-pecuniary damages (hereinafter: NPDs) arising from violations of the duty to inform in healthcare by comparing two periods (2008–2010 and 2018–2020) in the context of patient self-determination and European trends in patient rights. The 193 final judgments selected from the Wolters Kluwer Law Database based on keyword searches underwent qualitative content analysis and quantitative processing using SPSS (Statistical Package for the Social Sciences, SPSS version 25.0). A selection criterion was that the judgment should assess on its merits whether the duty to inform had been fulfilled or violated. The real value of the adjudged compensation was compared and normalized in relation to the minimum wage (multiplied by the minimum wage) in order to reveal the actual socio-economic weight of the compensation. The results show that while in 2008–2010, the lack of information was mostly considered an additional element of professional negligence, by 2018–2020, it was recognized as a separate violation of personality rights that infringed on the right to self-determination, and the rate of complete rejection of claims for NPDs decreased. However, the increase in nominal amounts was accompanied only to a limited extent by an increase in the real value of compensation. The findings suggest that Hungarian judicial practice is moving closer to the autonomy-centred European approach, while strengthening the reparative function of NPDs—ensuring compensation that is perceptible in real terms—remains an open task. Full article

Background/Objectives: Reducing antibiotic use in the dairy sector is a critical objective. This study aimed to assess whether Danish farmers were able to achieve this objective appropriately without affecting animal health. Methods: Participants chose from three options—antibiotics (ABs), no treatment (NO), and non-steroidal anti-inflammatory drugs (NSAIDs)—and their decisions were compared against a laboratory-defined ‘gold standard’. Results: The results indicated that farmers’ professional judgment was unreliable; they frequently misallocated antibiotic treatments, often failing to treat cows that needed them while treating those that did not. Overall, producers administered significantly fewer antibiotics than laboratory results suggested were necessary. Although veterinarians achieved better results via in-clinic bacteriological examinations, their ability to identify specific bacterial species was insufficient. Consequently, cows remain at a significant risk of persistent infection following a clinical episode, regardless of the farmer’s chosen treatment. Conclusions: The study concludes that significant progress needs to be made both on farms and in veterinary clinics to ensure that the use of antibiotics is as little as possible, but as much as necessary. Until such reliability improves, unsuitable treatment will continue to compromise animal health. Full article

Background/Objectives: Nutritional management is a core component of care for patients with chronic kidney disease (CKD), and nephrology nurses play a key role in education and clinical monitoring. However, how nurses develop and enact nutritional competence in daily practice remains insufficiently explored. This study aimed to explore nephrology nurses’ perceptions and experiences of nutritional management in CKD care. Methods: A qualitative descriptive study was conducted through semi-structured interviews with 22 nephrology nurses. Data were analyzed using thematic analysis according to Braun and Clarke. Methodological rigor was ensured following trustworthiness criteria, and reporting adhered to the Consolidated Criteria for Reporting Qualitative Research (COREQ) guidelines. Results: The thematic analysis of the interviews identified six main themes: (1) Professional identity and nutritional competence, largely developed through clinical experience rather than structured education. (2) Interprofessional collaboration, perceived as essential but inconsistently implemented. (3) Nutritional education in practice, embedded in daily care and tailored to individual needs. (4) Experiential learning through self-directed nutrition updating. (5) Patient-related challenges, including adherence issues, generational differences, and cultural/educational barriers. (6) Nutritional assessment and decision-making, grounded in routine clinical monitoring and personalized judgment. Participants also highlighted the potential of decision-support tools to enhance personalized nutritional management. Conclusions: Strengthening structured nutritional training, improving interprofessional integration, and implementing shared protocols may enhance the consistency, quality, and safety of nutritional care for patients with CKD, supporting more effective translation of evidence into clinical practice. Full article

This conceptual and analytical study examines how digitalization may reduce the cost and complexity of ESG and circular economy reporting for women-led SMEs within the evolving EU sustainability reporting framework. Particular attention is given to selected contextual examples from the Danube Region. Using a conceptual accounting approach grounded in EU regulatory documents, the academic literature, and prior bibliometric research, it identifies four key challenge domains: measurement, valuation, disclosure, and professional judgment. The analysis is complemented by an exploratory public data illustration based on publicly available documents and illustrative cases of women-led SMEs from the Danube Region. The empirical illustration is intended solely to contextualize and demonstrate the practical visibility of the proposed accounting domains rather than to validate the conceptual framework statistically. It develops an accounting-oriented problem matrix linking these challenges to digital enablers such as data platforms, automation tools, and traceability technologies. The findings suggest that digital accounting capabilities may support more efficient, reliable, comparable, and scalable ESG reporting. A conceptual framework is proposed, connecting regulatory drivers, digital accounting capabilities, and reporting outcomes, including enhanced assurance readiness and potentially improved access to finance. The study also outlines practical recommendations, including minimum viable ESG datasets and a staged digital adoption approach, alongside policy implications related to harmonized data requests and targeted capacity-building for SMEs. The study contributes to the literature by integrating ESG reporting, circular economy, digitalization, and gender-related constraints affecting women-led SMEs within an explicitly accounting-centered analytical framework. Full article