Search Results (704)

Individuals with serious mental illness (SMI) face profound and largely preventable physical health inequities shaped by social and structural conditions, representing a major public health concern related to avoidable health inequalities. Because many receive everyday support in community mental health (CMH) systems, these services represent a crucial arena for understanding how such inequities are encountered and made sense of in practice. The study examines how physical health is understood within German CMH practice. Five group discussions with 30 CMH workers were analysed using an interpretive qualitative approach. The analysis identified five professional logics through which physical health becomes part of CMH support: trusting relationships that both enable and limit action; psychological stability as a core mandate; physical health positioned between recognition and delegation; fragile motivation combined with an ethics of restraint; and health promotion situated between aspiration and structural constraint. The findings show that helping relationships, everyday environments, and organisational structures create specific conditions for health-related support. Strengthening these interconnected levels may enable CMH to integrate physical health more systematically, offering insights relevant to international CMH contexts facing similar relational and structural challenges. Full article

Artificial intelligence (AI) is increasingly transforming talent acquisition and workforce analytics, raising both efficiency opportunities and ethical concerns. This study aims to map the intellectual structure and evolution of AI-enabled recruitment research with a focus on ethical and data-driven approaches. A bibliometric analysis was conducted on 1893 Scopus-indexed journal articles published between 2014 and 2025 using VOSviewer. The results reveal rapid growth in the field, dominant thematic clusters spanning machine learning applications, HR analytics, and ethical governance, and strong international collaboration led by the United States, China, and the United Kingdom. Findings also highlight the increasing prominence of fairness, transparency, and explainability within AI recruitment research. The study concludes by identifying research gaps and proposing future directions for integrating ethical AI frameworks with workforce analytics to support responsible talent acquisition. Full article

Bioethics education has become established as an essential component for addressing the ethical challenges associated with biomedical development, biotechnology, and decision-making in the healthcare field. Although numerous studies have analyzed the teaching of bioethics among medical students and other health professions, empirical research on bioethics literacy in religious formation contexts remains limited. The objective of this study was to evaluate the level of bioethical knowledge (here operationalized as bioethics literacy) among Catholic seminarians in Colombia and to explore the psychometric properties of a questionnaire designed to measure bioethics literacy in this population. A cross-sectional observational study was conducted through the administration of a structured questionnaire consisting of 32 multiple-choice items with a single correct answer addressing philosophical foundations, personalist bioethics, bioethical principles, clinical bioethics, and issues related to biotechnology. A total of 216 complete questionnaires were analyzed using descriptive statistics and exploratory psychometric analyses, including item difficulty and discrimination, internal consistency, and exploratory factor analysis. The results showed a moderate overall level of bioethics literacy, with better performance in applied domains such as clinical bioethics and bioethical principles, and lower levels of correct responses in philosophical foundations and personalist bioethics. The questionnaire showed moderate internal consistency and a preliminary factorial structure, suggesting its usefulness as an exploratory tool for assessing bioethical knowledge in seminary educational contexts. These results highlight the importance of strengthening the integration between philosophical and theological education and the applied analysis of bioethical problems in seminary educational programs. Full article

Generative artificial intelligence in higher education is typically framed as either a student productivity tool or an institutional disruption. This agenda-setting essay advances a third position: mobile generative AI functions as relational infrastructure—a persistent communicative presence that mediates identity, meaning-making, and belonging during institutional transition. Focusing on international graduate student onboarding, we abductively “think through” two complementary theoretical lenses. Constitutive Artificial Intelligence Identity Theory (CAIIT) conceptualizes AI as a co-constitutive participant in identity formation through recursive communicative feedback loops. Language Convergence/Meaning Divergence (LC/MD) theory explains how shared institutional language masks interpretive gaps across intercultural and bureaucratic contexts. Reading narrative vignettes through these frameworks, we argue that generative AI is neither simple curricular tool nor personal aid, but both relational and organizational infrastructure, redistributing translational, emotional, and interpretive labor in higher education. We outline four design principles for AI-integrated onboarding: distinguish communicative scaffolding from cognitive replacement; design systems that assume meaning divergence; center equity in AI-mediated transitions; and anticipate ethical risk. Reframing AI as relational infrastructure shifts AI-in-education research toward relational accountability and institutional care. Full article

Man-made climate change is empirically proven and places ethical and strategic responsibility on the current generation to mitigate risks for future generations. Within this context, the selection of future energy carriers is a central determinant of sustainable development. While electrification is widely promoted, particularly in the transport sector, it is associated with complex production chains, critical raw material dependencies, unresolved recycling challenges, and potential resource scarcity. Synthetic fuels therefore re-emerge as a potential complementary option, especially for applications that are difficult to electrify directly. However, their role remains controversial due to efficiency losses and cost challenges. This paper uses qualitative research based on expert interviews to investigate the role of synthetic fuels in the sustainable management of energy transition and responsible practices. A total of 11 experts, representing the energy sector, research institutions, engineering fields, environmental organizations, and political–regulatory contexts participated. The analysis focused on four dimensions—efficiency, awareness, knowledge, and acceptance. The findings have shown that synthetic fuels are not a universal substitute for fossil fuels but a highly conditional option for hard-to-electrify applications. Efficiency losses, limited renewable electricity availability, knowledge gaps, conceptual ambiguity, and acceptance challenges significantly constrain their systemic role. The paper concludes that synthetic fuels can only make a meaningful contribution under strict conditions, with clear prioritization, realistic expectations, and coherent long-term policy frameworks aligned with intergenerational responsibility and genuine sustainability. The findings should be interpreted primarily within the German and European policy and innovation context, as the expert sample is largely embedded in institutions operating in this environment. Nevertheless, the insights provide relevant indications for broader international debates on the role of synthetic fuels in energy transition. Full article

This article re-examines Islamic ethics of war and peace by returning to the formative Meccan–Medinan trajectory of the Prophet Muḥammad’s life, where early Islamic moral reasoning developed amid persecution, migration, diplomacy, and armed conflict. Contemporary debates frequently portray Islam either as a tradition that sacralizes violence through jihad or as one that reduces peace to purely inward spirituality. Both perspectives obscure the historically grounded ethical discourse that emerged within the early Muslim community. This study argues that the Qurʾān—understood within the Islamic tradition as the authoritative source of ethical guidance—together with prophetic practice articulated a coherent moral framework governing the use of force, the pursuit of peace, and the restoration of social order after conflict. Drawing on Qurʾānic discourse, canonical ḥadīth, classical tafsīr and sīrah literature, and modern scholarship in Islamic studies, religious ethics, and conflict resolution theory, the article reconstructs how early Islamic sources represent the ethical regulation of violence. The analysis identifies a threefold trajectory in prophetic practice: a Meccan phase characterized by nonviolent endurance and moral witness under persecution; a Medinan phase marked by constitutional governance, plural coexistence, and tightly regulated defensive warfare; and a culminating ethic of negotiated peace and post-conflict reconciliation exemplified in the Treaty of Ḥudaybiyyah and the Conquest of Mecca. Taken together, these stages reveal an integrated moral vision in which force is neither celebrated nor treated as a default instrument of political expansion, but permitted only under strict ethical constraints shaped by justice (ʿadl), mercy (raḥma), proportionality, and the protection of communal life. By reconstructing this early prophetic framework, the article demonstrates that Islamic sources contain significant internal resources for limiting violence, regulating warfare, and prioritizing reconciliation. In doing so, it contributes to contemporary scholarship on Islamic ethics and situates the prophetic model within broader global debates on the moral regulation of war, peacebuilding, and post-conflict justice. Full article

Background/Objectives: Antimicrobial resistance (AMR) remains a major global health threat, strengthening the case for antimicrobial stewardship strategies that limit unnecessary broad-spectrum empiric therapy while preserving timely escalation when clinically warranted. Before any clinical deployment of large language model (LLM)-based antibiotic decision support can be considered, structured offline evaluation is needed to assess whether model outputs align with auditable stewardship constraints under real-world admission contexts. We therefore evaluated whether post hoc LLM-generated empiric antibiotic recommendations showed greater concordance with a pre-specified stewardship benchmarking framework than clinician-initiated regimens in a retrospective shadow-mode setting. Methods: Single-center retrospective paired evaluation at Clinical Emergency Hospital of Bucharest (Internal Medicine, 2020–2024). The unit of analysis was the admission (N = 493), with paired 24 h empiric regimens (clinician-prescribed vs. post hoc LLM-recommended via OpenAI API; not visible to clinicians; no influence on care). Local laboratory-derived epidemiology was precomputed from microbiology exports and provided as structured prompt context to approximate information parity with clinicians’ implicit local ecology knowledge. Primary (prespecified) endpoint: any contextual guardrail violation (unjustified carbapenem/antipseudomonal/anti-MRSA under prespecified structured severity/MDR-risk rules), exact McNemar. Key secondary (prespecified): Δ contextual guardrail penalty (LLM − Clin), sign test and Wilcoxon signed-rank (ties reported). Ethics committee approval was obtained. Results: Guardrail violations occurred in 17.0% of clinician regimens vs. 4.9% of LLM regimens (paired RD −12.2%; matched OR 0.216, 95% CI 0.127–0.367; McNemar exact p = 1.60 × 10⁻¹⁰). Δ penalty had median 0 with 398/493 ties; among non-ties, improvements (Δ < 0) exceeded adverse shifts (79 vs. 16; sign-test p = 3.47 × 10⁻¹¹). Conclusions: In this offline, non-interventional paired evaluation, LLM-generated empiric regimens showed greater concordance with a pre-specified stewardship benchmarking framework than clinician empiric regimens for the same admissions. These findings should not be interpreted as evidence of clinical superiority, patient safety, or causal effectiveness, but rather as process-level benchmarking within a rule-based stewardship construct. As such, reproducible guardrail-based benchmarking may serve as an early pre-implementation step to identify alignment and potential failure modes before prospective, safety-governed evaluation. Full article

The accelerated expansion of artificial intelligence (AI) is reshaping economic systems, labour markets and democratic life, giving rise to hybrid human–AI societies. In this context, education becomes a strategic arena for enabling sustainable and socially just transitions within the Fourth Industrial Revolution. This article examines how digital competence can be reconceptualized to prepare future citizens and educators for these emerging societal configurations, with particular attention to the Ibero-American context. A conceptual framework is proposed that integrates algorithmic literacy, critical data awareness, AI ethics, human–AI collaboration skills, and civic and socio-emotional capacities as core dimensions of “next-decade” digital competence. Methodologically, the study combines three complementary approaches: (a) a structured review of interdisciplinary literature on AI, digital competence and sustainability; (b) an analysis of international and regional policy documents and competence frameworks relevant to Ibero-America; and (c) selected empirical insights drawn from the first author’s doctoral research on digital competence and AI use in teacher education. The findings reveal significant tensions between rapid AI adoption and persistent structural inequalities in the Global South, while identifying key leverage points for aligning teacher education, public policy and institutional strategies with the Sustainable Development Goals. The proposed framework aims to support policymakers, universities and international organizations in fostering inclusive and sustainable AI-driven social change while mitigating new forms of exclusion and dependency. Full article

Background: Artificial Intelligence (AI) and Generative AI (GenAI) are increasingly integrated into educational and professional settings, offering personalized learning, productivity gains, and enhanced engagement. However, excessive reliance may compromise critical thinking, autonomous problem-solving, and emotional regulation among youth (i.e., adolescents and young adults) and early-career professionals. Aim: This review examines the cognitive, educational, and behavioral impacts of AI and GenAI use in youth, highlighting implications for their responsible integration in learning and professional development. Methods: A narrative review was conducted, synthesizing empirical studies, psychometric instruments, and international policy frameworks addressing AI engagement. Emphasis was placed on cognitive, behavioral, educational, and ethical dimensions across youth and early-career professionals. Results: AI enhances learning efficiency, creativity, and professional decision-making but may also foster cognitive offloading, dependency, and addiction-like behaviors. Instruments such as the Conversational AI Dependence Scale (CAIDS) and the Problematic ChatGPT Use Scale (PCGUS) help identify maladaptive patterns. Effective strategies include structured pedagogy, human oversight, reflective practice, AI literacy, and ethical guidance. Paradoxically, higher AI competence and trust may increase reliance, underscoring the need for guided and balanced engagement. Conclusions: Responsible AI integration requires multidimensional approaches combining instructional scaffolding, metacognitive strategies, supervision, and governance to preserve autonomy, professional judgment, and cognitive development in youth. Full article

Background/Objectives: The integration of artificial intelligence (AI) into rehabilitation practice has expanded rapidly, including its emerging application in palliative care contexts. Although international organisations have established ethical and governance frameworks for AI in healthcare, these initiatives remain largely high-level and are not specifically tailored to the clinical complexity, vulnerability, and relational dimensions of palliative care rehabilitation. The absence of context-specific ethical–regulatory guidance poses challenges for responsible implementation in ethically sensitive settings. This study aimed to consolidate ethically grounded regulatory guidance for the use of AI in palliative care rehabilitation by translating existing international principles into context-sensitive domains. Methods: A qualitative documentary analysis with a normative ethical–regulatory orientation was conducted using the READ (Ready, Extract, Analyse, Distil) framework. Authoritative international policy, governance, and regulatory documents addressing AI in healthcare were identified and analysed. Data were extracted using a structured analytical table and coded according to predefined ethical–regulatory domains derived from previously published ethical guidelines and verified through documentary analysis. Results: The analysis identified five convergent ethical–regulatory domains recurrent across international governance frameworks: (1) Human oversight and clinical responsibility; (2) Patient autonomy, preferences, and proportionality; (3) Transparency and explainability; (4) Fairness, equity, and non-discrimination; and (5) Professional competence and ethical literacy. These domains were synthesised into practical ethical–regulatory considerations linking ethical principles with governance expectations and clinical implementation requirements. Conclusions: This study provides context-sensitive ethical–regulatory guidance that bridges high-level AI governance principles with the operational realities of palliative care rehabilitation. By systematising and operationalising existing ethical norms, the proposed framework supports responsible clinical decision-making, strengthens institutional accountability, and safeguards patient dignity and autonomy in vulnerable care contexts. Full article

Background: Artificial intelligence (AI) is poised to fundamentally reshape healthcare delivery, offering unprecedented advancements in diagnostics, treatment personalization, and operational efficiency. However, a growing body of international research reveals a critical “optimism–knowledge gap”: healthcare professionals are enthusiastic about AI’s potential but possess limited technical knowledge and practical experience. This gap compromises the safe and effective implementation of AI tools. The Italian healthcare context presents a unique and amplifying challenge, as it is defined by the stringent “human-in-the-loop” oversight mandated by the Garante per la protezione dei dati personali (Italy’s Data Protection Authority). This legal framework makes clinician competence not just a goal, but a prerequisite for regulatory compliance. Objective: This study aimed to provide an exploratory quantitative assessment of AI awareness, practical application, and understanding of its limitations among a sample of clinicians in Italy. It specifically sought to compare the preparedness of hospital-based clinicians and general practitioners (GPs) and to identify the workforce’s perceived educational needs within this unique legal environment. Methods: A descriptive, cross-sectional survey was conducted from February to August 2025. Using a non-probability convenience sampling method via professional networks, the survey yielded 362 total responses. Data were analyzed descriptively and inferentially using Chi-square (${\chi }^2$) tests to compare cohort responses on familiarity, practical exposure, knowledge of limitations, and interest in further training. Results: A universal and high demand for education was found, with 89.9% of all respondents being “Moderately” or “Very” interested in learning more about AI. This optimism coexists with dangerously low practical exposure. The gap was most profound among GPs, 44.1% of whom have “Never” used an AI tool—a rate significantly higher than hospital clinicians (34.9%; ${\chi }^2=3.14$, p = 0.045). Furthermore, 32.6% of GPs admitted that they “understand some benefits but not the limitations.” Conclusions: Italian clinicians mirror the global optimism–knowledge gap. These findings underscore the urgent need for structured, continuous education in AI literacy to address ethical and regulatory imperatives within the Italian healthcare system. Full article

Objective: This exploratory study presents an international, multi-stakeholder snapshot of perceptions regarding real-world data and real-world evidence in health technology assessment. The aim is to identify perceived opportunities, barriers, and enabling conditions rather than to generate generalizable conclusions. Methods: A 21-item, expert-validated questionnaire was distributed via LimeSurvey to diverse health technology assessment stakeholders, including academia, industry, health technology assessment agencies, healthcare providers, policymakers, patients, and payers. The survey explored perceptions of value, methodological and regulatory challenges, and future outlooks for RWD/RWE use in HTA. Ethical approval was obtained by the University of West Attica Ethics Committee, and pilot testing was conducted prior to dissemination. Data were analyzed using descriptive statistics, consistent with the study’s exploratory intent and acknowledging that results are preliminary and not statistically generalizable. Results: Thirty-two completed responses demonstrated preliminary stakeholder support for integrating real-world data and real-world evidence into health technology assessment. Respondents represented academia, industry, HTA agencies, healthcare providers, policymakers, and patient/advocacy groups; however, no payer responses were obtained. Respondents emphasized the value of real-world data in complementing clinical trials by capturing real-world effectiveness, patient diversity, and long-term outcomes, especially in rare diseases and cancer. Key challenges included poor data quality, confounding biases, and regulatory barriers. Stakeholders highlighted the importance of standardization, transparency, and international collaboration. Opportunities included better decision-making, personalized healthcare, and improved post-market monitoring, with strong calls for robust infrastructure, clear methodologies, patient involvement, and supportive health policy frameworks. Conclusions: Real-world data and evidence enhance health technology assessment by supporting better decisions and personalized care. However, issues like data quality, methods, and trust must be addressed through standardization, strong infrastructure, and collaboration to ensure effective and impactful implementation in healthcare, while acknowledging these insights are based on a small exploratory sample. Full article

The accelerated expansion of advanced technologies—particularly artificial intelligence, intelligent systems, and interactive digital environments—is influencing contemporary media ecosystems and contributing to changes in educational practices. This study provides a systematic and descriptive bibliometric mapping of recent scientific production on artificial intelligence in education, algorithmic ethics, and digital inequality. A total of 229 Scopus-indexed documents published between 2021 and 2026 were analyzed using Biblioshiny and VOSviewer to examine publication patterns, influential authors and sources, and the conceptual structure of the field. Results indicate a marked increase in research output since 2024, with an annual growth rate of 47.58%, an average of 8.68 citations per document, and an international co-authorship rate of 24.45%. These indicators reflect an expanding and increasingly collaborative research landscape, accompanied by a diversification of thematic priorities within the field. The analysis identifies five thematic clusters: (1) the technical foundations of AI and digital transformation; (2) intelligent and immersive learning environments; (3) personalized and adaptive learning systems; (4) AI literacy and pedagogical integration; and (5) ethical considerations, including algorithmic bias and educational robotics. The findings highlight the need for explicit justification of database selection, strengthened critical AI literacy, and context-sensitive strategies that address disparities in access, skills, and institutional capacity. Overall, this study offers a coherent overview of a research area that is currently expanding and undergoing conceptual reorganization, providing evidence-informed insights for future research, policy development, and the design of equitable AI-driven educational technologies. Full article

Artificial intelligence has transitioned from a peripheral innovation to a core infrastructure shaping higher education within a remarkably short period. While conceptual debates on AI ethics, pedagogy, and academic integrity are expanding, empirically grounded syntheses that consolidate global evidence remain limited. This study addresses this gap by providing an integrated cross-domain synthesis and statistically grounded overview of AI adoption, use, and governance across higher education systems. Using a secondary statistical synthesis methodology, the study aggregates large-scale quantitative data published between 2021 and 2025 from reputable international sources, including student and faculty surveys, institutional reports, research indices, and regulatory datasets. Results demonstrate near-universal student adoption of AI tools, rapid but uneven professional engagement among faculty and staff, a sharp rise in AI-related academic misconduct, accelerating impacts on research production and scientific workflows, and persistent gaps in institutional preparedness, policy development, and equity. The findings reveal a widening disconnect between bottom-up AI adoption and top-down governance mechanisms, particularly in assessment design, academic integrity frameworks, faculty capacity building, and quality assurance systems. Moreover, this paper argues that AI can no longer be treated as an optional educational technology and must instead be governed as a foundational component of higher education infrastructure. The study concludes by outlining evidence-based policy implications for institutions, regulators, and quality assurance agencies, emphasizing the need for coordinated, adaptive, and equity-oriented governance frameworks grounded in empirical realities rather than speculative narratives. Full article

Doctoral training in applied computing and information systems is the structured development of a researcher’s capacity to produce original, rigorous, and scholarship that is relevant to practice, supported through doctoral supervision, which provides academic guidance for research design decisions, progress management, scholarly quality, and researcher development. In this setting, Design Science Research (DSR) is a methodology that generates knowledge through the purposeful design and evaluation of an artifact intended to address a defined problem. In parallel, Action Research (AR) generates knowledge through collaborative, iterative cycles of planned action and critical reflection conducted with stakeholders in real settings. Bringing both traditions together, Action Design Research (ADR) integrates DSR and AR by developing and evaluating artifacts through participatory cycles focused on intervention while maintaining explicit expectations of rigor and contribution. These approaches are often used in international or study abroad research contexts, which are research environments spanning national, cultural, institutional, or governance boundaries and therefore require adaptive methods, careful ethical attention, and sustained stakeholder engagement. This synthesis results in an integrated methodological framework that positions Action Design Research as a supervisory scaffold for doctoral training in applied computing and information systems. The framework integrates Design Science Research and Action Research within an iterative cycle embedded in dialogical supervision and ethical reflexivity. It contributes a structured model for aligning methodological rigor, doctoral learning, and practical impact in complex and international research environments. Full article