Search Results (569)

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

This study explores the multifaceted attitudes of high school students toward the use of artificial intelligence (AI) and large language models (LLMs) like ChatGPT in educational contexts. Drawing upon a tripartite model of attitudes, our research evaluates affective, cognitive, and behavioral dimensions to offer a nuanced understanding of students’ perceptions. The affective dimension assesses emotional responses to AI tools, the cognitive dimension examines beliefs about the utility and ethical considerations of AI, and the behavioral dimension evaluates actual usage patterns of AI technologies. Utilizing a newly developed survey instrument tailored for the educational context, data was collected from 93 high school students across different regions of Italy in the period that ranged from February 2024–March 2024. Exploratory factor analysis (EFA) was employed to explore the underlying structure of the survey instrument and identify underlying factors influencing AI acceptance. The analysis reveals three distinct factors—Mindful AI Learning, Embracing AI Effects, and LLM as Learning Companion, highlighting the complexity of students’ attitudes toward AI. Results indicate a cautious but optimistic reception of AI in education, offering crucial insights into Information Intelligence for enhanced learning and the design of personalized learning pathways. The study contributes to the literature by offering a novel scale to measure attitudes toward artificial intelligence, specifically focusing on both general AI and Generative AI large language models, such as ChatGPT. Moreover, it highlights the critical need for AI literacy, ethical digital learning frameworks, and robust institutional policies to bridge the digital divide. Consequently, this work is framed as a preliminary exploratory investigation. Ultimately, these findings advance our knowledge of transformative digital learning processes and inform future strategies for human–machine integration in educational systems. Full article

Research on twice-exceptional students, particularly those with co-occurring intellectual giftedness and dyslexia, remains limited and conceptually fragmented. This study examines the reading- and writing-related profiles of these students by comparing three groups: gifted students without dyslexia (G), gifted students with dyslexia (G-D), and dyslexic students without intellectual giftedness (D). The sample consisted of 133 Spanish-speaking primary school students (Grades 3–6). The results revealed a distinct and non-linear performance pattern. G-D students exhibited marked difficulties in lower-level literacy processes, including phonological and lexical processing, with a performance pattern closer to that of dyslexic peers. However, they showed relative strengths in higher-order language abilities, particularly text comprehension, oral comprehension, and written composition. The findings suggest a non-uniform profile of reading- and writing-related abilities in these students, characterized by weaknesses in several lower-level literacy processes and relative strengths in some higher-order language abilities. This pattern may contribute to the underidentification of these students across educational and clinical contexts. By providing empirical evidence from Spanish, a relatively underexplored orthographic context, this study contributes to current models of twice-exceptionality and highlights the need for more sensitive and staged identification procedures, as well as multidimensional assessment and intervention approaches that address both strengths and weaknesses. Full article

Background/Objectives: Lung cancer is a leading cause of cancer-related mortality worldwide. As patients increasingly utilize large language models (LLMs) for health information, evaluating the readability and patient-centeredness of these tools is critical. This study aims to compare the performance of ChatGPT-4o mini, Microsoft Copilot, and Google Gemini in providing lung cancer information, focusing on their utility for individuals with limited health literacy. Methods: In this cross-sectional study (March 2026), 30 responses to ten standardized lung cancer-related queries were analyzed. Outputs were assessed using JAMA benchmarks and mDISCERN for quality, the SMOG index for readability, and PEMAT-P for understandability and actionability. Inter-rater reliability was analyzed using intraclass correlation coefficients (ICCs). Results: ChatGPT-4o mini demonstrated superior readability, achieving a sixth-grade level (SMOG: 6.23 ± 0.72, p < 0.001). Gemini achieved higher JAMA scores, indicating stronger academic rigour. While PEMAT-P scores were highest for ChatGPT (63.7%), all models exhibited moderate mDISCERN quality. Inter-rater reliability was excellent for JAMA (ICC = 1.000) and PEMAT-P (ICC = 0.883), though moderate for mDISCERN (ICC = 0.365), reflecting inherent interpretative subjectivity in qualitative assessment. No hallucinations were observed. Conclusions: Current LLMs exhibit a trade-off between accessibility and academic rigour: ChatGPT favours patient-friendly readability, while Gemini emphasizes structured content. The observed inter-rater variability in mDISCERN underscores the complexity of standardizing qualitative AI evaluation. These findings suggest that LLMs function best as complementary aids rather than substitutes for physician-led communication. Full article

Persistent disparities in literacy outcomes affect deaf learners, who may experience writing instruction that does not align with their linguistic contexts. This study examined how teachers’ instructional reasoning about writing developed during participation in an online Strategic and Interactive Writing Instruction (SIWI) professional development (PD) program. Nineteen teachers of deaf students completed a 30-hour virtual PD that combined asynchronous modules and synchronous collaborative sessions focused on evidence-based writing instruction. Teachers completed video-based situational simulations at three time points across the PD; responses were scored using a 5-point holistic scale to assess growth in pedagogical content knowledge. A post-workshop survey also asked teachers to rate prior use, anticipated implementation, and readiness to implement SIWI-aligned practices on a 3-point scale. Survey results indicated relatively low pre-workshop use of practices and higher anticipated implementation and readiness after PD. Repeated-measures analyses of simulation scores indicated significant improvement over time, reflecting strengthened ability to identify instructional priorities, integrate language and writing instruction, and justify responsive teaching decisions. To illustrate what this growth looked like in practice, the manuscript includes an embedded illustration of one teacher’s scenario responses across the three time points, showing a shift from more general/imprecise instructional commentary to more SIWI-aligned, objective-driven reasoning that explicitly links language supports to targeted writing instruction and next instructional steps. These findings suggest that video-based simulations offer a feasible, practice-oriented way to assess teacher learning in online PD, and that programs preparing teachers of deaf writers should pair self-report measures with simulation-based tasks that document how teachers apply pedagogical content knowledge to writing instruction. Full article

Spanish–English bilingual learners in U.S. dual language and bilingual education programs develop Spanish orthographic competence while receiving literacy instruction across two writing systems that differ in phonological transparency, orthographic depth, and grammatical marking. This study examined experts’ perceptions of the clarity, instructional coherence, pedagogical relevance, applicability, and refinement priorities of a pedagogical framework for Spanish orthographic development in contexts where Spanish is used as a language of instruction and literacy. The framework conceptualizes Spanish orthographic decision-making as the coordinated activation of phonological mapping, orthographic–grammatical reasoning, and visual–lexical retrieval within biliteracy development. Using a qualitative evaluative design, the study analyzed open-ended questionnaire and interview data from 44 experts in bilingual education and Spanish literacy-related fields. Findings show broad convergence regarding the framework’s clarity, instructional coherence, and relevance for bilingual contexts. Participants emphasized pre-dictation preparation, explicit metalinguistic analysis, visual-memory activation and retrieval routines, and cross-linguistic comparison between Spanish and English. They also identified refinement priorities, including classroom-ready examples, clearer articulation of error and autocorrection, and stronger integration with reading, writing, and oracy practices. This study positions Spanish orthographic instruction as a cognitively guided biliteracy practice and identifies design principles for strengthening orthographic, metalinguistic, and cross-linguistic instruction in bilingual programs. Full article

Orthographic depth varies across alphabetic writing systems and plays a central role in spelling acquisition. In immersion education, a second language (L2) is used as a language of instruction for part of the curriculum, such that learners are primarily confronted with its writing system during the initial stages of literacy development. This early exposure may shape the spelling strategies subsequently deployed in the first language (L1), which also corresponds to the dominant language of the surrounding community. This article provides a structured review of key mechanisms involved in spelling acquisition, orthographic depth, and cross-linguistic influence in bilingual and immersion contexts. On this basis, it proposes a conceptual and predictive framework specifying how the orthographic depth of the instructional language modulates spelling strategies and spelling error profiles in L1. Focusing on French-speaking pupils enrolled in immersion programmes with L2s characterised by either predominantly phonemic or opaque orthographies, the framework integrates strategy-based models of orthographic development. The model distinguishes phonological, lexical, and morphographic components of orthographic knowledge and predicts that immersion in phonemic-dominant orthographies favours phonographic dominance and regularisation patterns, whereas immersion in opaque orthographies promotes greater reliance on lexical–orthographic strategies, resulting in distinct and systematic spelling error profiles in French. Full article

Aligning information technology (IT) workforce demands with educational assessments is essential for bridging skills gaps; yet, no prior corpus maps IT task reasoning to Programme for International Student Assessment (PISA) proficiency levels. This paper introduces a large language model (LLM)-powered framework aligning IT competencies with PISA 2022 and the OECD (Organisation for Economic Co-operation and Development) Learning Compass 2030, drawing on O*NET v30.2 (Occupational Information Network), ESCO (European Skills, Competences, Qualifications, and Occupations) v1.2.1, PISA descriptors and OECD definitions. The framework operates in two stages: Stage 1 aligns 562 IT task statements with minimum PISA 2022 proficiency levels via LLM annotation and cross-model validation; and Stage 2 extends this mapping to the OECD Learning Compass 2030 through the semantic clustering of task embeddings and a bidirectional gap analysis of 95 ESCO transversal skills. Using Gemini 2.5 Flash, 562 tasks are annotated with minimum PISA levels across Mathematical, Reading, and Science literacy (first stage). Annotation reliability is assessed through a five-model cross-validation against a blind human domain expert (treated as a reference benchmark, not a gold standard) on a stratified 100-task sample (17.8% of the corpus), with agreement ranging from fair (Gemini 2.5 Flash, κ = 0.29) to moderate (Claude Haiku 4.5, κ = 0.50; LLaMA 3.3 70B, κ = 0.44). A bias-correction sensitivity analysis confirms that distributional findings remain stable after accounting for the primary annotator’s systematic overestimation, and OLS-calibrated alignment against O*NET ability ratings provides directional plausibility support. Validated tasks are embedded and clustered into 25 technical profiles via K-Means, each classified against OECD dimensions. The framework is extended to 95 ESCO transversal skills in 24 clusters. Bidirectional analysis reveals that, while every PISA proficiency level is engaged by at least one transversal cluster, 33% of these clusters, covering creative, ethical, social–emotional, and dispositional competencies, fall entirely outside PISA’s cognitive scope. This boundary mapping identifies where the PISA-based alignment is valid and where complementary tools are required for a full readiness assessment. Full article

Background: Large language models and other generative artificial intelligence (AI) tools are increasingly being embedded in digital healthcare services, including mobile health applications, telerehabilitation, remote monitoring, and hybrid care pathways. In this review, digital healthcare refers to technology-mediated healthcare services in which digital platforms, mobile applications, wearables, remote communication, and AI-enabled interfaces support health assessment, self-management, rehabilitation, clinical decision support, or service delivery. When AI-generated exercise guidance moves from general education to individualized recommendations about dose, progression, contraindications, or rehabilitation, it may become directly actionable and safety-relevant. Objectives: This review aimed to clarify when AI-generated exercise guidance in digital healthcare may warrant safety-relevant governance attention and to outline implementation considerations for explainability, human oversight, and service-level governance. It addresses a gap in the literature: general AI-governance and exercise-prescription discussions rarely specify how point-of-use explanations, review thresholds, and escalation safeguards can be organized for directly actionable AI exercise guidance. Methods: We conducted a governance-oriented narrative review of peer-reviewed literature and representative regulatory or guidance documents. This review was not designed as a systematic review, scoping review, or exhaustive evidence map; transparent source mapping was used to support conceptual synthesis. Searches and source mapping focused on generative AI, large language models, explainable AI, clinical decision support, digital health, mobile health, exercise prescription, rehabilitation, trust, automation bias, and human oversight. Sources were included when they informed the safety, explainability, governance, or real-world implementation of patient-facing AI-generated exercise guidance. Extracted material was grouped by evidentiary role and synthesized through framework synthesis and governance mapping to distinguish literature-supported observations, author interpretation, and proposed implementation tools. Results: The included sources were first organized into five thematic groups: digital exercise delivery and exercise-prescription evidence; explainability, trust, and automation bias literature; professional responsibility, ethics, and patient disclosure literature; regulatory and policy documents; and digital literacy, patient/clinician attitudes, and equity literature. The synthesis then proceeded from safety relevance to explanation needs, human oversight and escalation needs, and selected regulatory and policy signals before translating these strands into conceptual and implementation-oriented outputs rather than empirically validated instruments. AI-generated exercise guidance was most safety-relevant in scenarios involving individualized dose, progression, contraindication-sensitive action, or rehabilitation strategy. Across the included sources, generic transparency alone was not sufficient to support reviewable use; relevant explanation elements included evidence sources, risk warnings, reasoning paths, and reasonable alternatives. Oversight considerations varied with embodied risk, clinical ambiguity, user vulnerability, and likelihood of direct enactment. Implementation considerations linked interface design, clinical review, escalation, auditability, and post-deployment monitoring. Conclusions: AI-generated exercise guidance in digital healthcare may warrant governance attention as a patient-safety and accountability issue when it influences actionable exercise decisions. The proposed framework offers a conceptual basis for designing more reviewable and accountable mobile and remote exercise-support services. Future work can validate these outputs in patient-facing services, clinician review workflows, usability studies, implementation pilots, and safety evaluations. Full article

Friar Hernando de Talavera can be considered the main strategist of evangelisation at the end of the 15th century in the recently conquered Kingdom of Granada. To this end, he used the publication Breve y muy provechosa doctrina de lo que debe saber todo cristiano together with eight other very useful treatises [Brief and Very Useful Doctrine of What Every Christian Should Know, with Eight Other Very Useful Treatises], which he accompanied with his Instrucción a los vecinos del Albaicín [Instruction to the Residents of the Albaicín]. Successive editions of the catechism and some books included under the generic title Breve y muy provechosa doctrina [A Brief and Very Useful Doctrine] throughout the 16th century demonstrated its doctrinal soundness and pastoral effectiveness. Furthermore, they were later used not only for catechesis but also for literacy in the Kingdom of Granada and in the early days of the American conquest. The study will systematically present the different editions and their intentions from the Granada incunabulum to the present day. The texts composed by the first archbishop of Granada were the words used to unite several kingdoms and conquered territories in the faith and in the Castilian language. The aim of this study is to provide a systematic overview of the various editions published throughout history and to analyse the influence that some of them exerted on the subsequent development of evangelisation in Granada, Castile, and possibly the Americas. Full article

Discharge notes contain complex clinical language that patients often struggle to understand. We investigate the impact of automatic highlighting and multi-step prompting on discharge note simplification using in-context learning with large language models. Highlighting is performed using a domain-specific Cardiology Interface Terminology. We used 28 MIMIC-III discharge notes paired with physician-authored summaries and evaluated four configurations (U1, U2, H1, H2): one-step and two-step simplification applied to either highlighted or unhighlighted notes. In one-step simplification (U1 or H1), the model generated a structured, patient-friendly summary directly from the note. In two-step simplification (U2 or H2), the model first generated a structured summary and then simplified it to a 6th-grade reading level. Manual evaluation showed that completeness improved from U1 to H2 (U1 < H1 < U2 < H2), with H2 achieving the highest completeness (92.45%) and the fewest errors (1) compared to U1 (79.05%, 5 errors). Improvements were statistically significant (p < 0.001), except between H1 and U1. Readability improves across all methods (e.g., FKGL reduced from 11 to 7.7 in U2). LLM-based evaluation using both ChatGPT and Gemini shows strong agreement (ρ = 0.88) and favors H2. This pilot study shows combining highlighting with two-step prompting yields more patient-comprehensible summaries. Full article

Conversational search systems (CSSs) are emerging as a transformative interface for health information seeking, enabling multi-round, natural language interactions that integrate diverse medical resources. This scoping review synthesizes evidence on the capabilities, limitations, applications, and future directions of CSSs in healthcare. Following PRISMA-ScR guidelines, we systematically searched multidisciplinary databases (2010–2025), screened 3789 records, and included 325 studies addressing CSSs in health contexts. Analysis identified six thematic domains: (1) capabilities and limitations, (2) enhancement methods, (3) clinical applications, (4) trust, user experience, and interaction design, (5) readability, health literacy, and patient communication, and (6) cross-lingual and domain-specific adaptation. Findings show CSSs offer advantages in personalization, structured output, and patient education, but face challenges in accuracy, timeliness, and semantic consistency, particularly in high-risk clinical decision-making. Enhancement strategies such as retrieval-augmented generation (RAG), knowledge graphs (KG), fine-tuning, and composite approaches improve performance, while trust-building requires transparency, empathy, and ethical safeguards. Cross-lingual disparities and cultural adaptability remain critical gaps. Overall, CSSs hold substantial potential to improve health information access and literacy, but safe, equitable, and culturally sensitive integration demands multidimensional optimization in knowledge updating, bias control, and interaction design, alongside clinician oversight, to ensure reliability and maximize public health impact. Full article

Background/Objectives: Clinical childbirth in high-income countries is increasingly shaped by standardised routines that do not always accommodate individual preferences. In Germany, approximately one in eight pregnant persons experiences clinically significant childbirth-related post-traumatic stress disorder symptoms, with pregnant persons facing language or health-literacy barriers being at particular risk of inadequate preference integration. Methods: This paper presents the conceptual foundation and proposed study design for FAIR-Birth, an interdisciplinary initiative to develop and feasibility-test a mobile application supporting Advance Birth Planning (ABP) embedded within midwifery-led antenatal care. The intervention combines four elements: transfer of the Advance Care Planning concept to antenatal care, a domain-restricted Large Language Model (LLM) supporting multilingual preference articulation, integration of the resulting ABP document into midwifery-led continuity of care, and iterative adaptation. Following the updated MRC framework, this study will employ a sequential mixed-methods design encompassing systematic review, participatory instrument development, Delphi consensus on the knowledge base, iterative technical development with usability testing, and a feasibility study across two perinatal centres in Bavaria. Results/Conclusions: FAIR-Birth is expected to generate a content-validated ABP instrument, a domain-restricted multilingual LLM dialogue system, and an evaluated application prototype. The work corresponds to the development and feasibility phases of the MRC framework; effectiveness questions are reserved for a planned subsequent randomised controlled trial. Full article

Background: Adults with type 2 diabetes increasingly use patient portals, telemonitoring systems, mobile applications, text messaging programs, and other digital services to support self-management. In practice, however, these approaches often still depend on nursing support to help patients understand, use, and sustain digital care in everyday settings. This scoping review mapped how nurses are involved in supporting adults with type 2 diabetes to use digital tools, information, and services for self-management across care settings. Methods: This scoping review followed Joanna Briggs Institute methodology and was reported in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews. The review question was guided by the Population, Concept, and Context framework. A literature search was conducted in January 2026 in PubMed, Scopus, Embase, and EBSCO/CINAHL. A total of 230 records were identified, 71 duplicates were removed, and 159 records underwent title and abstract screening. Fifty-three full-text articles were assessed for eligibility, and 15 studies met the inclusion criteria. Data were extracted using a structured charting table and synthesized descriptively and thematically. Results: The 15 included studies were published between 2021 and 2026 and represented evidence from 10 countries across primary care, community health centers, telehealth programs, and hospital-linked services. Five interrelated themes were identified: nurses as digital self-management educators; nurses as remote monitors and care coordinators; nurses as facilitators of digital engagement, confidence, and supported use; nurses as implementation partners in digital diabetes care; and equity, access, and context as shaping conditions of digital diabetes support. Only one study directly measured digital health literacy, whereas the remaining studies addressed digital engagement more indirectly through onboarding, portal communication, telemonitoring, reminders, tailored feedback, and implementation work. Common barriers included workload, unclear responsibilities, technical difficulties, age- or literacy-related access challenges, language needs, and uneven infrastructure. Conclusions: The included studies suggest that nurses commonly contributed to making digital diabetes care more understandable, usable, and actionable for adults with type 2 diabetes. Their roles were described across education, monitoring, coordination, implementation, and support for digital engagement. Future studies could measure digital health literacy more explicitly, describe nursing tasks in greater detail, and examine how equity-related factors shape digital diabetes care. Full article

Script reforms are socially motivated actions undertaken by institutions or communities that intentionally select, modify, or standardize writing systems. Although scripts are often analyzed as technical ways of writing, designed to enhance literacy and facilitate communication, this is not how script users typically perceive them. Beyond their linguistic function, scripts acquire deep social significance through their critically intertwined relations with issues of identity, political ideologies, and linguistic differentiation. This study analyzes such ideological underpinnings within script use and discusses the social ramifications of language codification. The study draws on different orthographic debates from Africa, Asia, and beyond, to demonstrate the social nature of script. The outcome of this study has implications for communities confronting orthographic decisions and competing script choices. Full article