mdpi-ai/citelens huggingface dataset:
Teachers’ Perceptions and Preparedness for Teaching English as a Foreign Language to Students with Developmental Dyslexia: A Systematic Review
Field: PSYCHOLOGY

Students with developmental dyslexia (DD) face significant challenges when learning English as a foreign language (EFL), highlighting the need for targeted support in educational systems. EFL teachers’ perceptions and preparedness regarding DD are crucial for effective instruction and improved learning outcomes in inclusive classrooms. However, no systematic review has yet explored EFL teachers’ perceptions and preparedness to teach students with DD. This systematic review, conducted in accordance with the PRISMA guidelines, examines existing research between 2005 and 2025 on EFL teachers’ perceptions and preparedness to teach students with DD. Studies were retrieved from databases including APA PsycNet, Crossref, ERIC, ProQuest, PubMed, and Scopus databases. Of 17,798 results, 16 studies met the inclusion criteria. The findings reveal mixed EFL teachers’ perceptions toward DD and inadequate training specific to DD. Moreover, practical teaching strategies and targeted interventions remain underrepresented in the literature. Most teachers lack formal DD-specific training, leading to insufficient classroom support. This review emphasizes the urgent need for improved in-service training and the development of effective resources. Future research should prioritize developing and evaluating practical teaching strategies and professional development programs on teacher preparedness in EFL contexts.
A psycho-behavioral perspective research for elderly leisure sports participation via big-data and comparative analyses
Field: PSYCHOLOGY

The health of the elderly and the need for research to support them has never been more important. This study aims (a) to analyze the participation behavior of the elderly in leisure sports through big-data analysis and (b) to compare and analyze the motivations, limitations, and satisfaction of participation in leisure sports by age group. First, big-data analysis using text-mining technique was conducted using the TEXTOM program to collect and analyze data between May 1, 2023 and November 24, 2024. Next, a survey was conducted among adults aged 20 years and older who regularly participate in leisure sports to determine their motivations, limitations, and satisfaction with leisure participation. From June to December 2024, the data of 301 participants were collected and analyzed using SPSS 28.0. Specifically, this study analyzed the validity and reliability of the data and then compared and analyzed the three age groups through multivariate analysis of variance. Big-data analysis identified key terms and four clusters related to senior leisure sports participation: (a) Policy, (b) Welfare, (c) Senior Sports, and (d) Employment. The results of the comparative study through the questionnaire showed that compared to younger participants in leisure sports, the elderly showed higher results in the factors of self-challenge motive, social interaction motive, and leisure participation satisfaction, but lower results in the factor of cost constraints. This means that the elderly participate in leisure sports for challenge and social interaction, are more satisfied, and are less constrained by cost. The scientific and objective results of this study could be used as a resource to specifically understand the leisure sports participation behavior of the elderly.
Empowering leadership and occupational burnout: the moderated mediation model
Field: PSYCHOLOGY

This study examines the impact of empowering leadership on occupational burnout through the mediating role of workaholism and the moderating effect of psychological hardiness in the relationship between empowering leadership and occupational burnout. The present study employs empowerment and hardiness theory. Further, the moderated mediation hypothesis was also investigated. Survey responses from 212 permanent employees (nurses) in the healthcare industry were gathered using the temporal separation (two time-lags with one month between the first and second lags) to test the proposed hypotheses. Different statistical analysis techniques, confirmatory factor analysis, discriminant and convergent validity and PROCES-macro were used. The current study findings shows that empowering leadership significantly reduces occupational burnout. Furthermore, the results of the study confirm that workaholism plays a crucial role as a mediator between empowering leadership and occupational burnout in the workplace. Additionally, the findings shows that empowering leadership burdens nurses by making them work excessively, which causes occupational burnout in the workplace. Furthermore, psychological hardiness is a significant moderator in the relationship between workaholism and occupational burnout. Finally, the moderated mediation model results showed that nurses with high psychological hardiness adjust and manage well with intense workloads, i.e., workaholism, when emboldened through their leaders which leads to reduction in occupational burnout. The findings emphasize the potential advantages and hazards of empowering leadership in the nursing profession and the management of healthcare. This study builds on earlier research by empirically investigating how workaholism and psychological hardiness influence the relationship between empowering leadership and occupational burnout in the nursing profession of Pakistan.
Efficacy and safety of immune checkpoint inhibitors for brain metastases of non-small cell lung cancer: a systematic review and network meta-analysis
Field: ONCOLOGY

Background: Previous studies have demonstrated that immune checkpoint inhibitors (ICIs) significantly improve prognosis in lung cancer patients with brain metastases (BMs). This systematic review and network meta-analysis aims to evaluate the efficacy and safety of 10 ICIs recommended by the 2024 Chinese Society of Clinical Oncology guidelines for treating non-small cell lung cancer (NSCLC) without driver genes, focusing on NSCLC patients presenting with BMs.Materials and methods: A comprehensive literature search of PubMed, Embase, and the Cochrane Library was conducted through June 2024 to identify eligible controlled trials and head-to-head randomized controlled trials investigating 10 ICIs in NSCLC patients with BMs. Pairwise and network meta-analyses were performed using hazard ratios (HRs) and relative risks (RRs) with 95% confidence intervals (CIs). Treatment efficacy was ranked hierarchically through the surface under the cumulative ranking curve (SUCRA).Results: Sixteen trials from 11 studies, encompassing 1,274 NSCLC patients with BMs, were included. The meta-analysis demonstrated that ICIs significantly improved overall survival (OS: HR, 0.66; 95% CI, 0.52–0.85; P = 0.001) and progression-free survival (PFS: HR, 0.67; 95% CI, 0.54–0.84; P < 0.001). SUCRA ranking identified pembrolizumab as the most effective agent for OS improvement (SUCRA 71%), while camrelizumab showed superior PFS benefits (SUCRA 92%). ICIs were associated with increased objective response rates (RR: 1.52; 95% CI, 1.13–2.06; P = 0.006), but elevated risks of immune-mediated adverse events (RR: 2.50; 95% CI, 1.46–4.30; P = 0.001) and grade 3–5 immune-mediated adverse events and infusion reaction (RR: 6.39; 95% CI, 1.53–26.69; P = 0.011).Conclusion: ICIs demonstrate superior survival benefits compared to chemotherapy in NSCLC patients with BMs, with pembrolizumab and camrelizumab emerging as optimal choices for OS and PFS improvement, respectively. However, vigilant monitoring of immune-mediated adverse events and infusion reactions remains critical in clinical practice.
The Impact of Ancient Greek Prompts on Artificial Intelligence Image Generation: A New Educational Paradigm
Field: AI

Background/Objectives: This article explores the use of Ancient Greek as a prompt language in DALL·E 3, an Artificial Intelligence software for image generation. The research investigates three dimensions of Artificial Intelligence’s ability: (a) the sense and visualization of the concept of distance, (b) the mixing of representational as well as mythic contents, and (c) the visualization of emotions. More specifically, the research not only investigates AI’s potentialities in processing and representing Ancient Greek texts but also attempts to assess its interpretative boundaries. The key question is whether AI can faithfully represent the underlying conceptual and narrative structures of ancient literature or whether its representations are superficial and constrained by algorithmic procedures. Methods: This is a mixed-methods experimental research design examining whether a specified Artificial Intelligence software can sense, understand, and graphically represent linguistic and conceptual structures in the Ancient Greek language. Results: The study highlights Artificial Intelligence’s possibility in classical language education as well as digital humanities regarding linguistic complexity versus AI’s power in interpretation. More specifically, the research not only investigates AI’s potentialities in processing and representing Ancient Greek texts but also attempts to assess its interpretative boundaries. The key question is whether AI can faithfully represent the underlying conceptual and narrative structures of ancient literature or whether its representations are superficial and constrained by algorithmic procedures. The study highlights Artificial Intelligence’s possibility in classical language education as well as digital humanities regarding linguistic complexity versus AI’s power in interpretation. Conclusions: The research is a step toward a more extensive discussion on Artificial Intelligence in historical linguistics, digital pedagogy, as well as aesthetic representation by Artificial Intelligence environments.
Navigating stigma and somatization: a qualitative exploration of mental health experiences among middle-aged adults in rural China
Field: PSYCHOLOGY

This study investigated the experiences of stigma and somatization among middle-aged adults with mental health issues. Using frameworks of public stigma, self-stigma, affiliate stigma, and somatization (both presenting and functional), the study explores how individuals navigate the stigma associated with mental health. Interviews were conducted with middle-aged adults in rural areas, and the data were analyzed using Interpretative Phenomenological Analysis (IPA) to gain insights into their lived experiences. The findings reveal that mental health stigma in rural China significantly influences how individuals express mental distress, often leading to somatization. Patients tend to frame their mental health issues in terms of physical symptoms, such as headaches or fatigue, to avoid stigma. The study also highlights the role of cultural norms in shaping these expressions, particularly within the context of close-knit rural communities where mental health issues is stigmatized. The implications for education and policy are discussed, emphasizing the need for improved public mental health education and more equitable distribution of healthcare resources between urban and rural areas. This study contributes to the understanding of mental health stigma in rural China and offers practical suggestions for addressing mental health challenges in underserved communities.
Development and Validation of a Questionnaire on Students’ Mathematics Capital: A Tool to Explore Opportunities in the Mathematics Classroom
Field: EDUCATION

Understanding students’ opportunities in mathematics education requires tools that capture the social and cultural dimensions shaping their engagement with the subject. One way to conceptualise these opportunities is through the notion of mathematics capital, which encompasses the resources and dispositions that students bring to their mathematical experiences. This study introduces and validates a questionnaire designed to measure secondary students’ mathematics capital, adapting the well-established science capital framework to the mathematical domain. Grounded in Bourdieu’s concept of capital, the questionnaire operationalises mathematics capital across mathematical forms of cultural capital, mathematics-related behaviours and practices, and mathematics-related forms of social capital. The questionnaire was administered to 119 students in an Italian secondary school as part of a broader study on mathematical memes. Statistical analyses, including correlation tests and Cronbach’s alpha, confirm the instrument’s reliability and internal coherence, highlighting the influence of both school and extracurricular environments. The questionnaire provides educators with a practical tool to better understand students’ engagement with mathematics and to inform strategies for fostering equity in mathematics education. By making mathematics capital a measurable construct, this research contributes to discussions on how cultural and social factors shape students’ trajectories in mathematics and beyond.
Artificial Intelligence in Ovarian Cancer: A Systematic Review and Meta-Analysis of Predictive AI Models in Genomics, Radiomics, and Immunotherapy
Field: AI

Background/Objectives: Artificial intelligence (AI) is increasingly influencing oncological research by enabling precision medicine in ovarian cancer through enhanced prediction of therapy response and patient stratification. This systematic review and meta-analysis was conducted to assess the performance of AI-driven models across three key domains: genomics and molecular profiling, radiomics-based imaging analysis, and prediction of immunotherapy response. Methods: Relevant studies were identified through a systematic search across multiple databases (2020–2025), adhering to PRISMA guidelines. Results: Thirteen studies met the inclusion criteria, involving over 10,000 ovarian cancer patients and encompassing diverse AI models such as machine learning classifiers and deep learning architectures. Pooled AUCs indicated strong predictive performance for genomics-based (0.78), radiomics-based (0.88), and immunotherapy-based (0.77) models. Notably, radiogenomics-based AI integrating imaging and molecular data yielded the highest accuracy (AUC = 0.975), highlighting the potential of multi-modal approaches. Heterogeneity and risk of bias were assessed, and evidence certainty was graded. Conclusions: Overall, AI demonstrated promise in predicting therapeutic outcomes in ovarian cancer, with radiomics and integrated radiogenomics emerging as leading strategies. Future efforts should prioritize explainability, prospective multi-center validation, and integration of immune and spatial transcriptomic data to support clinical implementation and individualized treatment strategies. Unlike earlier reviews, this study synthesizes a broader range of AI applications in ovarian cancer and provides pooled performance metrics across diverse models. It examines the methodological soundness of the selected studies and highlights current gaps and opportunities for clinical translation, offering a comprehensive and forward-looking perspective in the field.
Intraoperative Radiation Therapy (IORT) in Gynecologic Cancers: A Scoping Review
Field: ONCOLOGY

Objective: We aimed to analyze the current literature for IORT in gynecological cancers and summarized clinical outcomes regarding patient selection. Methods: A systematic search was conducted utilizing PUBMED, Embase, and CINAHL to identify studies following PRISMA-ScR guidelines. A PICOS structure was utilized: population: patients with epithelial gynecological cancers; intervention: IORT; C: a comparator was not required, as we aimed to analyze patient selection; outcome: clinical outcomes and overall survival; and S: experimental and quasi-experimental analytical observational studies and descriptive observational studies, excluding case series published in English and limited to the last 10 years. Data extraction was conducted for patient selection, IORT, oncological outcomes, and morbidity. Results: A total of 707 results were identified, and 509 studies were uploaded to Covidence for screening after removing duplications. Of the 21 eligible studies, 9 were included in the final review. The total number of patients included was 348. The studies were retrospective single-institution studies, except for one. There was significant heterogeneity in their design and protocols. IORT was exclusively used for recurrent and advanced stage gynecological cancers adjunct to pelvic exenteration or laterally extended endopelvic resections with variable indications across institutions. The mean number of IORT patients per study was 2.8 per year. Survival rates were variable and dependent on the surgical margin. Endometrial cancer had a favorable outcome compared to vulvar and cervical cancers. Conclusions: Current clinical practice, as demonstrated by the research, is consistent with NCCN guidelines that endorse the application of IORT in instances of recurrent cervical, vaginal, and vulvar malignancies; however, there are no established recommendations for primary tumors. The analysis shows that there are gaps in our knowledge, mainly regarding the status of the margins, the criteria used to choose patients, and the outcomes that are specific to each histology. The standardization of protocols and prospectively powered studies are needed to refine patient selection criteria.
CacheFormer: High-Attention-Based Segment Caching
Field: AI

Efficiently handling long contexts in transformer-based language models with low perplexity is an active area of research. Numerous recent approaches like Linformer, Longformer, Performer, and Structured state space models (SSMs), have not fully resolved this problem. All these models strive to reduce the quadratic time complexity of the attention mechanism while minimizing the loss in quality due to the effective compression of the long context. Inspired by the cache and virtual memory principle in computers, where in case of a cache miss, not only the needed data are retrieved from the memory, but the adjacent data are also obtained, we apply this concept to handling long contexts by dividing it into small segments. In our design, we retrieve the nearby segments in an uncompressed form when high segment-level attention occurs at the compressed level. Our enhancements for handling long context include aggregating four attention mechanisms consisting of short sliding window attention, long compressed segmented attention, dynamically retrieving top-k high-attention uncompressed segments, and overlapping segments in long segment attention to avoid segment fragmentation. These enhancements result in an architecture that outperforms existing SOTA architectures with an average perplexity improvement of 8.5% over similar model sizes.
ChatGPT in Education: Challenges in Local Knowledge Representation of Romanian History and Geography
Field: EDUCATION

The integration of AI tools like ChatGPT in education has sparked debates on their benefits and limitations, particularly in subjects requiring region-specific knowledge. This study examines ChatGPT’s ability to generate accurate and contextually rich responses to assignments in Romanian history and geography, focusing on topics with limited digital representation. Using a document-based analysis, this study compared ChatGPT’s responses to local archival sources, monographs, and topographical maps, assessing coverage, accuracy, and local nuances. Findings indicate significant factual inaccuracies, including misidentified Dacian tribes, incorrect historical sources, and geographic errors such as misplaced landmarks, elevation discrepancies, and incorrect infrastructure details. ChatGPT’s reliance on widely digitized sources led to omissions of localized details, highlighting a fundamental limitation when applied to non-digitized historical and geographic topics. These results suggest that while ChatGPT can be a useful supplementary tool, its outputs require careful verification by educators to prevent misinformation. Future research should explore strategies to improve AI-generated educational content, including better integration of regional archives and AI literacy training for students and teachers. The study underscores the need for hybrid AI-human approaches in education, ensuring that AI-generated text complements rather than replaces verified academic sources.
Spiritual orientation and mental health: an SEM analysis of meaning and death attitudes as mediators in Turkish religious officials
Field: PSYCHOLOGY

This study examines the relationships between spiritual orientation, meaning in life, attitudes towards death, and indicators of psychological health (depression, anxiety, and stress) among 348 Muslim religious officials in Turkey (28% female). Using structural equation modelling (SEM), the results showed that spiritual orientation directly and indirectly reduces psychological distress by enhancing personal meaning and fostering more accepting attitudes towards death. Results showed a moderate positive association between spiritual orientation and meaning in life, and weak but significant negative associations between meaning/attitudes towards death and psychological symptoms. As one of the first empirical studies to examine the mediating role of death attitudes in this population, the research highlights the theoretical relevance of existential frameworks such as logotherapy. The study offers practical implications for the development of culturally sensitive psychoeducational and spiritual counselling programmes aimed at supporting the mental health of religious professionals exposed to grief and death-related stressors.
Study of an effective machine learning-integrated science curriculum for high school youth in an informal learning setting
Field: EDUCATION

This study evaluates the effectiveness of a machine learning (ML) integrated science curriculum implemented within the Science Research Mentorship Program (SRMP) for high school youth at the American Museum of Natural History (AMNH) over 2 years. The 4-week curriculum focused on ML knowledge gain, skill development, and self-efficacy, particularly for under-represented youth in STEM. ML is increasingly prevalent in STEM fields, making early exposure to ML methods and artificial intelligence (AI) literacy crucial for youth pursuing STEM careers. However, STEM fields, particularly those focused on AI research and development, suffer from a lack of diversity. Learning experiences that support the participation of under-represented groups in STEM and ML are essential to addressing this gap. Participant learning was assessed through pre- and post-surveys measuring ML knowledge, skills, and self-efficacy. Results from the implementation of the curriculum show that participants gained understanding of ML knowledge and skills (p < 0.001, d = 1.083) and self-efficacy in learning ML concepts (p = 0.004, d = 0.676). On average, participants who identified as female and non-white showed greater learning gains than their white male peers (ML knowledge: p < 0.001, d = 1.191; self-efficacy: p = 0.006, d = 0.631), decreasing gaps in ML knowledge, skills, and self-efficacy identified in pre-survey scores. The ML-integrated curriculum effectively enhances students’ understanding and confidence in ML concepts, especially for under-represented groups in STEM, and provides a model for future ML education initiatives in informal science settings. We suggest that policy makers and school leaders take into account that high school age youth can learn ML concepts through integrated curricula while maintaining an awareness that curriculum effectiveness varies across demographic groups.