Search Results (1,180)

Addressing the urgent need for sustainable transformation in higher education, this paper explores how a collaborative action research group of teacher educators from different institutions contributed to higher education transformation through sustainable education approaches. Drawing on cultural–historical activity theory (CHAT), we analyze how cross-institutional partnerships fostered personal and professional development through digital collaboration, regular online meetings, and reflective dialogue. The study employed participatory action research, using weekly reflective journals and group meetings as mediating tools supporting sustained professional learning. Findings indicate that building common ground across institutional contexts and investing in trust-building cultivated a meaningful collaborative environment, a “third space” that mediated expansive learning and professional transformation. Within this space, the diversity of institutional backgrounds enriched the activity system, and productive contradictions served as generative mechanisms that catalyze expansive learning by exposing participants to diverse institutional perspectives. The study further shows that sustainable collaboration emerged not from formal institutional structures but from shared ownership, cultural alignment, and relational commitment. These social and cultural processes supported the development of systems-thinking, strategic-thinking, and interpersonal competencies supporting sustainable professional development. The study highlights the potential of sustainable cross-institutional spaces as a model for professional growth in higher education. Full article

Priasis is a chronic inflammatory skin disease characterized by keratinocyte hyperproliferation, impaired differentiation, and dysregulated immune responses. Emerging evidence highlights the central role of keratinocytes as immune-competent cells that integrate signals from cytokines, metabolic cues, the gut–skin axis, and the tissue microenvironment. Key intracellular signaling pathways, including NF-κB, JAK/STAT, MAPK, and PI3K/AKT/mTOR, along with the IL-23/IL-17 axis, orchestrate keratinocyte-mediated inflammation and epidermal hyperplasia. Metabolic factors, nutrients, and redox balance further modulate these responses, while the intestinal microbiota and its metabolites, such as short-chain fatty acids, shape systemic and cutaneous inflammation. This review offers a critical, integrated perspective, that moves beyond descriptive summaries. We propose a conceptual framework in which the keratinocyte metabolic state, particularly the sirtuin/NAD+ axis, acts as a crucial convergence point for systemic nutritional, microbial, and inflammatory signals. Targeting sirtuins and associated pathways with natural or synthetic modulators represents a promising, host-centric strategy to restore keratinocyte function and reduce chronic inflammation. This synthesis underscores the potential of combining molecular, metabolic, microbial, and nutritional insights to develop personalized and effective approaches for psoriasis management.  Full article

Background/Objectives: Efficient and personalized task allocation in hospital emergency departments (EDs) is critical for operational efficiency and patient-centered care. However, the complexity of staff coordination and the variability among patients and healthcare professionals pose significant challenges. This study proposes a simulation-based framework for modeling doctors and nurses as intelligent agents guided by computational trust mechanisms. The objective is to explore how trust-informed coordination can support AI-driven and personalized decision-making in ED management. Methods: The framework was implemented in Unity, a 3D graphics platform, where agents assess their competence and patient-specific needs before undertaking tasks and adaptively coordinate with colleagues. The simulation environment enables real-time observation of workflow dynamics, resource utilization, and patient outcomes. We examined three scenarios—Baseline, Replacement, and Training—reflecting alternative staff management strategies. Results: Trust-informed task allocation balanced patient safety and efficiency by adaptively responding to nurse performance and patient acuity levels. In the Baseline scenario, prioritizing safety reduced errors but increased patient delays compared to a FIFO policy. The Replacement scenario improved throughput and reduced delays, though at additional staffing costs. The training scenario fostered long-term skill development among low-performing nurses, despite short-term delays and risks, supporting sustainable and personalized capacity building in ED teams. Conclusions: The proposed framework demonstrates the potential of computational trust for personalized and evidence-based decision support in emergency medicine. By linking staff coordination with adaptive and AI-informed decision-making, hospital managers are provided with a tool to evaluate alternative staffing and treatment policies under controlled and repeatable conditions. This work thus contributes to the broader vision of precision and personalized medicine, where operational decisions dynamically adapt to both patient needs and staff capabilities. Full article

Disproportionality in missing persons cases raises critical questions about forensic and legal decision making. In the UK, Black individuals comprise 14% of missing persons but only 3% of the population. This study analysed 18,266 cases from nine police forces in England and Wales to examine how case characteristics and ethnicity influence risk assessments. Analyses proceeded in three stages: (i) descriptive profiling of demographic, contextual, and risk-related factors; (ii) statistical comparison across ethnic groups; (iii) predictive modelling of how these characteristics influence risk classification. Ethnicity did not independently predict risk classification once other characteristics were controlled for. However, characteristics disproportionately associated with Black missing persons, such as youth and care orders, were linked to lower risk classifications. In contrast, White individuals were more often reported with mental health, health, or harm risks, which strongly predicted high-risk classification. This suggests police decision making may be indirectly shaped by ethnicity via associated characteristics, raising concerns about equity in assessment and investigative prioritisation. Potential mechanisms include underreporting of vulnerabilities in minority communities and inconsistencies in police recording practices. The study highlights the need for culturally informed, evidence-based decision frameworks in missing persons investigations to support just and accurate decision making in policing. Full article

Background: Medical education is highly demanding and often entails stress, pressure, and competition. Understanding what drives students’ satisfaction is essential to support learning and well-being. This study aims to identify factors associated with satisfaction with medical education among Polish medical students, focusing on motivation, personal circumstances, resilience, and the long-term impact of COVID-19. Methods: In a cross-sectional online survey, 334 students from years one, four, and six completed measures of satisfaction with medical studies (nineteen items), motivation (ten items), resilience (using the Brief Resilience Scale), self-rated health, financial situation, global life satisfaction, and study-related stress, plus eight items on COVID-19 impact. Associations were assessed using Spearman correlations and Mann–Whitney U tests. Results: Higher satisfaction was associated with intrinsic motivation (e.g., personal decision to study medicine or interest in medicine), more favorable personal circumstances (better health, financial situation, higher global life satisfaction, and lower stress), and greater individual resilience. Students reporting pandemic-related setbacks (knowledge gaps, reduced confidence, curtailed clinical exposure, and interpersonal skills) showed lower satisfaction with overall experience, relationships, theoretical and practical classes, and perceived future competence. Conclusions: Intrinsic motivation, resilience, and supportive personal circumstances were linked to higher satisfaction, whereas enduring pandemic disruptions coincided with lower satisfaction across domains. Targeted strategies that cultivate intrinsic motivation and resilience and address financial/health stressors and COVID-19 learning gaps may enhance student satisfaction. Full article

Higher education faces increasing demands to address student diversity in engagement, learning preferences, and professional readiness. This study examined the feasibility of integrating personality-tailored case-based learning in teacher education. Building on the Big Five personality model and principles of differentiated, case-based pedagogy, we developed a prototype that generated individualized case descriptions using a personality inventory and generative AI. The intervention was implemented in a teacher education course, with 37 students (≈79%) completing an anonymous evaluation survey. Quantitative measures included emotion-word selections and Likert-type ratings of case relevance and group discussions; qualitative data were collected through open-ended reflections. Findings indicated that students experienced the intervention as engaging, relevant, and appropriately challenging. Group discussions received the highest ratings, with students emphasizing the value of peer dialogue for gaining new perspectives and making sense of the cases. Qualitative themes highlighted the realism of personalized scenarios, opportunities for reflection, and the importance of scaffolding, while challenges included unclear instructions and limited diversity among cases. The study demonstrates the feasibility and perceived pedagogical value of personality-tailored cases as a scalable model of differentiation in higher education. Future research should adopt controlled designs to disentangle the effects of personality instruction, feedback, and personalization, and systematically evaluate the distinctiveness of generated cases. By integrating psychological self-insight with authentic practice scenarios, personality-informed case-based learning shows promise for enhancing student agency, reflective competence, and readiness for professional practice. Full article

As artificial intelligence (AI) advances, there is growing interest in leveraging this technology to enhance climate change research and responses. While AI has been applied in quantitative climate research, its role in qualitative research remains underdeveloped. Yet, qualitative inquiry is essential for understanding how individuals perceive and experience the effects of climate change. This study aimed to both (1) gain a deeper understanding of New York City residents’ perceptions and lived experiences of climate change and (2) evaluate the suitability of AI for analyzing qualitative data. Using StreetTalk, a qualitative method involving street-intercept video interviews and social media dissemination, research teams analyzed interview transcripts through four approaches: human-only, human-then-AI, AI-then-human, and AI-only. Co-authors were then provided with anonymized (blinded) versions of the final theme sets that they did not contribute to and evaluated them using a standardized rubric developed for this study. The AI-then-human approach produced the most comprehensive and contextually accurate results, yielding nine key themes: (1) personal responsibility and action, (2) community unity and support, (3) government and corporate responsibility, (4) concern for future generations, (5) climate change impact, (6) climate-related conspiracy theories, (7) low literacy around local climate change, (8) helplessness, and (9) competing interests around climate change. These findings provide valuable local perspectives to guide evidence-based strategies for climate mitigation and community engagement. This research also represents an initial step toward establishing best practices for integrating AI into qualitative data analysis. Full article

Background/Objectives: This study aimed to develop the Technological Competency as Caring in Healthcare Providers Instrument (TCCHI) for multidisciplinary use, based on Locsin’s theory of Technological Competency as Caring in Nursing. Methods: A content validation design employing a modified Delphi technique was conducted with a multidisciplinary panel of 10 healthcare experts (recruited by purposive sampling based on expertise in technology/caring). The preliminary 67-item pool was derived from a literature review and theoretical alignment. Two Delphi rounds were implemented to establish face and content validity. Qualitative feedback from Round 1 guided item refinement for Round 2. The Wilcoxon matched-pairs signed-rank test was used to confirm the response stability between rounds. Results: Among the 67 initial items, 38 were retained after two Delphi rounds, achieving an I-CVI of 0.80–0.90. Response stability was established (p > 0.05). The resulting 38 items were categorized into six refined concepts reflecting the integration of technology and caring. Inter-rater consistency, assessed by the Intraclass Correlation Coefficient (ICC), was moderate (Round 1 ICC = 0.49; Round 2 ICC = 0.50), suggesting initial variability among the multidisciplinary panel. Conclusions: The TCCHI is a comprehensive and theoretically grounded instrument applicable across diverse healthcare disciplines. However, the moderate inter-rater consistency suggests that further empirical validation is required. Further psychometric evaluation, including confirmatory factor analysis and internal consistency reliability testing, is required to establish construct validity and strengthen the instrument’s applicability in diverse healthcare settings. Full article

The rapid integration of artificial intelligence (AI) into healthcare has reshaped medical education and clinical practice. While technological innovation is vital, soft skills are essential for preserving trust, ethical accountability, and humanistic care. This study explores the evolving role of soft skills in medical education in the AI era by examining definitional challenges, pedagogical strategies, and the integration of AI-related literacy. A narrative review methodology synthesized evidence across seven thematic domains, focusing on curricular integration, pedagogical strategies, and assessment approaches in medical education within AI-enabled learning environments. The findings demonstrated that soft skills improve patient adherence, satisfaction, safety, and trust; strengthen physicians’ professional identity, collaboration, and resilience; and enhance system-level outcomes, such as resilience, safety, and public trust. Experiential, reflective, and competency-based pedagogies remain the most effective instructional strategies, while AI-supported tools, including virtual patients, adaptive simulations, large language models (LLMs), and Retrieval-Augmented Generation systems (RAG), offer complementary benefits by enhancing doctor-patient communication, providing real-time personalized feedback, and strengthening clinical reasoning. Soft skills function as an interconnected and synergistic ecosystem that is reinforced by cognitive, affective, humanistic, and ethical mechanisms. Integrating these competencies with AI literacy promotes theoretical clarity, supports programmatic assessment, and fosters responsible innovation, ensuring that technological advancement enhances rather than diminishes the humanistic foundations of medicine. Full article

Background/Objectives: Readiness for practice is an essential outcome of nursing education, yet the factors influencing it among baccalaureate nursing students in Mongolia remain underexplored. This study aimed to provide a holistic understanding of factors influencing readiness for practice among baccalaureate nursing students in Mongolia, employing both quantitative and qualitative approaches. Methods: A convergent mixed-methods design was used. The study included 150 final-year baccalaureate nursing students from 14 Mongolian universities. Quantitative data were collected via survey and analyzed using multiple regression analyses in SPSS 26.0. Concurrently, qualitative data were obtained through focus group interviews with 25 participants (nurses and faculty) and analyzed using content analysis. Results: Quantitative analyses revealed that the clinical learning environment, clinical competence, and critical thinking significantly influenced readiness for practice, explaining 40% of the variance. Qualitative findings—derived from nurses’ and faculty’s perspectives and findings—provided deeper insights: “maturity” was defined as students’ coping ability and adaptability; “competence” encompassed clinical, ethical, cultural, and communication skills; and “professional values” reflected passion, motivation, and readiness to engage in practice. These findings highlighted the essential interplay between personal, educational, and contextual factors in shaping readiness. Conclusions: Findings suggest strategies to enhance nursing students’ readiness, including fostering supportive clinical learning environments, structured mentorship, and integrating ethical and cultural training into curricula. These insights offer actionable recommendations for nursing schools and clinical institutions to strengthen collaboration, support professional development, and prepare competent, adaptable, and ethically grounded nursing graduates in Mongolia. Full article

Educational equity and access to quality learning opportunities represent fundamental pillars of sustainable societal development, directly aligned with the United Nations Sustainable Development Goal 4 (Quality Education). Student retention remains a critical challenge in higher education, with early disengagement strongly predicting eventual failure and limiting opportunities for social mobility. While machine learning models have demonstrated impressive predictive accuracy for identifying at-risk students, most systems prioritize performance metrics over practical deployment constraints, creating a gap between research demonstrations and real-world impact for social good. We present an accountable and interpretable decision support system that balances three competing objectives essential for responsible AI deployment: ultra-early prediction timing (day 14 of semester), manageable instructor workload (flagging 15% of students), and model transparency (multiple explanation mechanisms). Using the Open University Learning Analytics Dataset (OULAD) containing 22,437 students across seven modules, we develop predictive models from activity patterns, assessment performance, and demographics observable within two weeks. We compare threshold-based rules, logistic regression (interpretable linear modeling), and gradient boosting (ensemble modeling) using temporal validation where early course presentations train models tested on later cohorts. Results show gradient boosting achieves AUC (Area Under the ROC Curve, measuring discrimination ability) of 0.789 and average precision of 0.722, with logistic regression performing nearly identically (AUC 0.783, AP 0.713), revealing that linear modeling captures most predictive signal and makes interpretability essentially free. At our recommended threshold of 0.607, the predictive model flags 15% of students with 84% precision and 35% recall, creating actionable alert lists instructors can manage within normal teaching duties while maintaining accountability for false positives. Calibration analysis confirms that predicted probabilities match observed failure rates, ensuring trustworthy risk estimates. Feature importance modeling reveals that assessment completion and activity patterns dominate demographic factors, providing transparent evidence that behavioral engagement matters more than student background. We implement a complete decision support system generating instructor reports, explainable natural language justifications for each alert, and personalized intervention templates. Our contribution advances responsible AI for social good by demonstrating that interpretable predictive modeling can support equitable educational outcomes when designed with explicit attention to timing, workload, and transparency—core principles of accountable artificial intelligence. Full article

This study examines university teachers’ digital competences during Emergency Remote Teaching at three Spanish institutions—the University of La Laguna, the University of Extremadura, and the University of Valladolid—and, from the faculty perspective, appraises hybrid teaching experiences and institutional support services. We employed a qualitative multi-case design using semi-structured focus-group interviews and discussion groups with 57 instructors from Social Sciences and Humanities, Engineering, and Health Sciences, selected via purposive sampling. Data were deductively coded in Atlas.ti 24. Faculty perceive hybrid teaching as useful for widening access and repositioning the virtual campus as a communicative hub; they highlight Moodle, videoconferencing, content-authoring tools such as H5P, and methodologies like gamification and flipped learning to enhance motivation. Nonetheless, generational gaps and concerns about the authenticity of online assessment persist, supporting continued reliance on in-person examinations. Technical and training support services are viewed positively, yet respondents call for more staffing and stronger dissemination of teaching resources. Consolidating teachers’ digital competences requires institutional policies that integrate robust infrastructure, contextualized continuous professional development, and communities of practice to ensure the sustainability of hybrid models in higher education at the national level. Full article

Background/Objectives: This study explores the concept of dignity in care for older patients with cancer in Korea using the hybrid model. Methods: A three-phase hybrid model approach was employed for concept analysis. In the theoretical phase, a literature review was conducted to determine the attributes and definition of dignity in care. In the fieldwork phase, the practicability of the defined concept was assessed through practical observations. In the final analysis phase, findings from the theoretical and fieldwork phases were compared and synthesized to validate the attributes and definition of the concept. Results: Four dimensions of dignity in care were found identified: intrinsic, relational, social, and illness-related. Professional dimension was added based on nurses’ perspective. Attributes of dignity in care for older cancer patients include eight key elements: personal identity, a deepened sense of value and meaning of life, respect, relationships (with medical staff, family, and fellow patients), support for society’s care policies, systemic support from healthcare systems, free will and choice, and proactive coping strategies. For nurses, dignity in care involves seven attributes: understanding and respecting human values, ethical and moral attitudes, interaction-based communication through the cultivation of rapport, systemic support from healthcare systems, protection of dignity, activities promoting dignity, and professional competency. Conclusions: This study provides concept definitions and attributes for dignity in care, equipping clinical nurses with assessment tools to better understand and enhance the dignity of older cancer patients. Full article

Depression affects millions of people worldwide. Traditional management relies heavily on periodic clinical assessments and self-reports, which lack real-time responsiveness and personalization. Despite numerous research prototypes exploring Internet of Things (IoT)-based mental health support, almost none have translated into practical, mainstream solutions. This gap stems from several interrelated challenges, including the absence of robust, flexible, and safe architectural frameworks; the diversity of IoT device ownership; the need for further research across many aspects of technology-based depression support; highly individualized user needs; and ongoing concerns regarding safety and personalization. We aim to develop a reference architecture for IoT-based safe and personalized depression management. We introduce IoTMindCare, integrating current best practices while maintaining the flexibility required to incorporate future research and technology innovations. A structured review of contemporary IoT-based solutions for depression management is used to establish their strengths, limitations, and gaps. Then, following the Attribute-Driven Design (ADD) method, we design IoTMindCare. The Architecture Trade-off Analysis Method (ATAM) is used to evaluate the proposed reference architecture. The proposed reference architecture features a modular, layered logical view design with cross-layer interactions, incorporating expert input to define system components, data flows, and user requirements. Personalization features, including continuous, context-aware feedback and safety-related mechanisms, were designed based on the needs of stakeholders, primarily users and caregivers, throughout the system architecture. ATAM evaluation shows that IoTMindCare supports safety and personalization significantly better than current designs. This work provides a flexible, safe, and extensible architectural foundation for IoT-based depression management systems, enabling the construction of optimal solutions that integrate the most effective current research and technology while remaining adaptable to future advancements. IoTMindCare provides a unifying, aggregation-style reference architecture that consolidates design principles and operational lessons from multiple prior IoT mental-health solutions, enabling these systems to be instantiated, compared, and extended rather than directly competing with any single implementation. Full article

Active methodologies are consolidated as a key trend in education for the competence development of students at all educational stages, due to the academic, social, personal, and professional benefits that their implementation in the classroom provides. The profusion of systematic reviews on different types of active methodologies in recent years provides a high level of accumulated evidence. Therefore, a review of reviews allows comparing and contrasting different studies, offering a comprehensive perspective on their impact on Basic Education (Primary and Secondary Education). This study carries out an umbrella review through a qualitative systematic analysis using WoS, Scopus, and Dialnet databases. Reviews carried out in the last six years on the use of different active methodologies have been analysed, obtaining a total of 33 final references. The findings indicate that general research on active methodologies in Basic Education is limited, and these have a positive impact on students with favorable effects on their academic performance and comprehensive development. Despite their relevance, passive and directive methodologies remain predominant. Furthermore, the need for teacher training for effective implementation of active methodologies in the classroom is highlighted. Full article