Search Results (3,777)

Teacher preparation programmes must now ensure instructional continuity and quality across face-to-face, online, and hybrid modes, even amid health, climate, or security crises. This mixed-methods study examined which principles policymakers and teacher education directors deem essential for such resilience, and how those principles align with prior research and leadership theory. Semi-structured elite interviews (N = 25) were analyzed inductively to surface field-driven themes and deductively through two models: the ten evidence-based training principles synthesized by Hadad et al. and the six capacities of Striepe and Cunningham’s Crises Leadership Framework (CLF). Results show strong consensus on theory–practice integration, university–school partnerships, and collaborative learning, mapping chiefly to the CLF capacities of adaptive roles and stakeholder collaboration. Directors added practice-oriented priorities—authentic field immersion, formative feedback, and inclusive pedagogy—extending the crisis care and contextual influence dimensions. By contrast, policymakers uniquely stressed policy–academic co-decision-making, reinforcing complex decision-making at the system level. Reflective thinking skills and digital pedagogy, though prominent in the literature, were under-represented, signalling implementation gaps. Overall, the integrated model offers a crisis-ready blueprint for curriculum design, partnership governance, and digital capacity-building that can sustain continuity and quality in pre-service teacher education. Full article

Background/Objectives: There is a growing need for programs addressing perinatal mental health, particularly for new mothers. Postpartum depression (PPD) may occur during pregnancy or within the first year postpartum, with both short- and long-term negative consequences for both mothers and their infants. This study describes the cultural adaptation, implementation, and one-year follow-up of the Mom-to-Mom (M2M) program for minority Bedouin women in Southern Israel. Methods: We conducted a community-based intervention (M2M) emphasizing cultural adaptation. Outreach efforts were conducted in collaboration with healthcare professionals to encourage referral to the M2M program. A total of 111 mothers completed a self-administered questionnaire that included socio-demographic characteristics and PPD symptoms (PPDs) at two time points: prior to the intervention (Time-1) and one year after participating in the program (Time-2). PPD was measured using the Edinburgh Postnatal Depression Scale (EPDS), using a score cutoff of ≥10. Results: There was a significant decrease in PPDs (EPDS ≥ 13) between Time-1 and Time-2 after one year of follow-up in the M2M program (from 45% to 19.8%). Of the participants, 75% were referred to the program by healthcare professionals. Among those with EPDS ≥ 10, 30% were referred to mental health services. This program provided education, professional support, and led to the establishment of the first M2M center within a Bedouin community, located in the Negev (Naqab). Conclusions: The results emphasize the importance of culturally sensitive approaches to increase awareness, early diagnosis, and professional support in addressing PPD, tailored to a cultural context. Culturally adapted programs can be effective in minority populations and contribute to reducing disparities in maternal mental health care. Full article

Background and objectives: Large language models (LLMs) show promise in automating open-ended evaluation tasks, yet their reliability in rubric-based assessment remains uncertain. Variability in scoring, feedback, and rubric adherence raises concerns about transparency and pedagogical validity in educational contexts. This study introduces CourseEvalAI, a framework designed to enhance consistency and fidelity in rubric-guided evaluation by fine-tuning a general-purpose LLM with authentic university-level instructional content. Methods: The framework employs supervised fine-tuning with Low-Rank Adaptation (LoRA) on rubric-annotated answers and explanations drawn from undergraduate computer science exams. Responses generated by both the base and fine-tuned models were independently evaluated by two human raters and two LLM judges, applying dual-layer rubrics for answers (technical or argumentative) and explanations. Inter-rater reliability was reported as intraclass correlation coefficient (ICC(2,1)), Krippendorff’s α, and quadratic-weighted Cohen’s κ (QWK), and statistical analyses included Welch’s t tests with Holm–Bonferroni correction, Hedges’ g with bootstrap confidence intervals, and Levene’s tests. All responses, scores, feedback, and metadata were stored in a Neo4j graph database for structured exploration. Results: The fine-tuned model consistently outperformed the base version across all rubric dimensions, achieving higher scores for both answers and explanations. After multiple-testing correction, only the Generative Pre-trained Transformer (GPT-4)—judged Technical Answer contrast remains statistically significant; other contrasts show positive trends without passing the adjusted threshold, and no additional significance is claimed for explanation-level results. Variance in scoring decreased, inter-model agreement increased, and evaluator feedback for fine-tuned outputs contained fewer vague or critical remarks, indicating stronger rubric alignment and greater pedagogical coherence. Inter-rater reliability analyses indicated moderate human–human agreement and weaker alignment of LLM judges to the human mean. Originality: CourseEvalAI integrates rubric-guided fine-tuning, dual-layer evaluation, and graph-based storage into a unified framework. This combination provides a replicable and interpretable methodology that enhances the consistency, transparency, and pedagogical value of LLM-based evaluators in higher education and beyond. Full article

This study adopts a systems perspective to examine the persistent effects of China’s One-Child Policy (OCP) on the subjective well-being of older adults, emphasizing structural persistence, reinforcing feedback, and path-dependent lock-in in complex socio-technical systems. Using nationally representative data from the China Longitudinal Aging Social Survey (CLASS-2014), we exploit the OCP’s formal rollout at the end of 1979—operationalized with a 1980 cutoff—as a quasi-natural experiment. A Fuzzy Regression Discontinuity (FRD) design identifies the Local Average Treatment Effect of being an only-child parent on late-life well-being, mitigating endogeneity from selection and omitted variables. Theoretically, we integrate three lenses—policy durability and lock-in, intergenerational support, and life course dynamics—to construct a cross-level transmission framework: macro-institutional environments shape substitution capacity and constraint sets; meso-level family restructuring reconfigures support network topology and intergenerational resource flows; micro-level life-course processes accumulate policy-induced adaptations through education, savings, occupation, and residence choices, with effects materializing in old age. Empirically, we find that the OCP significantly reduces subjective well-being among the first generation of affected parents decades later (2SLS estimate ≈ −0.23 on a 1–5 scale). The effects are heterogeneous: rural residents experience large negative impacts, urban effects are muted; men are more adversely affected than women; and individuals without spouses exhibit greater declines than those with spouses. Design validity is supported by a discontinuous shift in fertility at the threshold, smooth density and covariate balance around the cutoff, bandwidth insensitivity, “donut” RD robustness, and a placebo test among ethnic minorities exempt from strict enforcement. These results demonstrate how demographic policies generate lasting impacts on elderly well-being through transforming intergenerational support systems. Policy implications include strengthening rural pension and healthcare systems, expanding community-based eldercare services for spouseless elderly, and developing complementary support programs. Full article

Background/Objectives: Ulcerative Colitis (UC) lacks longitudinal visual data, which limits both disease progression modeling and the effectiveness of computer-aided diagnosis systems. These systems are further constrained by sparse intermediate disease stages and the discrete nature of the Mayo Endoscopic Score (MES). Meanwhile, synthetic image generation has made significant advances. In this paper, we propose novel ordinal embedding architectures for conditional diffusion models to generate realistic UC progression sequences from cross-sectional endoscopic images. Methods: By adapting Stable Diffusion v1.4 with two specialized ordinal embeddings (Basic Ordinal Embedder using linear interpolation and Additive Ordinal Embedder modeling cumulative pathological features), our framework converts discrete MES categories into continuous progression representations. Results: The Additive Ordinal Embedder outperforms alternatives, achieving superior distributional alignment (CMMD 0.4137, recall 0.6331) and disease consistency comparable to real data (Quadratic Weighted Kappa 0.8425, UMAP Silhouette Score 0.0571). The generated sequences exhibit smooth transitions between severity levels while maintaining anatomical fidelity. Conclusions: This work establishes a foundation for transforming static medical datasets into dynamic progression models and demonstrates that ordinal-aware embeddings can effectively capture disease severity relationships, enabling synthesis of underrepresented intermediate stages. These advances support applications in medical education, diagnosis, and synthetic data generation. Full article

The purpose of this study is to propose and critically assess a sustainable urban regeneration model for the redevelopment of the former K-2 military airbase in Daegu, Korea. Large-scale idle military sites pose significant challenges in terms of ecological remediation, social integration, and economic transformation, but also offer opportunities for redefining urban identity and global competitiveness. To address this, we develop the concept of the “Global Contents City,” a planning framework that integrates cultural exchange, creative industries, education, and tourism within a sustainable urban ecosystem. The research employs a qualitative methodology that combines theoretical review, comparative analysis of international precedents (e.g., Munich-Riem, Tempelhof, Stapleton, and Toronto), and design-oriented masterplanning. The findings highlight design strategies that spatially interconnect cultural, educational, industrial, and ecological functions while reinforcing low-carbon infrastructure and green open space. By situating the Daegu K-2 case in an international context, the study demonstrates how lessons from post-military redevelopments can be adapted to Korea, contributing to both scholarly debates and practical frameworks for sustainable city-making. Full article

Major immunization programs and initiatives have prioritized the advancement of equity in immunization. Over the past decade, the World Health Organization has made contributions to understanding inequalities in immunization, including global analyses of immunization inequality as well as tools for knowledge dissemination and capacity strengthening. This article provides an overview of these contributions, highlighting key findings of scholarly reports and journal articles and identifying areas for further research and development to expand monitoring efforts and enhance their impact. Global analyses have primarily drawn from household survey data to explore inequalities related to economic status, education, gender, and geography. Reports and articles address childhood immunization, COVID-19 vaccine indicators, and maternal tetanus protection. Inequalities were reported across all dimensions, with variation by country and income grouping. Time trends generally suggest persistent, though narrowing, inequalities. Areas for further development include the following: increasing awareness and political support for advancing equity in immunization; expanding the collection, availability, and use of disaggregated immunization data; continuous capacity building of inequality monitoring, especially at national and subnational levels; adapting inequality monitoring practices to changing contexts and priorities; strengthening the links between data/evidence and action/impact; and building on existing partnerships and collaborations. Full article

This paper presents the design, development, and preliminary validation of a haptic virtual reality simulator for Shielded Metal Arc Welding (SMAW) at the Pontificia Universidad Católica de Valparaíso, Chile, aimed at enhancing psychomotor training for mechanical engineering students in line with Industry 4.0 demands. The system integrates Unity 3D, a commercial haptic device, and a custom 3D-printed electrode holder replicating the welding booth, enabling interaction through visual, auditory, and tactile feedback. Thirty students with minimal welding experience and seven experts participated in usability and realism assessments. The results showed that 80% of students perceived motor skill improvement, 60% rated realism as adequate, and 90% preferred hybrid training (simulator + workshop). The prototype was practically implemented at the mechanical engineering school, requiring only a mid-range workstation, the Touch haptic device, and the developed software, demonstrating feasibility in real academic settings. The findings indicate potential to build confidence, support motor coordination, and provide a safe, resource-efficient training environment, while experts emphasized the need for automated feedback and improved haptic fidelity. The modular architecture allows scalability, extension to other welding processes, and adaptation for inclusive education. This prototype demonstrates how locally developed immersive technologies can modernize technical education while promoting sustainability, accessibility, and skill readiness. Full article

The integration of Virtual Worlds (VW) and Intelligent Tutoring Systems (ITS) represents a transformative advancement in education, combining immersive, interactive learning with AI-driven personalization. This study explores the synergies between these technologies, analyzing their benefits, challenges, and applications in domains such as medical training, STEM education, and language learning. Findings highlight their shared characteristics of adaptability, real-time feedback, and collaborative learning. However, challenges such as computational demands, pedagogical complexity, and ethical concerns must be addressed. Future research should focus on hybrid models leveraging blockchain, IoT, and augmented reality to enhance adaptive and scalable learning experiences. Full article

This study investigates the determinants of Romanian workers’ migration intentions towards Italy, integrating economic, social, and psychological perspectives. Based on a sample of 358 respondents, four hypotheses were tested concerning perceived living standards, working conditions, quality of public services, and anticipated integration difficulties. Data were analysed using descriptive statistics, Spearman’s rho correlation, Mann–Whitney U, Chi-square, ANOVA, and ordinal logistic regression. The results confirm that higher perceived living standards and better working conditions in Italy significantly increase the likelihood of expressing migration intentions, while favourable evaluations of healthcare and education act as additional pull factors. Conversely, anticipated integration difficulties, particularly language barriers and cultural adaptation, reduce migration intentions, indicating that socio-psychological obstacles can counterbalance economic incentives. By combining non-parametric and multivariate analyses, the study demonstrates that migration is a multidimensional process shaped not only by structural opportunities but also by behavioural and psychological appraisals. These findings are consistent with recent research on European labour mobility and contribute to the literature by highlighting the role of subjective perceptions in shaping migration decisions. Implications for policy include the need to address both economic disparities and integration barriers to support more balanced mobility within the European space. Full article

Background: Ethical dilemmas and the moral distress they generate are central challenges in healthcare practice and professional identity formation. While moral reasoning has been widely studied, comparative evidence on how medical students and practicing physicians approach ethical dilemmas remains scarce in Eastern Europe. Methods: A total of 244 participants (51 senior medical students and 193 physicians) completed an adapted version of the Defining Issues Test, version 2 (DIT-2). Three classical dilemmas were assessed: end-of-life decision-making, access to life-saving medication, and the reintegration of a fugitive. Responses were analyzed through descriptive statistics and chi-square tests to identify differences in decision choices and underlying reasoning. Results: Physicians consistently endorsed conventional, law-based reasoning, emphasizing legality and professional codes, while medical students demonstrated greater variability, indecision, and openness to compassion-driven justifications. In the “Jan and the Drug” and “Fugitive” dilemmas, significant between-group differences highlighted tensions between legality, empathy, and justice (p < 0.01). These differences in reasoning indicate differing vulnerabilities to moral distress, especially when legal and compassionate perspectives conflict. Conclusions: The findings reveal distinct patterns of moral reasoning that reflect different levels of vulnerability to moral distress in healthcare contexts. Integrating structured ethics training and reflective dialogue into both undergraduate and continuing medical education could mitigate moral distress by fostering a balance between justice, compassion, and professional responsibility. Full article

This research presents a novel conceptual framework for inclusive education by integrating Internet of Things (IoT)-driven real-time environmental and behavioral monitoring with adaptive teaching strategies. Unlike traditional methods, our model leverages sensor-based data collection to analyze classroom conditions, teacher mobility, and student interactions, enabling dynamic adjustments that aim to enhance engagement and inclusivity. While the framework is theoretical and has not yet undergone experimental validation, we discuss how optimizing spatial configurations, voice dynamics, and movement patterns could support student participation, particularly for learners with diverse needs. Pilot implementations and empirical testing are planned for future research. By merging data-driven insights with educators’ expertise, our approach offers a scalable vision for creating responsive, inclusive learning environments that proactively address barriers to education. Full article

This paper examines the evolving trajectory of the Social Determinants of Health (SDOH), tracing their development from early observational studies to contemporary, interdisciplinary frameworks that emphasize structural inequities and relational dynamics. It explores foundational milestones such as the Whitehall studies, the Heckler Report, and the World Health Organization’s conceptual models, which positioned SDOH as key drivers of population health. The paper highlights how upstream determinants—such as governance, policy, and socioeconomic systems—influence downstream health outcomes through mechanisms of social stratification and unequal access to resources. While SDOH are increasingly applied in clinical and educational settings, significant challenges persist, including underinvestment in community systems, fragmented care models, and political rollbacks of equity-centered policies. The paper critiques deterministic and deficit-focused framings of SDOH and underscores a shift toward more relational, context-sensitive, and agency-oriented approaches, reflected in the emerging concept of “social dynamics of health.” It highlights the importance of experiential education, competency-based curricula, and digital innovations in driving systemic transformation. Emphasis is placed on reimagining SDOH pedagogy and expanding interdisciplinary, data-driven research to bridge the gap between knowledge and practice. Amid shifting political landscapes, sustaining health equity efforts requires embracing adaptive, participatory models that acknowledge power, community agency, and structural change. Full article

This study examines the key factors influencing the quality of distance learning in higher education during the COVID-19 pandemic, a period when online learning became the dominant mode of education. Using a descriptive method and a 26-item questionnaire, data were collected from a representative sample of 360 students in Vojvodina, Serbia. The factors analyzed include computer literacy and technology access (Ph1), students’ ability to balance life obligations with study demands (Ph2), and their motivation for distance learning (Ph3). The results show that 89% of students had adequate IT access, 47% were able to reconcile study and personal obligations, and 70% reported strong motivation. Correlation analysis confirmed a statistically significant positive relationship between all three factors and students’ perceptions of well-organized distance learning, thus supporting the main research hypothesis. Beyond these findings, this study interprets digital literacy as adaptability, time management as resilience, and motivation as value orientation and future thinking—core dimensions of sustainability competences outlined in the European GreenComp framework. Distance learning is therefore positioned not only as an emergency response but also as a transformative pedagogy that integrates brain (knowledge), hands (skills), heart (values), and spirit (purpose), contributing to sustainable and resilient higher education. Full article

This study introduces an Encyclopedic Agent for children’s learning that integrates multimodal retrieval with retrieval-augmented generation (RAG). To support this framework, we construct a dataset of 9524 Wikipedia pages covering 935 encyclopedia topics, each converted into images with associated topical queries and explanations. Based on this dataset, we fine-tune SigLIP, a vision–language retrieval model, using LoRA adaptation on 8484 training pairs, with 1040 reserved for testing. Experimental results show that the fine-tuned SigLIP significantly outperforms baseline models such as ColPali in both accuracy and latency, enabling efficient and precise document-image retrieval. Combined with GPT-5 for response generation, the Encyclopedic Agent delivers illustrated, interactive Q&A that is more accessible and engaging for children compared to traditional text-only methods. These findings highlight the feasibility of applying multimodal retrieval and RAG to educational agents, offering new possibilities for personalized, child-centered learning in domains such as science, history, and the arts. Full article