Search Results (374)

This study examines how Ontario’s Health and Physical Education curriculum addresses the needs of young adults with disabilities. A total of 54 individuals aged 18–35 years old with developmental, learning, or physical disabilities who had completed secondary school in Ontario participated in a cross-sectional mixed-methods survey. Participants were recruited through disability-focused community networks and a university psychology participant pool. They completed the Sex Education subscale of the Sexual Knowledge, Experience, Feelings and Needs Scale, a 35-item sexual knowledge questionnaire, and open-ended questions. Quantitative data were analyzed using descriptive statistics and independent samples t-tests; qualitative responses were examined using thematic analysis. Participants reported limited factual knowledge, minimal classroom representation, and heavy reliance on independent learning. Barriers included inaccessible materials, teacher discomfort, and the absence of disability narratives in sexuality units. Findings point to three priorities: revising curriculum content, expanding educator training, and creating disability-affirming resources. These measures will help ensure comprehensive and rights-based sexuality education that supports the autonomy and well-being of students with disabilities. Full article

Recent advancements in large language models (LLMs) have significantly enhanced the effectiveness of Retrieval-Augmented Generation (RAG) systems. This study focuses on the development and evaluation of a domain-specific AI chatbot designed to support international student admissions by leveraging LLM-based RAG pipelines. We implement and compare multiple pipeline configurations, combining retrieval methods (e.g., Dense, MMR, Hybrid), chunking strategies (e.g., Semantic, Recursive), and both open-source and commercial LLMs. Dual evaluation datasets of LLM-generated and human-tagged QA sets are used to measure answer relevancy, faithfulness, context precision, and recall, alongside heuristic NLP metrics. Furthermore, latency analysis across different RAG stages is conducted to assess deployment feasibility in real-world educational environments. Results show that well-optimized open-source RAG pipelines can offer comparable performance to GPT-4o while maintaining scalability and cost-efficiency. These findings suggest that the proposed chatbot system can provide a practical and technically sound solution for international student services in resource-constrained academic institutions. Full article

High-quality, openly accessible clinical datasets remain a significant bottleneck in advancing both research and clinical applications within medical artificial intelligence. Case reports, often rich in multimodal clinical data, represent an underutilized resource for developing medical AI applications. We present an enhanced version of MultiCaRe, a dataset derived from open-access case reports on PubMed Central. This new version addresses the limitations identified in the previous release and incorporates newly added clinical cases and images (totaling 93,816 and 130,791, respectively), along with a refined hierarchical taxonomy featuring over 140 categories. Image labels have been meticulously curated using a combination of manual and machine learning-based label generation and validation, ensuring a higher quality for image classification tasks and the fine-tuning of multimodal models. To facilitate its use, we also provide a Python package for dataset manipulation, pretrained models for medical image classification, and two dedicated websites. The updated MultiCaRe dataset expands the resources available for multimodal AI research in medicine. Its scale, quality, and accessibility make it a valuable tool for developing medical AI systems, as well as for educational purposes in clinical and computational fields. Full article

This study proposes a non-semantic multimodal approach to predict segment access frequency (SAF) in lecture archives. Such archives, widely used as supplementary resources in modern education, often consist of long, unedited recordings that are difficult to navigate and review efficiently. The predicted SAF, an indicator of student viewing behavior, serves as a practical proxy for student engagement. The increasing volume of recorded material renders manual editing and annotation impractical, making the automatic identification of high-SAF segments crucial for improving accessibility and supporting targeted content review. The approach focuses on lecture archives from a real-world blended learning context, characterized by resource constraints such as no specialized hardware and limited student numbers. The model integrates multimodal features from instructor’s actions (via OpenPose and optical flow), audio spectrograms, and slide page progression—a selection of features that makes the approach applicable regardless of lecture language. The model was evaluated on 665 labeled one-minute segments from one such course. Experiments show that the best-performing model achieves a Pearson correlation of 0.5143 in 7-fold cross-validation and 61.05% average accuracy in a downstream three-class classification task. These results demonstrate the system’s capacity to enhance lecture archives by automatically identifying key segments, which aids students in efficient, targeted review and provides instructors with valuable data for pedagogical feedback. Full article

Digital hoarding (DH) is an emerging behavior with potential implications for psychological well-being and daily functioning. While traditionally associated with physical hoarding disorder, DH presents unique challenges in digital environments, particularly among university students increasingly immersed in technology. This study examines the relationship between DH and academic performance, proposing a theoretical model in which academic engagement and academic burnout act as mediating mechanisms. Drawing on the Job Demands–Resources Theory, we provide evidence that DH contributes to a health impairment process that negatively affects student outcomes. Our findings reveal DH as a novel predictor of academic burnout, highlighting its detrimental impact on academic performance. These results carry significant theoretical and practical implications, offering new insights into the role of technology-related anxiety disorders in educational settings. From a practical perspective, our study underscores the need for higher education institutions to implement targeted interventions focused on emotional regulation and learning strategies to mitigate the negative effects of DH. Despite limitations related to sample specificity and cross-sectional data, this research opens avenues for future longitudinal studies and interventions aimed at addressing DH in both academic and professional contexts. By linking digital behaviors to mental health and performance, this work aligns with public health interests in understanding technology’s impact on youth well-being. Full article

Throughout the generations, traditional academic writing skills development has taught students in socio-human programs to express their knowledge and thoughts with an evidence-based foundation, helping them make a special connection with their professional fields. However, a lack of digital learning and writing resources in this process has been identified. This study of the social work field connects digital academic writing, social protection functionality, and research innovations, identifying and exploring open-access (OA) educational and social resources for social work higher education (SWHE). Applying content analyses to online documents and websites, we identified key terms characteristic of social work, following a standard approach on formulating research questions, identifying categories, creating a code book, sampling, and measuring information. The research questions were as follows: How is digital academic writing being developed in social work education programs in Romanian universities? Where do researchers, students, teachers, and professionals gather OA digital information and data for academic innovation? What kind of OA information and data are contained in websites for academic writing? We also used OA socio-educational resource analysis to derive digital, evidence-based, and academic writing codes. The frequencies of these elements in documents and websites were examined. Professional samples of four OA documents and five academic and non-academic Romanian websites with extensions were processed. Furthermore, information from a non-academic official website concerning social protection functionality was observed, identified, and measured. We concluded that academic writing is not included as an independent course in the curricula of Romanian social work programs at universities; this topic is rarely researched. Digital and evidence-based education is also a marginalized topic in socio-human scientific resources. OA information, laws, reports, and statistics were identified. Information on scientific research, academic–non-academic partnerships, descriptions of good practices, and human resources information was lacking. In conclusion, this study contributes to increasing productivity and developing digital academic skills in social work education and research. Full article

Background/Objectives: Breast milk donation is increasingly recognized as a vital public health resource, providing optimal nutrition for infants who cannot be breastfed by their biological mothers. Human milk banks play a crucial role in supporting infant health, particularly for preterm and medically vulnerable infants. Despite its recognized benefits, the practice of breast milk donation and utilization remains influenced by a complex interplay of individual, social, and cultural factors. Methods: This study systematically examines how demographic and sociocultural variables are associated with attitudes and behaviors related to breast milk donation. Data were collected from 1223 Israeli mothers via questionnaires distributed through WhatsApp groups between April and May 2025. By analyzing the questionnaire results, the research identified key predictors of willingness to donate and use donated milk, as well as patterns of awareness and perceived support within different population groups. Results: Higher education (χ² = 12.87, p = 0.0016) and settlement type (χ² = 83.096, p = 0.000) were significantly associated with greater willingness to donate breast milk. Income level had no effect on donation behavior, though higher-income participants were more open to its use (χ² = 86.838, p = 0.000). Lifestyle also influenced perceptions of social support (F(2, 1220) = 259.4036, p < 0.001) and cultural support (F(2, 1220) = 601.2383, p < 0.001) of milk donation and use. Significant correlation was found between perceived cultural and social support (t = 5.140, p = 0.000), emphasizing their interrelated influence on milk donation attitudes. Conclusions: The findings assist in guiding the development of public health policies, as well as targeted awareness campaigns, to promote equitable access to donor milk services across varied communities. From the public health perspective, the findings of this study can orientate campaigns that encourage both donation and use of breast milk focusing on segments of the population in which the level of awareness is relatively low. Full article

Mental illness relapse among teachers presents a growing public health concern, particularly in under-resourced settings, where social and structural factors often go unaddressed. This study aimed to explore challenges outside the workplace that contribute to mental illness relapse among public school teachers in Limpopo Province and how they cope with them. Guided by the Stress-Vulnerability Model, a qualitative explorative phenomenological design was employed. Fourteen participants with a documented history of existing mental illness and mental illness relapse were purposively selected across four different hospitals. The data were collected through in-depth, face-to-face semi-structured interviews until data saturation was reached. The interviews were audio-recorded, transcribed verbatim, and analysed using Tesch’s open coding method. The findings revealed unstable home environments, community-level stigma, inadequate institutional support, and systemic barriers to mental healthcare access. Moreover, the participants rely on family members for support and on spiritual practices to cope, highlighting gaps in formal support systems. Addressing these overlooked challenges is critical to reducing relapse resulting from social and systematic challenges, promoting mental health equity, and sustaining teacher resilience in underserved communities. This study calls for collaborative efforts from policymakers, educational institutions, healthcare providers, and community leaders, including faith-based organisations, to develop integrated mental health strategies. Such strategies can promote mental health equity, reduce stigma, and support sustainable teacher well-being in vulnerable communities. Full article

Butterflies (Lepidoptera: Papilionoidea), considered bioindicators of habitat conservation status, are one of the groups used to assess the impact of habitat management and improvement efforts in the Biological Station of Torretes (Ibi, Alicante, Spain). This area also houses the Botanical Garden of the University of Alicante. The study was focused on the period from autumn 2022 to late spring 2023, and data on butterfly diversity and abundance from the current study were compared with historical data. Our results highlight that open spaces and the availability of trophic resources (nutritional plants) were key factors influencing the richness and abundance of Papilionoidea. The program for the creation and maintenance of new habitats at the biological station has increased spatial heterogeneity and the availability of trophic resources, which has led to a significant increase in the richness and abundance of butterflies in a short period. To support the conservation of Mediterranean ecosystems, and entomofauna in general, the Papilionidae are proposed as model groups for designing conservation projects based on traditional habitat management. These projects should promote spatial heterogeneity, include programs to enrich plant diversity, and be complemented by environmental education and outreach initiatives. Full article

Low achievement in mathematics across educational levels has long been a concern for researchers. Recent evidence points to equipping teachers with skills and competencies that align with the demands of the modern, technology-rich world. This systematic review explored how technology-facilitated heutagogical practices contribute to the professional development of preservice and in-service mathematics teachers. Drawing on 21 empirical studies published between 2017 and 2024, this review identified three major findings. First, technology-enhanced heutagogical practices promote teaching skills by fostering learner autonomy, self-reflection, and professional identity development. Second, tools such as mobile apps, Massive Open Online Courses (MOOCs), adaptive learning platforms, and collaborative digital environments support the integration of heutagogical principles. Third, implementation is challenged by limited digital access, institutional constraints, and the need for gradual adaptation to self-determined learning models. These findings prove the need for policy and institutional investment in digital infrastructure, blended learning models, and teacher support. Theoretically, this study affirms heutagogy as a relevant pedagogical approach for preparing mathematics teachers in dynamic learning contexts. There is also a need for more empirical studies to investigate scalable models for technology-driven heutagogy, especially in resource-constrained settings. Full article

Nutrition education, framed within Supplemental Nutrition Assistance Program Education (SNAP-Ed) guidance, was provided to SNAP-eligible shoppers at community supported agriculture (CSA) sites in Michigan where SNAP nutrition incentives were accepted. An evaluation was conducted on data sources from sites where the CSA Food Navigator program was implemented to assess the delivery of nutrition education, understand the needs and experiences of SNAP-eligible shoppers, and measure behavioral outcomes. A multi-phase, mixed-methods design incorporated (1) outcome surveys with SNAP-eligible shoppers at participating CSA sites; (2) open-ended feedback surveys from CSA site staff; (3) nutrition educator activity logs; (4) a semi-structured nutrition educator focus group; and (5) semi-structured focus groups with SNAP-eligible shoppers. In phase one, descriptive analysis was completed on the quantitative data and constant comparative analysis was completed on the qualitative data. In phase two, these data were collated into case reports for respective CSA sites; then, a cross-case analysis was performed. In phase three, statistical tests were performed on SNAP-eligible shoppers’ survey data to assess outcomes against a nationally representative sample of nutrition incentive program participants. Results indicate significantly higher fruit and vegetable consumption among shoppers relative to SNAP incentive participants nationally. Key qualitative themes were (1) relating over transacting: investing in multi-level relationships, (2) personalizing engagement and experiential nutrition education, (3) activating social–ecological spheres to promote changes in access, and (4) enhancing education support and resources for accessibility. The findings have practical implications to enhance the delivery and impact of CSA-based nutrition education. Full article

We propose the extension for Artificial Intelligence (AI)-supported learning recommendations within higher education, focusing on enhancing the widely-used Moodle Learning Management System (LMS) and extending it to the Learning eXperience Platform (LXP). The proposed LXP is an enhancement of Moodle, with an emphasis on learning support and learner motivation, incorporating various recommendation types such as content-based, collaborative, and session-based recommendations to provide the next learning resources given by lecturers and retrieved from the content curation of Open Educational Resources (OER) for the learners. In addition, we integrated a chatbot using Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) with AI-based recommendations to provide an effective learning experience. Full article

Management in the current era is strongly supported by a range of advanced technologies that enhance organizational efficiency and effectiveness. One particularly promising technology is Virtual Reality (VR). This exploratory study investigates the perceived practicality of VR across the systems engineering project lifecycle, providing quantitative insights into how VR is valued at different stages and by various user profiles. Drawing on data from $n=56$ professionals, the study employs advanced analytical methods like GLM, CFA, SEM, CATPCA, and CHAID classification trees to extract insights from the dataset. Results reveal that the perceived utility of VR varies significantly across project stages, with the training and proposal phases receiving the highest ratings, and early planning stages such as specification and requirement definition rated the lowest. A unidimensional structure of VR practicality was validated, with strong factor loadings for stages involving prototyping and testing. Key predictors of perceived practicality included willingness to adopt VR (positive effect), familiarity with VR (mixed effects), and education level (negative effect). While traditional demographic variables had limited explanatory power, experiential and psychological factors—such as hands-on VR exposure and openness to innovation—emerged as critical determinants of perceived value. These findings offer an empirically grounded framework for the strategic implementation of VR in systems engineering. Practically, this research guides decisions on resource allocation by identifying the specific project stages (e.g., training, proposal development) and user profiles (e.g., those open to adoption, those with specific experiential backgrounds) where VR is likely to provide the most significant benefit and return on investment. Full article

This article introduces a freely available Spanish–Mixtec parallel corpus designed to foster natural language processing (NLP) development for an indigenous language that remains digitally low-resourced. The dataset, comprising 14,587 sentence pairs, covers Mixtec variants from Guerrero (Tlacoachistlahuaca, Northern Guerrero, and Xochapa) and Oaxaca (Western Coast, Southern Lowland, Santa María Yosoyúa, Central, Lower Cañada, Western Central, San Antonio Huitepec, Upper Western, and Southwestern Central). Texts are classified into four main domains as follows: education, law, health, and religion. To compile these data, we conducted a two-phase collection process as follows: first, an online search of government portals, religious organizations, and Mixtec language blogs; and second, an on-site retrieval of physical texts from the library of the Autonomous University of Querétaro. Scanning and optical character recognition were then performed to digitize physical materials, followed by manual correction to fix character misreadings and remove duplicates or irrelevant segments. We conducted a preliminary evaluation of the collected data to validate its usability in automatic translation systems. From Spanish to Mixtec, a fine-tuned GPT-4o-mini model yielded a BLEU score of 0.22 and a TER score of 122.86, while two fine-tuned open source models mBART-50 and M2M-100 yielded BLEU scores of 4.2 and 2.63 and TER scores of 98.99 and 104.87, respectively. All code demonstrating data usage, along with the final corpus itself, is publicly accessible via GitHub and Figshare. We anticipate that this resource will enable further research into machine translation, speech recognition, and other NLP applications while contributing to the broader goal of preserving and revitalizing the Mixtec language. Full article

Background/Objectives: The educational needs of individuals with OA and obesity can drive personalized resources for effective dietary interventions that align patient interests with weight and disease management. Therefore, the purpose of the present study was to evaluate differences in nutritional education topics of interest between patients with OA who are characterized as having higher (≥30 kg/m²; HBMI) and lower BMI (<30 kg/m²; LBMI). Methods: Cross-sectional survey data (n = 296) were dichotomized into HBMI (n = 172; BMI: 38.67 ± 6.59 kg/m²) and LBMI (n = 124; BMI: 25.59 ± 3.00 kg/m²) groups. A mixed-method approach examined group differences across four primary domains: (i) strategies for weight management and healthy lifestyle, (ii) interest in vitamins and supplements, (iii) foods and nutrient with anti-inflammatory properties, and (iv) dietary patterns for weight loss. Logistic regression models compared topic interests between groups. Thematic analysis of open-ended responses captured additional insights. Results: Compared to LBMI participants, those in the HBMI group showed greater interest in weight loss strategies, emotional eating, and diets such as low-carbohydrate and ketogenic approaches, but less interest in general supplement information and plant-based diets. HBMI also had greater interest in practical strategies (e.g., feeling full, affordable foods) and reduced interest in certain anti-inflammatory foods. Both groups expressed a desire for evidence-based resources on foods that promote joint health. Conclusions: BMI-specific differences in nutritional education preferences highlight the need for tailored, patient-centered strategies in OA management. Addressing these differences may improve the effectiveness of education interventions and enhance patient–provider communication around diet and lifestyle in OA care. Full article