Search Results (871)

The emergence of artificial intelligence (AI) coaching challenges the professional roles and identities of human coaches, yet empirical research on this transformation remains scarce. This qualitative field study investigates how professional coaches navigate their roles following the organizational adoption of AI coaching. Drawing on the automation-augmentation paradox, occupational role identity, and role ambiguity theories, we analyzed 15 semi-structured interviews with 12 professional coaches in an Asian coaching firm, contextualized by pre- and post-interviews with the company CEO and the AI provider. Findings reveal that top-down AI implementation triggered significant role ambiguity, catalyzing both protective and expansive identity work. Coaches defended their unique human value (e.g., empathy), while simultaneously experimenting with AI, shifting their perception from threat to collaborative tool. This adaptive process enabled the emergence of distinct AI functions and new “blended” human–AI coaching models. Our resulting conceptual framework demonstrates that resolving the automation-augmentation paradox in relational professions is fundamentally an identity-driven process rather than a technical task reallocation. Furthermore, our findings demonstrate that organizationally induced role ambiguity can serve as a catalyst for professional renewal and vocational adaptation, particularly when supported by participatory leadership, thereby advancing theory and contributing new insights to the literature on technological and vocational transformation in organizational contexts. Full article

This study investigates the challenge of affordable housing in Riyadh, a city undergoing rapid transformation aligned with Saudi Arabia’s Vision 2030. It aims to bridge the structural gap in the housing market by developing a comprehensive analytical framework that measures housing suitability for emerging middle-income families, linking it to economic, spatial, and behavioral dimensions. The research employs a sequential mixed-methods design. The first phase involved a Multi-Criteria Decision Analysis (MCDA) of 106 residential neighborhoods, constructing a Housing Suitability Index (HSI) based on financing cost (≤SAR 880,000), quality of urban life, and geographical accessibility. The second phase utilized focus groups with 16 participants from real estate developers and new families to explore behavioral drivers and subjective trade-offs. Quantitative results identified “convenience clusters” primarily in the city’s southeastern and southwestern sectors, offering an optimal balance between price and accessibility. Qualitative analysis revealed a significant trust gap and a misalignment of priorities: new families are increasingly willing to sacrifice unit size for central location and construction quality, a preference that conflicts with developers’ strategies focused on luxury units or peripheral projects for higher margins. The study concludes that achieving the 70% homeownership target requires a hybrid policy model, combining supply-side stimuli (e.g., subsidized land) with demand-side management (e.g., progressive mortgages). It recommends integrating the HSI into urban planning to direct investment towards logistically connected areas, fostering sustainable communities. Full article

Reflection, as a key component of the educational process, strengthens the child’s sense of identity, self-awareness, and ability to learn consciously. Although the theoretical foundations of reflection are widely studied in educational science, its practical application in the context of preschool education remains fragmented and under-researched. The aim of this study is to reveal how preschool teachers understand, apply, and model the reflection process in educating 4–5-year-old children, to reveal the role of reflection between pedagogical action and conscious perception of education. The study is based on a qualitative methodology, applying the semi-structured interview method and the principles of qualitative content analysis. The study involved preschool teachers and educational support specialists working with 4–5-year-old children. Data analysis made it possible to identify the fundamental directions of the concept and application of reflection in pedagogical practice. The results of the study showed that reflection in preschool education acts as a link between action and awareness, that is, a process that allows both the teacher and the child to reflect on and transform the educational experience. It manifests itself through emotional awareness, self-assessment, professional growth, and adaptation of activities to the needs of children. Reflective practices integrated into daily education promote children’s metacognitive and emotional abilities, strengthen their self-awareness and motivation for learning, and provide teachers with the opportunity to purposefully improve their professional practice and improve the quality of education. Full article

Background: Digital transformation in healthcare has advanced rapidly in hospitals and primary care, while long-term care facilities have often lagged behind. In nursing homes, nurses play a central role in coordinating care and accessing medical expertise, yet digital tools to support these tasks remain inconsistently implemented. The CareConnect study, funded under the German Model Program for Telecare (§ 125a SGB XI), aimed to develop and implement a multiprofessional telecare system tailored to nursing home care. Objective: This implementation study examined the feasibility, acceptability, and early adoption of a multiprofessional telecare system in nursing homes, focusing on implementation processes, contextual influences, and facilitators and barriers to integration into routine nursing workflows. Methods: A participatory implementation design was employed over 15 months (June 2024–August 2025), involving a university hospital, two nursing homes (NHs), and four medical practices in an urban region in Germany. The telecare intervention consisted of scheduled video-based teleconsultations and interdisciplinary case discussions supported by diagnostic devices (e.g., otoscopes, dermatoscopes, ECGs). The implementation strategy followed the Standards for Reporting Implementation Studies (StaRI) and was informed by the Consolidated Framework for Implementation Research (CFIR). Data sources included telecare documentation, nurse surveys, researcher observations, and structured feedback discussions. Quantitative and qualitative data were analyzed descriptively and triangulated to assess implementation outcomes and mechanisms. Results: A total of 152 documented telecare contacts were conducted with 69 participating residents. Most interactions occurred with general practitioners (48.7%) and dermatologists (23%). Across all contacts, in 79% of cases, there was no need for an in-person visit or transportation. Physicians rated most cases as suitable for digital management, as indicated by a mean of 4.09 (SD = 1.00) on a 5-point Likert scale. Nurses reported improved communication, time savings, and enhanced technical and diagnostic skills. Key challenges included delayed technical integration, interoperability issues, and varying interpretations of data protection requirements across facilities. Conclusions: This pilot study suggests that telecare can be feasibly introduced and accepted in nursing home settings when implemented through context-sensitive, participatory strategies. Implementation science approaches are essential for understanding how telecare can be sustainably embedded into routine nursing home practice. Full article

Forests play a critical role in Earth’s ecosystem, yet monitoring their long-term, large-scale spatiotemporal dynamics remains a significant challenge. This study addresses this gap by developing an integrated framework to map annual forest distribution in Hunan, China, from 1999 to 2023 at a high resolution of 30 m. Our methodology combines multi-temporal satellite imagery (Landsat 5/7/8/9) with key environmental variables, including digital elevation models, temperature, and precipitation data. To efficiently reconstruct historical maps, training samples were automatically derived from a reliable 2023 forest product using a transferable logic, drastically reducing manual annotation effort. Comprehensive evaluations demonstrate the robustness of our approach: (1) Qualitative analyses reveal superior spatial detail and temporal consistency compared to existing global forest maps. (2) Rigorous quantitative validation based on ∼9000 reference samples confirms high and stable accuracy (∼92.4%) and recall (∼91.9%) over the 24-year period. (3) Furthermore, comparisons with government forestry statistics show strong agreement, validating the practical utility of the data. This work provides a valuable, accurate long-term dataset that forms a scientific basis for critical downstream applications such as ecological conservation planning, carbon stock assessment, and climate change research, thereby highlighting the transformative potential of multi-source data fusion and automated methods in advancing geospatial monitoring. Full article

The continued marginalisation of Indigenous knowledges and practices in environmental education sustains curricula and pedagogies grounded in Western worldviews. This exclusion reinforces limited or deficit-oriented perceptions of Indigenous cultures, environments, and epistemologies. Therefore, this study draws on the theory of critical consciousness to examine the need for Indigenous peoples and educators to become critically aware of the forces shaping their educational experiences and to use this awareness to transform their lives and teaching practices for a sustainable future. To illustrate how this transformation might occur, a qualitative study was conducted with ten Nigerian secondary school teachers who engaged with the design and implementation of a decolonisation model for environmental education. Findings show that seven participants successfully adopted the model, and several demonstrated notable shifts in their perspectives during the process. The study offers two key contributions: a conceptual framework for understanding decolonisation in environmental education and a practical decolonisation model for teachers. These contributions have broader relevance for educational reform and environmental education in countries with similar contexts to Nigeria and in marginalised communities in the Global North, where learners are often alienated from their local realities in favour of globalist perspectives. Full article

Digital transformation poses significant challenges to employee well-being, particularly in public administration, where hierarchical structures, increasing digitalization pressures, and high mental health-related absenteeism underscore the need to understand individual and job demands and resources. This study explores these aspects from the perspectives of employees and supervisors in public administration. Between September 2023 and February 2024, semi-structured interviews were conducted with eight employees and eleven supervisors from public administration organizations in Northern Germany and analyzed using deductive–inductive qualitative content analysis based on the Job Demands-Resources model. Identified individual resources included technical affinity, error tolerance, and willingness to learn, while key job resources involved early and transparent communication, attentive leadership, technical support, and counseling services, with most job resources linked to leadership behavior and work organization. Reported job demands comprised insufficient participation, inadequate planning, and lengthy procedures, whereas personal demands included fears and concerns about upcoming changes and negative attitudes toward transformation. The variation in perceived demands and resources highlights the individuality of the employees’ experiences. The findings provide initial insights into factors influencing psychological well-being at work during digital transformation, emphasizing the importance of participatory communication, employee involvement, leadership awareness of stressors, and competence development. Future research should employ longitudinal and interventional designs to improve causal understanding and generalizability. Full article

Background: Digital transformation is reshaping healthcare operations, with loyalty programs increasingly used to strengthen patient engagement and streamline administrative workflows. However, fragmented information systems and manual verification routines continue to create bottlenecks, inconsistencies, and extended lead times. Methods: This study applies a mixed-methods approach within the Business Process Management (BPM) lifecycle to redesign the eligibility verification process for a loyalty program at Casa de Saúde São Mateus Hospital. Quantitative time measurements were collected during peak periods, while qualitative insights from staff observations and discussions supported process discovery and bottleneck identification. The proposed solution integrates a centralized SQL database, automated verification routines, and a dedicated administrative interface synchronized with the MedicineOne system. Results: The redesigned process reduced eligibility verification time by approximately 80% and improved Flow Efficiency by around 11.7%. Manual interventions, data fragmentation, and discount-application errors decreased substantially. The centralized database improved data reliability, while automated checks enhanced consistency and reduced staff workload. The system also enabled more accurate beneficiary management and improved coordination across administrative activities. Conclusions: Integrating Healthcare 4.0 principles with BPM enhances internal logistics, reduces lead times, and improves operational reliability. The proposed model offers a replicable framework for modernizing healthcare service delivery. Full article

Background/Objectives: This systematic review examines how artificial intelligence (AI) is transforming fingerprint and latent print identification in criminal investigations, tracing the evolution from traditional dactyloscopy to Automated Fingerprint Identification Systems (AFISs) and AI-enhanced biometric pipelines. Methods: Following PRISMA 2020 guidelines, we conducted a literature search in the Scopus, Web of Science, PubMed/MEDLINE, and legal databases for the period 2000–2025, using multi-step Boolean search strings targeting AI-based fingerprint identification; 68,195 records were identified, of which 61 peer-reviewed studies met predefined inclusion criteria and were included in the qualitative synthesis (no meta-analysis). Results: Across the included studies, AI-enhanced AFIS solutions frequently demonstrated improvements in speed and scalability and, in several controlled benchmarks, improved matching performance on low-quality or partial fingerprints, although the results varied depending on datasets, evaluation protocols, and operational contexts. They also showed a potential to reduce certain forms of examiner-related contextual bias, while remaining susceptible to dataset- and model-induced biases. Conclusions: The evidence indicates that hybrid human–AI workflows—where expert examiners retain decision making authority but use AI for candidate filtering, image enhancement, and data structuring—currently offer the most reliable model, and emerging developments such as multimodal biometric fusion, edge computing, and quantum machine learning may contribute to making AI-based fingerprint identification an increasingly important component of law enforcement practice, provided that robust regulation, continuous validation, and transparent governance are ensured. Full article

Interactive science galleries have transformed how the public engages with science, shifting from object-centred displays to immersive, design-led experiences. This study situates these changes within broader cultural and economic contexts, exploring how design mediates our understanding of science and reflects neoliberal and experiential values. Using archival research, qualitative interviews with museum professionals, and reflective practice, the research examines the evolution of interactive science spaces at the Science Museum in London—The Children’s Gallery, Launch Pad, and Wonderlab. The findings reveal that exhibition design increasingly prioritises entertainment, immersion, and pleasure, aligning with the rise in the experience economy and the influence of corporate models such as Disneyland. While such strategies enhance visitor engagement and accessibility, they risk simplifying complex scientific narratives and reducing learning to consumption. The study concludes that effective science communication design should balance enjoyment with critical inquiry, using both comfort and discomfort to foster curiosity, reflection, and ethical awareness. By analysing design’s role in shaping the “science experience”, this research contributes to understanding how cultural institutions can create more nuanced, thought-provoking encounters between audiences, knowledge, and space. Full article

This work explores the qualitative dynamics of the modified unstable time-fractional nonlinear Schrödinger equation (mUNLSE), a model applicable to nonlinear wave propagation in plasma and optical fiber media. By transforming the governing equation into a planar conservative Hamiltonian system, a detailed bifurcation study is carried out, and the associated equilibrium points are classified using Lagrange’s theorem and phase-plane analysis. A family of exact wave solutions is then constructed in terms of both trigonometric and Jacobi elliptic functions, with solitary, kink/anti-kink, periodic, and super-periodic profiles emerging under suitable parameter regimes and linked directly to the type of the phase plane orbits. The validity of the solutions is discussed through the degeneracy property which is equivalent to the transmission between the phase orbits. The influence of the fractional derivative order on amplitude, localization, and dispersion is illustrated through graphical simulations, exploring the memory impacts in the wave evolution. In addition, an externally periodic force is allowed to act on the mUNLSE model, which is reduced to a perturbed non-autonomous dynamical system. The response to periodic driving is examined, showing transitions from periodic motion to quasi-periodic and chaotic regimes, which are further confirmed by Lyapunov exponent calculations. These findings deepen the theoretical understanding of fractional Schrödinger-type models and offer new insight into complex nonlinear wave phenomena in plasma physics and optical fiber systems. Full article

In the era of digital intelligence, cultural heritage is undergoing a profound transformation. This study investigates how digital technologies facilitate the inheritance and innovation of traditional culture in China, focusing on the case of Huangyan’s Song Rhyme Culture in Zhejiang Province. Drawing on the framework of “glocalized intelligent heritage,” the research explores how global technological systems interact with local cultural practices to produce new forms of cultural continuity. Methodologically, the study employs a qualitative case study approach supported by empirical data. It combines policy analysis, semi-structured interviews with twenty-six stakeholders, field observations, and quantitative indicators such as visitor statistics, online engagement, and project investment. This mixed design provides both contextual depth and measurable evidence of digital transformation. The findings show that digital intelligence has reshaped cultural representation, platform-based public engagement, and local sustainability. In Huangyan, technologies such as AI-based monitoring, 3D modeling, and VR exhibitions have transformed heritage display into an interactive and educational experience. Digital media have enhanced public engagement, with more than 1.2 million virtual visits and over 20 million online interactions recorded in 2024. At the same time, the project has stimulated cultural tourism and creative industries, contributing to a 28.6% increase in cultural revenue between 2020 and 2024. The study concludes that digital intelligence can function as a cultural bridge by strengthening heritage mediation, widening access, and enabling platform- and institution-based participation, while noting that embodied intergenerational cultural transmission lies beyond the direct measurement of this research design. Full article

Background: Differentiating bipolar disorder (BD) from unipolar major depressive disorder (MDD) at first episode is clinically consequential but challenging. Artificial intelligence/machine learning (AI/ML) may improve early diagnostic accuracy across imaging and non-imaging data sources. Methods: Following PRISMA 2020 and a pre-registered protocol on protocols.io, we searched PubMed, Scopus, Europe PMC, Semantic Scholar, OpenAlex, The Lens, medRxiv, ClinicalTrials.gov, and Web of Science (2014–8 October 2025). Eligible studies developed/evaluated supervised ML classifiers for BD vs. MDD at first episode and reported test-set discrimination. AUCs were meta-analyzed on the logit (GEN) scale using random effects (REML) with Hartung–Knapp adjustment and then back-transformed. Subgroup (imaging vs. non-imaging), leave-one-out (LOO), and quality sensitivity (excluding high risk of leakage) analyses were prespecified. Risk of bias used QUADAS-2 with PROBAST/AI considerations. Results: Of 158 records, 39 duplicates were removed and 119 records screened; 17 met qualitative criteria; and 6 had sufficient data for meta-analysis. The pooled random-effects AUC was 0.84 (95% CI 0.75–0.90), indicating above-chance discrimination, with substantial heterogeneity (I² = 86.5%). Results were robust to LOO, exclusion of two high-risk-of-leakage studies (pooled AUC 0.83, 95% CI 0.72–0.90), and restriction to higher-rigor validation (AUC 0.83, 95% CI 0.69–0.92). Non-imaging models showed higher point estimates than imaging models; however, subgroup comparisons were exploratory due to the small number of studies: pooled AUC ≈ 0.90–0.92 with I² = 0% vs. 0.79 with I² = 64%; test for subgroup difference Q = 7.27, df = 1, p = 0.007. Funnel plot inspection and Egger/Begg tests found that we could not reliably assess small-study effects/publication bias due to the small number of studies. Conclusions: AI/ML models provide good and robust discrimination of BD vs. MDD at first episode. Non-imaging approaches are promising due to higher point estimates in the available studies and practical scalability, but prospective evaluation is needed and conclusions about modality superiority remain tentative given the small number of non-imaging studies (k = 2). Full article

This study systematically reviews and re-evaluates the international competitiveness and economic sustainability of China’s solar photovoltaic (PV) industry. Based on the PRISMA protocol, it integrates both qualitative and quantitative evidence from 70 core English-language publications published between 2000 and 2025. An analytical framework is developed that combines keyword co-occurrence analysis, thematic clustering, and mechanism pathway mapping. The study identifies three key thematic domains: policy governance mechanisms, economic feasibility and cost structures, and the coupling between technological innovation and environmental performance. The findings reveal a transition in China’s PV development pathway—from early policy-driven expansion to the co-evolution of institutional adaptation and market mechanisms—highlighting the dynamic tension among multi-level variables. Four institutional dimensions and associated variable chains are proposed, uncovering long-term contradictions such as the reliance on subsidies versus structural efficiency and the strategic mismatch between national industrial strategies and global decarbonization goals. The study suggests that future research should prioritize system modeling, feedback mechanism identification, and the theoretical expansion of multi-level governance frameworks. In doing so, this review provides a reusable variable classification framework for analyzing green industrial transformation and offers policy insights for emerging economies engaged in global climate governance. Full article

Artificial intelligence (AI) has rapidly evolved from an experimental tool in spine research to a multi-domain framework that has significantly influenced imaging analysis, surgical decision-making, and individualized outcome prediction. Recent advances have expanded beyond isolated applications, enabling automated image interpretation, patient-specific risk stratification, discovery of qualitative phenotypes, and integration of heterogeneous clinical and biomechanical data. These developments signal a shift toward more comprehensive, context-aware analytic systems capable of supporting complex clinical workflows in spine care. Despite these gains, widespread clinical adoption remains limited. High internal performance metrics do not consistently translate into reliable generalizability, interpretability, or real-world clinical readiness. Persistent challenges, which include dataset heterogeneity, transportability across institutions, alignment with clinical decision-making processes, and appropriate validation strategies, continue to constrain widespread implementation. In this perspective, we synthesize post-2019 advances in spine AI across key application domains: imaging analysis, predictive modeling and decision support, qualitative phenotyping, and emerging hybrid and language-based frameworks through a unified clinical-readiness lens. By examining how methodological progress aligns with clinical context, validation rigor, and interpretability, we highlight both the transformative potential of AI in spine research and the critical steps required for responsible, effective integration into routine clinical practice. Full article