Search Results (3,249)

Chronic pain is increasingly understood as a multidimensional condition in which, in a substantial subgroup of patients, a protective symptom can evolve into a persistent maladaptive disorder of the nervous system, while in others it may remain closely tied to ongoing mechanical or structural factors. Central sensitization (CS) represents a key mechanism underlying this transition, characterized by enhanced neural responsiveness and impaired endogenous pain inhibition, leading to a dissociation between pain and tissue pathology. The aim of this narrative review is to critically discuss current evidence on CS as a mechanism-based explanation for persistent pain, using lumbar disk herniation (LDH) as a clinical model of the radiological-clinical mismatch, and to discuss its direct implications for identifying sensitized phenotypes, multimodal assessment, and rehabilitation strategies. A total of 77 sources published between 2006 and 2026 were synthesized. These reviewed sources demonstrate that identification of the sensitized phenotype requires a multimodal assessment approach combining self-report measures, such as the Central Sensitization Inventory (CSI), with psychophysical methods including quantitative sensory testing (QST) and conditioned pain modulation (CPM). Cognitive-emotional factors are also critical, as postoperative kinesiophobia affects approximately 38.3% of LDH patients and is associated with increased pain intensity and reduced self-efficacy. Management strategies reported in these publications focus on mechanism-based interventions, particularly pain neuroscience education (PNE) and graded, time-contingent exercise, which aim to modify pain-related cognitions and restore endogenous inhibitory processes. These approaches may be supported by adjunctive therapies, including dry needling (DN), electro-dry needling (EDN), centrally acting pharmacological agents (e.g., serotonin–norepinephrine reuptake inhibitors [SNRIs] and gabapentinoids), and psychologically informed treatments such as cognitive behavioral therapy (CBT). While surgical decompression may reduce CS-related symptoms, preoperative sensitization does not necessarily predict poorer outcomes, highlighting the interaction between peripheral and central mechanisms. Adopting a sensitization-informed perspective may encourage a broader integration of contemporary pain models alongside traditional structural views in lumbar disc herniation clinical care. Full article

Background/Objectives: Major Depressive Disorder (MDD) is a leading cause of disability, yet physical therapy (PT) is underrepresented in hospital-based psychiatric care. While exercise is a known adjunctive treatment, specific evidence on functional, task-oriented interventions in acute settings remains scarce. This study explored changes in quality of life, depressive symptoms, pain, and self-efficacy in patients with MDD following a specialized hospital-based PT program focused on functional movement and autonomy. Methods: We conducted a prospective pre–post case series in the Short-Stay Psychiatric Unit of the Royo Villanova University Hospital (Zaragoza, Spain). We recruited seven adult patients with MDD via convenience sampling. The intervention consisted of a group-based PT program (two 45 min sessions/week during the hospital stay) utilizing task-oriented functional exercises targeting progressive strength, balance, and motor control designed to enhance self-efficacy through activities of daily living (ADLs), combined with health education. Outcomes included the EQ-5D-3L (quality of life), MADRS (depression), NRS (pain), GSE (self-efficacy), and GCPC-UN-ESU (satisfaction). Results: All seven participants (100%) exhibited a positive upward trend in self-perceived health status via the EQ-VAS (mean increase of 35 points). Six cases (85.7%) showed preliminary positive trends in the anxiety/depression dimension of the EQ-5D-3L, with the mean Single Index Value increasing from 0.310 to 0.683. Reductions in depressive symptom severity were observed in six participants, with several transitioning toward moderate or mild levels. Additionally, four patients reported descriptive reductions in pain intensity and showed favorable shifts in self-efficacy scores. Six participants expressed high satisfaction with the intervention. Conclusions: Integrating a hospital-based functional PT program with standard care may offer preliminary benefits for quality of life and reduce depressive symptoms in MDD patients. These findings suggest that task-oriented PT presents a feasible complementary approach for acute psychiatric admissions, although larger controlled trials are needed to confirm these exploratory results. Full article

This study presents and pilot-tests the Biomimetic Design Education Framework, a structured pedagogical model developed to systematize the translation of biological knowledge into furniture design within studio-based educational contexts. Positioned as a pilot implementation, the study introduces the Biomimetic Transfer Matrix as an accompanying analytical tool for assessing the depth of biological knowledge integration in student design work. It is based on 18 student projects developed during a furniture design course, assessed through qualitative content analysis. The projects were evaluated according to four types of biomimetic transfer: formal, structural, mechanical, and functional/behavioral. Results reveal that structural transfer was the most prevalent category (38.9%), followed by functional/behavioral transfer (33.3%), formal transfer (16.7%), and mechanical transfer (11.1%). This distribution indicates that structured pedagogical guidance can successfully direct students beyond surface-level morphological imitation toward deeper principle-based biological abstraction, while also identifying mechanical and system-based transfer as areas requiring targeted curricular development. On this basis, the study presents the Biomimetic Design Education Framework and introduces the Biomimetic Transfer Matrix as an analytical tool for examining different levels of biomimetic knowledge transfer in design. Results underline the importance of structured approaches to support deeper levels of biological abstraction in design education. The findings contribute to SDG 4 (Quality Education) by advancing evidence-based approaches to biomimetic design instruction. Full article

Background/Objectives: Adolescent mental health problems have increased in recent years, with growing concern about the impact of digital behaviors such as problematic video game use and gambling. Internet Gaming Disorder (IGD) and Problem Gambling Symptoms may share psychological risk markers, but evidence in Spanish adolescents is limited. This study aimed to examine the relationship between IGD, problem gambling symptoms, and mental health, and to identify sociodemographic, psychological, and behavioral factors associated, including microtransactions and loot boxes. Methods: A cross-sectional study was conducted with secondary education students from Extremadura (Spain). The final sample included 343 participants. Measures included an ad hoc questionnaire on video game use, the IGDS9-SF, SOGS-RA, and the Strengths and Difficulties Questionnaire (SDQ). Descriptive analyses, Spearman correlations, and multivariable regression (Poisson and negative binomial) were performed. Results: IGD and gambling were positively correlated (Spearman’s ρ = 0.386, p < 0.001) and associated with higher mental health difficulty scores (IGD: ρ = 0.299, p < 0.001; gambling: ρ = 0.214, p < 0.001). Male gender was associated with both outcomes (IGD: incidence rate ratio [IRR] = 1.21 [95% Confidence Interval: 1.13–1.30]; gambling: IRR = 2.90 [1.85–4.60]). Microtransactions were associated with both behaviors (IGD: IRR = 1.17 [1.09–1.25]; gambling: IRR = 1.74 [1.19–2.54]), while loot box use was related only to IGD (IRR = 1.13 [1.05–1.21]). Total SDQ score was positively associated with both IGD (IRR = 1.02 [1.02–1.03]) and gambling (IRR = 1.10 [1.06–1.13]). Younger age was associated with higher IGD scores (IRR = 0.97 [0.96–0.99]). Conclusions: There are similarities in the associations among the examined factors and increased scores of IGD and gambling in adolescents, particularly male gender, higher mental health difficulties scores, and involvement in monetized gaming systems. School-based, family, and public health prevention strategies may benefit from addressing the importance of psychological well-being and increase awareness of the potential risks associated with digital gaming practices. Full article

Background/Objectives: Cerebral palsy (CP) hip displacement requires longitudinal surveillance, frequently imposing significant burden on caregivers. While Multimodal Large Language Models (MLLMs) offer a potential solution to the health literacy gap, their accuracy in interpreting pediatric pelvic radiographs remains unproven. This study evaluates the effectiveness and safety of MLLMs in addressing caregiver concerns regarding CP hip management. Methods: Fifteen deidentified pediatric pelvic radiographs representing a spectrum of hip displacement severities were processed through three MLLMs: GPT-4o, Claude 3.5, and Gemini 1.5 Pro. Nine standardized caregiver prompts (n = 95 total responses per model) were utilized to simulate common clinical queries. Outcome measures included response word count, interactive characteristics, frequency of medical disclaimers, and diagnostic accuracy. Results: Quantitative analysis revealed that Claude 3.5 produced significantly shorter responses compared to other models (p < 0.01). GPT-4o demonstrated the highest safety alignment, with a 96.9% disclaimer rate, significantly exceeding Claude (60.0%) and Gemini (76.8%) (p = 0.03). Diagnostic “hallucinations” were observed, notably Claude misidentifying non-operative cases as bilateral hip replacements. While management recommendations were clinically relevant, they remained generic rather than patient-specific, failing to measure or apply migration percentage thresholds. Encouragingly, all models consistently directed users to consult an orthopaedic surgeon. Conclusions: MLLMs represent an opportunity to enhance health literacy by providing accessible management summaries and emphasizing professional consultation. However, significant radiographic hallucinations and a lack of specific, evidence-based guidance preclude their use as standalone diagnostic tools. Currently, MLLMs should be viewed as educational adjuncts requiring expert oversight in the pediatric orthopaedic care continuum. Full article

The development of digital learning technologies has introduced innovative tools to enhance science and chemistry education, including PhET simulations. This study aims to evaluate the effectiveness of PhET simulations on students’ learning outcomes through a systematic literature review following the PRISMA 2020 guidelines. A systematic search of Scopus and Crossref databases was conducted (last search: January 2026) using predefined keywords. Eligible studies were empirical research published between 2020 and 2026 that investigated PhET simulations in science-related education and reported learning outcomes, while non-empirical studies and non-Scopus-indexed articles were excluded. Risk of bias was assessed using an adapted Joanna Briggs Institute critical appraisal tool. Due to heterogeneity in study designs and outcome measures, the results were synthesized using a narrative approach. A total of 14 studies across elementary to higher education levels were included. The findings indicate that PhET simulations consistently improve learning outcomes, particularly academic achievement and conceptual understanding, with effects generally favoring simulation-based instruction over traditional methods. However, higher-order skills and affective outcomes such as motivation and attitude remain less frequently investigated. The evidence is limited by variability in study designs, incomplete reporting of non-cognitive outcomes, and the absence of quantitative synthesis. Overall, PhET simulations demonstrate strong potential as an effective interactive learning medium, although their impact depends on instructional design, teacher facilitation, and technological accessibility. Full article

Background: AI-supported game-based learning (AI-GBL) integrates artificial intelligence mechanisms, including adaptive difficulty adjustment, large language model (LLM) scaffolding, intelligent non-player characters (NPCs), and stealth assessment, into game-based educational environments. Objective: This systematic review synthesizes the empirical evidence on AI-GBL effectiveness, adaptive mechanisms, and intelligent assessment approaches across diverse educational contexts. Method: Following PRISMA 2020 guidelines, 55 peer-reviewed empirical studies (2021–2026) were identified from Web of Science and Scopus databases. Two independent reviewers screened records (κ = 0.89; 100% consensus on disagreements), extracted data using a standardized coding scheme, and assessed methodological quality using a five-criterion rubric. A thematic synthesis approach was adopted due to the heterogeneity of the evidence base. Results: The reviewed studies generally suggest promising positive effects of AI-GBL on knowledge acquisition, intrinsic motivation, and affective engagement under a range of educational conditions. LLM-based scaffolding reduces cognitive load but risks fostering passive dependency; adaptive difficulty adjustment benefits depend critically on the direction and magnitude of adaptation; AI NPCs function as credible instructional partners in both EFL and STEM contexts; stealth assessment achieves AUCs of 0.848–0.913. Challenges include algorithmic bias in assessment models, LLM latency, over-reliance risks, and a near absence of longitudinal evidence. Conclusions: AI-GBL’s effectiveness rests on principled alignment between AI mechanisms and learning theory rather than algorithmic sophistication per se. Equity-by-design approaches and longitudinal evidence constitute the field’s priority research needs. Full article

Background/Objectives: Artificial Intelligence (AI) integration in healthcare promises improved diagnostic accuracy, patient safety, and operational efficiency. However, AI acceptance among healthcare workers remains limited due to knowledge gaps, risk concerns, and governance challenges, particularly in developing countries like Saudi Arabia, where rapid healthcare modernization faces unique infrastructure, organizational, and cultural challenges. This research investigates the factors influencing AI acceptance among medical practitioners, nurses, administrators, and students in Saudi Arabian hospitals to identify key determinants and barriers to adoption. Methods: This cross-sectional study employed an extended Unified Theory of Acceptance and Use of Technology (UTAUT) framework integrated with ethical considerations from the Model for Ethical Assessment and Analysis of AI in Medicine (MEAAM). A structured bilingual questionnaire was administered to 119 healthcare professionals and students across Saudi Arabia, measuring constructs including Awareness and Knowledge, Performance Expectancy, Effort Expectancy, Facilitating Conditions, Social Influence, Trust, Perceived Risk, Ethical Governance, and Price Value. Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed for quantitative analysis, supplemented by thematic analysis of open-ended qualitative responses. Results: The PLS-SEM analysis explained 59.8% of variance in behavioral intention to adopt AI (R² = 0.598). Awareness and Knowledge emerged as the strongest predictor (β = +0.505, p < 0.001), followed by Performance Expectancy (β = +0.229, p < 0.05) and Social Influence (β = +0.123). Perceived Risk functioned as the primary barrier (β = −0.185, p < 0.05). Qualitative findings identified infrastructure gaps, regulatory ambiguities, and training deficiencies as major implementation barriers, while emphasizing opportunities in diagnostic accuracy and remote monitoring. Conclusions: AI acceptance in Saudi healthcare is primarily driven by knowledge, with perceived usefulness and peer support as secondary facilitators, while safety and accountability concerns remain substantial obstacles. Successful AI integration requires coordinated efforts in education, transparent governance frameworks, and institutional support. This study contributes theoretically by validating extended UTAUT in a non-Western healthcare context and practically by providing evidence-based strategies for sustainable AI adoption that enhance healthcare quality while respecting professional roles and ethical principles. Full article

Background/Objectives: Chronic diseases account for 74% of global deaths, with multimorbidity (existence of more than one chronic condition) increasing disability risk and treatment burden, leading to poor adherence, disease progression, and reduced quality of life. Nursing-led proactive care models that focus on patient engagement, education, and self-care can help mitigate these challenges. The study aims to evaluate the effectiveness of a nurse-led proactive health intervention in improving care for individuals with chronic diseases in general practice. Methods: A quasi-experimental pre–post study will be conducted in a Community Health Home in Northern Italy. Family and community nurses will deliver the intervention, which includes assessments, educational sessions, and follow-ups for patients aged 65+ with at least one chronic condition. Recruitment will occur over three months. Results: Primary outcomes include emergency department visits and hospitalizations, while secondary outcomes focus on medication adherence, self-care, and service utilization. Data will be collected at 6 and 12 months, and statistical analysis will use descriptive methods and generalized estimating equations (GEEs). Conclusions: This study will improve the understanding of the value of nurse-led proactive intervention, filling the gap in the literature by testing evidence-based approaches on a realistic frail population. Moreover, delivering a complex but structured intervention will provide evidence for future interventions to reduce treatment burden and improve health outcomes. Full article

Background/Objectives: Health literacy (HL) is recognized as an important social determinant of health. It supports healthy behaviors and effective health management throughout one’s life. For children and adolescents, developing HL influences their well-being, development, and ability to make informed health decisions. Nurses are strategically positioned to promote HL from an early age. To our knowledge, no prior synthesis has specifically examined nurse-led HL interventions targeting pediatric populations, highlighting the originality and relevance of this scoping review. The purpose of this review was to map and characterize nursing interventions aimed at improving HL outcomes in children and adolescents. Methods: A scoping review was conducted according to the Joanna Briggs Institute methodology, using a three-step search strategy, and reported in accordance with the PRISMA-ScR guidelines. Searches were conducted in MEDLINE, CINAHL, Scopus, Web of Science, and ProQuest with no date restriction, including studies published in Portuguese, English, or Spanish. Studies involving children and adolescents (ages 0–18) in any healthcare or community setting were eligible. Data on intervention characteristics and HL outcomes were extracted and analyzed descriptively, and no critical appraisal of the included sources was conducted. Results: A total of 44 studies were included. Interventions were predominantly school-based and focused on adolescents (n = 26), with a clear gap in early childhood (n = 2). Studies of early childhood primarily used storytelling and reading activities, whereas interventions targeting older children and adolescents more often employed participatory educational strategies, group-based approaches and digital platforms. The most frequently addressed topics were chronic disease management (n = 12), mental health (n = 7), and nutrition (n = 5). HL domains mainly focused on healthcare and health promotion, with fewer studies addressing disease prevention. Most interventions were conducted in school settings (n = 24), highlighting this context over those in primary care, community, and hospital settings. Conclusions: The results revealed nursing interventions used to promote HL, particularly in the management of chronic diseases, mental health and nutrition. However, the existing body of research is still limited. Key gaps include the absence of standardized measurement tools and the scarcity of longitudinal studies evaluating long-term outcomes. These limitations constrain the comparability and generalizability of findings, highlighting the necessity of more rigorous, methodologically robust research to support evidence-based practices. This scoping review comprehensively maps nurse-led interventions that promote HL among children and adolescents, identifying key priorities to guide future research in this area. Full article

Background: Cancer-related cognitive impairment is a frequent and clinically relevant concern among women with breast cancer, particularly during active oncological treatment, with potential consequences for memory, attention, executive functioning, daily autonomy, emotional well-being, and quality of life. This study aims to evaluate the effectiveness of the Playful Attention and Active Memory intervention (ALMA) on cognitive functioning in women with breast cancer undergoing active oncological treatment. Methods: This single-centre, three-arm, parallel-group randomized controlled trial at the University Healthcare Complex of Salamanca (Spain) will evaluate 63 women with breast cancer undergoing active oncological treatment. Participants will be randomized (1:1:1) into a health education control group, an individual non-tailored cognitive training group, or the ALMA multidimensional group intervention (two 120 min face-to-face sessions/week for four months, combining psychoeducation, targeted cognitive stimulation, and group feedback). Assessments will occur at baseline and post-intervention. The primary outcome is objective global cognitive performance (Montreal Cognitive Assessment). Secondary outcomes include perceived cognitive function, everyday cognition, functional autonomy, anxiety, sleep quality, performance status, and everyday memory failures. Intention-to-treat analysis using linear mixed models will perform prespecified comparisons of ALMA versus both other groups. Expected results: This study is designed to provide evidence on the potential value of a structured, multidimensional cognitive intervention delivered during active breast cancer treatment. By comparing ALMA with both health education and individual cognitive training, the trial may clarify whether the integration of psychoeducation, ecological cognitive stimulation, and group-based support offers additional benefits beyond cognitive practice alone. The inclusion of objective, subjective, and functionally oriented outcomes strengthens the clinical relevance of the protocol and may contribute to the development of more comprehensive supportive care strategies for cancer-related cognitive impairment. Trial registration: This protocol is registered at ClinicalTrials.gov under the identifier NCT07165912. Full article

Post-industrial waterfront regeneration can improve environmental quality and public space, but it may also produce green gentrification and unequal access to regeneration benefits. To support socially responsive planning evaluation, this study examines how green gentrification is spatially manifested and how residents perceive and support waterfront green space development in Shanghai’s Yangpu Riverside. A sequential mixed-methods design combines census, housing price, and green space data from 2000 to 2020 with 317 resident questionnaires. The study identifies socio-spatial changes associated with green gentrification, cross-culturally adapts and validates the Gentrification Worldview Instrument (GWI), and examines the associations among gentrification worldviews, place attachment, and support for green space development. Results show no statistically significant relative acceleration in housing price growth in near-waterfront neighborhoods during the regeneration period, but reveal an expanding housing price premium, educational upgrading, and population decline. These patterns are consistent with a spatially differentiated tendency toward green gentrification embedded in the broader state-led waterfront regeneration process, rather than demonstrating an independent effect of greenbelt construction. The Chinese-adapted GWI retains the three dimensions of neighborhood preservation, development support, and social integration. Among surveyed residents, development support and place identity are positively associated with support for waterfront green space development, whereas neighborhood preservation is negatively associated with support. The results further indicate a statistical mediation pattern in which place identity forms a significant indirect association between development support and support for green space development. The findings provide an evidence-based framework for evaluating inclusive waterfront regeneration and suggest that planning and design should integrate green space accessibility, local memory, residents’ perceptions, and social equity. Full article

Engineering computer graphics education frequently exhibits a gap between procedural CAD software (e.g. CATIA 2022) training and the strategic engineering reasoning required by industrial practice. This paper documents a holistic redesign of two advanced CAD courses. The study is framed within the Scholarship of Teaching and Learning (SoTL) tradition as a practitioner-led reflective case study. The redesign integrates four pedagogical mechanisms within an enterprise-CAD context: authentic problem-based learning, dual-layered asynchronous video scaffolding, software-agnostic heuristics (including pre-modelling cognitive mapping), and cognitive apprenticeship. The analysis triangulates three institutional data sources: quantitative Course Experience Survey indicators, qualitative student response themes, and twelve consecutive years of cohort-level academic performance records (2013–2024). The 2022 intervention iteration coincided with a marked elevation in academic performance. Grades reached approximately two standard deviations above the historical baseline. Concurrently, qualitative themes highlighted perceived industrial relevance and platform-portable confidence. However, performance in the post-intervention iterations (2023 and 2024) partially regressed. While scores remained above the historical mean, they did not sustain the 2022 peak. This pattern indicates partial sustainment, rather than evidence of a stable or definitive sustained pedagogical effect. This case is reported as descriptive rather than inferential. While the observed patterns align strongly with theoretical predictions, they do not establish definitive causal effects. Ultimately, the primary contribution of this study lies in documenting the integrated operationalization of these four mechanisms. Furthermore, it highlights longitudinal pedagogical sustainability as a critical, under-examined dimension that single-iteration evidence systematically obscures. Full article

Twice-exceptional students—those who are both gifted and have one or more disabilities—and gifted learners, more broadly, represent persistently underserved populations within educational systems. Gifted learners frequently encounter provision that does not adequately engage their potential, such as standardised approaches that neither recognise nor respond to their learning requirements. Traditional identification and programming approaches often rely on deficit-based approaches that pathologise neurodivergence and frequently neglect the complex, asynchronous learning profiles characteristic of twice-exceptional students. This article advances a functional alignment framework proposing that generative artificial intelligence’s processing patterns may align with the cognitive characteristics of some gifted and twice-exceptional learners. The proposed functional alignment spans five dimensions: conceptual movement, knowledge integration, topic continuity, working memory, and pacing and temporal flexibility; this positions GenAI as a potentially compatible interactive platform for personalised, strengths-based learning. The functional alignment framework is explicitly theoretical, advancing propositions rather than demonstrated effects, and requires empirical validation. Positioning GenAI as a mediating platform has the potential to disrupt longstanding barriers to evidence-informed educational provision for gifted and twice-exceptional students. Through examining the intersection of gifted education, special education, and educational technology, this theoretical work outlines a trajectory for the field, characterised by flexible, personalised, strengths-based approaches that can be responsive to the student in front of the teacher, instead of the all-too-often default to one-size-fits-all approaches. Critical considerations of equity, teacher capability, and ethical implementation are addressed, theorising that GenAI’s transformative potential may only be realised through deliberate, theoretically informed application grounded in deep understanding of learner neurodivergence and a proposed pivot from GenAI literacy to GenAI fluency. This work contributes to reconceptualising gifted education as inherently inclusive, responsive, and oriented towards actualising potential for gifted and twice-/multi-exceptional learners. Full article

This study examines the development and pedagogical impact of an AI-based pedagogical agent designed for modern e-learning environments. The research addresses a key challenge in digital education: the lack of personalization and immediate feedback in traditional e-learning systems. AI-driven agents “support and motivate learners through instructional interaction” and provide adaptive, data-driven learning experiences that surpass the limitations of rule-based systems. The study begins with a systematic literature review following PRISMA 2020, analyzing 46 publications from 2020 to 2025 to identify current AI architectures, pedagogical roles, and the empirical evidence of learning impact. The findings highlight the growing use of machine learning, deep learning, multimodal analytics, and large language models in educational agents. These systems perform roles such as tutor, coach, evaluator, dialogue partner, and consultant, offering cognitive, metacognitive, emotional, and analytical support. Modern agents “continuously monitor user interaction, analyze engagement, and adapt learning content”, enabling highly personalized learning pathways. The study also presents the design of a multimodal pedagogical agent capable of explanation, task generation, diagnostics, and adaptive feedback. Experimental results with students (n = 20) show improved performance, reduced errors, and higher engagement when learning with the agent. Overall, the research demonstrates that AI-based pedagogical agents enhance learning effectiveness and support autonomous learning in higher education. Full article