Search Results (681)

Background: Brazilian Jiu-Jitsu (BJJ) is a physically demanding sport associated with a notable risk of musculoskeletal injuries. Understanding injury patterns, rehabilitation approaches, and psychological readiness to return to sport (RTS) is essential for prevention and management strategies. This study aimed to investigate injury characteristics among Italian BJJ athletes, assess their rehabilitation processes and psychological recovery, and identify key risk factors such as belt level, body mass index (BMI), and training load. Methods: A cross-sectional survey was conducted among members of the Italian BJJ community, including amateur and competitive athletes. A total of 360 participants completed a 36-item online questionnaire. Data collected included injury history, rehabilitation strategies, RTS timelines, and responses to the Injury-Psychological Readiness to Return to Sport (I-PRRS) scale. A Random Forest machine learning algorithm was used to identify and rank potential injury risk factors. Results: Of the 360 respondents, 331 (92%) reported at least one injury, predominantly occurring during training sessions. The knee was the most frequently injured joint, and the action “attempting to pass guard” was the most reported mechanism. Most athletes (65%) returned to training within one month. BMI and age emerged as the most significant predictors of injury risk. Psychological readiness scores indicated moderate confidence, with the lowest levels associated with playing without pain. Conclusions: Injuries in BJJ are common, particularly affecting the knee. Psychological readiness, especially confidence in training without pain, plays a critical role in RTS outcomes. Machine learning models may aid in identifying individual risk factors and guiding injury prevention strategies. Full article

Plant growth monitoring is a complex and challenging task, which depends on a variety of environmental variables, such as temperature, humidity, nutrient availability, and solar radiation. Advances in optical sensors have significantly enhanced data collection on plant growth. These developments enable the optimization of agricultural practices and crop management through the integration of artificial vision techniques. Despite advances in the application of these technologies, limitations and challenges persist. This review aims to analyze the current state-of-the-art methodologies for using artificial vision and optical sensors in plant growth assessment. The systematic review was conducted following the guidelines for Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Relevant studies were analyzed from the Scopus and Web of Science databases. The main findings indicate that data collection in agricultural environments is challenging. This is due to the variability of climatic conditions, the heterogeneity of crops, and the difficulty in obtaining accurately and homogeneously labeled datasets. Additionally, the integration of artificial vision models and advanced sensors would enable the assessment of plant responses to these environmental factors. The advantages and limitations were examined, as well as proposed research areas to further contribute to the improvement and expansion of these emerging technologies for plant growth assessment. Finally, a relevant research line focuses on evaluating AI-based models on low-power embedded platforms to develop accessible and efficient decision-making solutions in both agricultural and urban environments. This systematic review was registered in the Open Science Framework (OSF). Full article

Objective: This proof-of-concept study aimed to evaluate and compare the clinical performance of two AI language models (ChatGPT-3.5 and OpenAI o3) in answering clinical pharmacy multiple-choice questions (MCQs), benchmarked against responses from specialist clinical pharmacists in Jordan, including academic preceptors and hospital-based clinicians. Methods: A total of 60 clinical pharmacy MCQs were developed based on current guidelines across four therapeutic areas: cardiovascular, endocrine, infectious, and respiratory diseases. Each item was reviewed by academic and clinical experts and then pilot-tested with five pharmacists to determine clarity and difficulty. Two ChatGPT models—GPT-3.5 and OpenAI o3—were tested using a standardized prompt for each MCQ, entered in separate sessions to avoid memory retention. Their answers were classified as true/false positives or negatives and retested after two weeks to assess reproducibility. Simultaneously, 25 licensed pharmacists (primarily from one academic institution and several hospitals in Amman) completed the same MCQs using validated references (excluding AI tools). Accuracy, sensitivity, specificity, and Cohen’s Kappa were used to compare AI and human performance, with statistical analysis conducted using appropriate tests at a significance level of p ≤ 0.05. Results: OpenAI o3 achieved the highest accuracy (83.3%), sensitivity (90.0%), and specificity (70.0%), outperforming GPT-3.5 (70.0%, 77.5%, 55.0%) and pharmacists (69.7%, 77.0%, 55.0%). AI performance declined significantly with increasing question difficulty. OpenAI o3 showed the highest accuracy in the cardiovascular domain (93.3%), while GPT-3.5 performed best in infectious diseases (80.0%). Reproducibility was higher for GPT-3.5 (81.6%, κ = 0.556) than OpenAI o3 (76.7%, κ = 0.364). Over two test rounds, GPT-3.5’s accuracy remained stable, whereas OpenAI o3’s accuracy decreased from 83.3% to 70.0%, indicating some variability. Conclusions: OpenAI o3 shows strong promise as a clinical decision-support tool in pharmacy, especially for low- to moderate-difficulty questions. However, inconsistencies in reproducibility and limitations in complex cases highlight the importance of cautious, supervised integration alongside human expertise. Full article

(1) Background: The Peruvian healthcare system is widely regarded as deficient, with ongoing improvements identified as a key area of need. This study sought to assess user satisfaction and the quality of care in two public hospitals in Trujillo. (2) Methods: A non-experimental, cross-sectional, and correlational study was carried out. A group of 384 people who used two public hospitals in the city of Trujillo was studied. The people in the study were chosen based on the researchers’ convenience sampling. Information was collected using a survey based on the SERVQUAL model. This survey was used to evaluate the quality of service. Descriptive and inferential analyses were performed, including Spearman’s correlation and multinomial logistic regression to assess associations and identify key predictors of perceived service quality. (3) Results: The results indicated that 97.66% of the users perceived a low quality of care and 100% expressed dissatisfaction with the services. The most critical dimensions were reliability and responsiveness, while tangible items obtained better results. A positive correlation (rho = 0.723) was identified between quality of care and user satisfaction, with empathy (rho = 0.559) and safety (rho = 0.543) emerging as the most influential dimensions. (4) Conclusions: Responsiveness and Security were identified as key predictors of the perceived service quality in two public hospitals in Trujillo, Peru. Despite high empathy correlations, only timely care and safety significantly influenced satisfaction. The findings align with SDG 3, calling for improved efficiency and humanized care in public health services. Full article

Background: While some evidence supports the benefits of food-related tasks, research examining their association with psychosocial health in adolescents remains scarce. The aim of this study was to examine the association between Spanish adolescents’ involvement in food-related household tasks and their psychosocial health. Methods: This cross-sectional study used secondary data from the original Eating Healthy and Daily Life Activities (EHDLA) study. The final sample comprised 273 boys (43.0%) and 361 girls (57.0%). Adolescents self-reported their weekly frequency of involvement in two food-related tasks: meal preparation and grocery shopping, with responses ranging from ‘never’ to ‘seven times’. Psychosocial health was assessed using the 25-item self-report version of the Strengths and Difficulties Questionnaire (SDQ), comprising five subscales: emotional problems, conduct problems, hyperactivity, peer problems, and prosocial behavior. A total difficulties score was calculated by summing the first four subscales. Generalized linear models were used to evaluate associations between the frequency of food task involvement (categorized into five levels) and SDQ outcomes. All models were adjusted for age, sex, socioeconomic status, body mass index, sleep duration, physical activity, sedentary behavior, and energy intake. Results: Concerning to the frequency of helping to prepare food for dinner, an inverse association was observed between food preparation involvement and several psychosocial problems. Adolescents who helped seven times per week reported significantly lower scores in conduct problems (B = −2.00; 95% CI −3.30 to −0.69; p = 0.003), peer problems (B = −2.83; 95% CI −4.29 to −1.38; p < 0.001), internalizing problems (B = −3.90; 95% CI −7.03 to −0.77; p = 0.015), and total psychosocial difficulties (B = −5.74; 95% CI −10.68 to −0.80; p = 0.023), compared to those who never helped. Conversely, those who helped seven times per week had higher prosocial behavior than their counterparts who never helped (B = 1.69; 95% CI: 0.14 to 3.24; p = 0.033). Regarding the frequency of helping to shop for food, similar patterns were found, with lower conduct problems (B = −2.11; 95% CI −3.42 to −0.81; p = 0.002), peer problems (B = −2.88; 95% CI −4.34 to −1.42; p < 0.001), internalizing problems (B = −4.16; 95% CI −7.28 to −1.04; p = 0.009), and total psychosocial difficulties (B = −6.31; 95% CI −11.24 to −1.39; p = 0.012) associated with more frequent involvement, especially among those who helped five or more times per week. Conversely, adolescents who helped seven times per week had higher prosocial behavior than their peers who never helped (B = 1.56; 95% CI: 0.01 to 3.11; p = 0.049). Conclusions: Although adolescent psychosocial health is influenced by multiple factors, our findings suggest that regular involvement in food-related household tasks may serve as a protective factor against conduct problems, peer problems, internalizing problems, and total difficulties, while also enhancing prosocial behavior. However, given the cross-sectional design, conclusions regarding causality should be made cautiously, and further longitudinal research is needed to confirm these associations and assess their long-term impact. These results highlight the relevance of daily structured routines, such as meal preparation and grocery shopping, as potential support for mental well-being during adolescence. Full article

Background: Youth tobacco use remains a significant public health concern, particularly in urban communities disproportionately burdened by health disparities. In Baltimore City, where tobacco-related harms are elevated, understanding the relationship between tobacco use—including e-cigarettes—and mental health among high school students is essential for guiding equitable prevention and cessation strategies. The CEASE (Communities Engaged and Advocating for a Smoke-free Environment) program, in collaboration with the American Lung Association’s Not On Tobacco (N-O-T) initiative, developed an online school-based survey to inform community-responsive interventions. Aims: This study aimed to examine the associations between cigarette use, including conventional cigarette use, and self-rated mental health among high school students in Baltimore City. Methods: High school students in Baltimore City completed an anonymous online survey that assessed demographic characteristics, tobacco knowledge and use, mental health, and related behaviors. Self-rated mental health was dichotomized as poor versus fair/good. Tobacco use categories included current use of e-cigarettes and conventional cigarettes. Logistic regression models were used to examine associations between tobacco use and self-rated mental health, adjusting for age, gender, race, and parental education. Odds ratios (ORs) with 95% confidence intervals (CIs) were reported. Results: No statistically significant associations were found between self-rated mental health and e-cigarette use and conventional tobacco use after adjusting for covariates. Conclusions: The absence of a significant association may reflect unique aspects of the social context in Baltimore City, where youth may not use tobacco products as a coping mechanism for mental health challenges. Alternatively, it may be due to limitations in measurement, particularly the use of a single-item mental health assessment. These findings should be considered preliminary. Future research using more comprehensive mental health measures and larger samples is warranted to further explore these complex relationships. Full article

Classroom management is an essential yet frequently under-practiced competency in undergraduate teacher education, with important implications for sustainable teacher preparation. This study investigated whether pre-service teachers who feel more capable of managing classrooms also engage more deeply with simulation-based training. Fifty-seven Korean pre-service teachers (15 men, 42 women), all undergraduate students enrolled in a secondary teacher education program at a college of education, completed a five-item classroom-management-efficacy scale, then experienced a 15 min branching simulation that required choosing recognition, punishment, or aggression strategies in response to a disrespectful virtual student. Interest was assessed immediately afterwards with a 24-item instrument covering the four phases of the interest-development model (triggered situational, maintained situational, emerging individual, and well-developed individual). A post-test comparative design and MANOVA revealed that efficacy level had a significant multivariate effect on overall interest (Wilks Λ = 0.78, p = 0.029, partial η² = 0.12). Scheffe contrasts showed that high-efficacy participants outscored their low-efficacy peers on maintained situational and emerging individual interest, p < 0.05, and surpassed the middle-efficacy group in three of the four phases. Repeated measures ANOVA confirmed a general decline from situational to individual interest across all groups (F (3, 52) = 9.23, p < 0.01), underscoring the difficulty of converting short-term curiosity into lasting commitment. These findings position classroom-management efficacy as a key moderator of engagement and support the use of adaptive simulations as sustainable tools for teacher education. By tailoring challenge levels and feedback to participants’ efficacy, guided simulations can foster deeper engagement and promote individualized growth—helping build resilient and well-prepared educators. Full article

Background: The Index of Orthodontic Treatment Need (IOTN) is widely used to assess the need for orthodontic treatment. IOTN consists of the Dental Health Component (DHC) and the Aesthetic Component (AC), evaluating malocclusion morphologically and aesthetically, respectively. However, the discriminatory power of individual DHC items and their relationship with AC grades remain unclear. Objective: This study aimed to evaluate the effectiveness of individual DHC items in school dental examinations and investigate their contribution to AC grades among junior high and high school students. Methods: A total of 726 students (443 males, 283 females; aged 12–18 years) from Tsurumi University Junior and Senior High School, excluding 168 students undergoing or having completed orthodontic treatment, were included. Nine calibrated orthodontists assessed DHC and AC using IOTN during standardized school examinations. The discriminatory power and information precision of DHC items were evaluated by Item Response Theory (IRT) analysis using three-, two-, or one-parameter logistic models depending on convergence. Correspondence analysis visualized the correlation between DHC and AC grades. Simple linear regression analyzed the contribution of each DHC item to AC grades. Results: Orthodontic treatment need was identified in 21.1% of students. Females showed a higher rate of treatment need than males. Correspondence analysis suggested that aesthetic evaluations were more lenient than morphological evaluations. IRT and regression analysis revealed that crowding (4.d), increased overjet (2.a), and increased overbite (2.f) demonstrated high discriminatory power and significant contributions to AC grades. Conclusions: Among the DHC items, crowding, increased overjet, and increased overbite had higher discriminatory power for malocclusion and contributed more significantly to AC evaluations compared to other items. Full article

Background/Objectives: This study aims to develop a standardized weight management intervention program for patients with endometrial cancer (EC) undergoing fertility preservation treatment and to provide a scientific foundation for midwives to implement weight management initiatives in the domains of oncology and reproduction. Methods: The weight management intervention program for patients with EC undergoing fertility preservation treatment was crafted following the directives of the Medical Research Council framework for developing and assessing complex interventions and the World Health Organization handbook for guideline development. The development process encompassed four distinct stages: (1) establishing the intervention development group, (2) identifying a theoretical basis and forming a content framework, (3) gathering and synthesizing evidence, and (4) refining and modeling the practice program. Results: The ultimate weight management program consisted of 6 primary, 18 secondary, and 53 tertiary items. Through two rounds of Delphi consultation, a response rate of 100% was attained, with an expert authority coefficient of 0.83. Conclusions: The developed intervention demonstrates scientific robustness and clinical feasibility, presenting a structured methodology for weight management for EC patients undergoing fertility preservation therapy. Full article

The promotion of sustainability, especially with regard to social and urban sustainability (e.g., well-being and neighborhood revitalization), is mainly linked to human activities and behaviors. Notably, pro-environmental behaviors and actions that promote sustainability depend on the degree to which the individual attributes responsibility, namely, internal and external environmental locus of control (E-LOC). Moreover, from a collectivist perspective, the well-being of communities may also depend on their ability to take action to achieve sustainability goals. In keeping with this, we conducted two different studies to develop and validate (internally and externally) a new instrument that is able to assess internal and external E-LOC by also capturing a third dimension in respect of community E-LOC. In the first study, we performed exploratory factor analysis (EFA) by collecting data from 694 subjects (55.3% cis females; mean age = 30.1, sd = 12.6). In the second study, we conducted confirmatory factor analysis (CFA) on a sample of 1.852 subjects (57% cis females; mean age = 27.6, sd = 11.4), which demonstrated an adequate fit to the theorized model. The final form of the instrument comprises nine items subdivided into internal, external, and community NE-LOC factors. Moreover, the results pointed out significant correlations between the NE-LOC scale and engagement in pro-environmental behaviors and attitudes, pro-environmental self-identity, readiness to change for sustainability, and eco-anxiety. Therefore, the NE-LOC scale can be considered a suitable instrument for the assessment of internal and external NE-LOC, as well as to measure the attribution of collective environmental responsibility. Full article

This paper explores the integration of Large Language Models (LLMs) and secure Gen-AI technologies within engineering design and manufacturing, with a focus on improving inventory management, component selection, and recommendation workflows. The system is intended for deployment and evaluation in a real-world industrial environment. It utilizes vector embeddings, vector databases, and Approximate Nearest Neighbor (ANN) search algorithms to implement Retrieval-Augmented Generation (RAG), enabling context-aware searches for inventory items and addressing the limitations of traditional text-based methods. Built on an LLM framework enhanced by RAG, the system performs similarity-based retrieval and part recommendations while preserving data privacy through selective obfuscation using the ROT13 algorithm. In collaboration with an industry sponsor, real-world testing demonstrated strong results: 88.4% for Answer Relevance, 92.1% for Faithfulness, 80.2% for Context Recall, and 83.1% for Context Precision. These results demonstrate the system’s ability to deliver accurate and relevant responses while retrieving meaningful context and minimizing irrelevant information. Overall, the approach presents a practical and privacy-aware solution for manufacturing, bridging the gap between traditional inventory tools and modern AI capabilities and enabling more intelligent workflows in design and production processes. Full article

Climate change presents major challenges to agriculture, especially in economically underdeveloped regions. In these areas, farmers often lack access to resources and timely information, which limits their ability to respond effectively to drought and threatens agricultural sustainability. This study uses survey data from farmers in underdeveloped regions of China to examine the association between their ability to acquire information and their drought response behaviors. The results indicate that better information acquisition ability is significantly correlated with more effective and scientifically informed decision-making in drought adaptation strategies. To explore the underlying mechanism, we introduce value perception—that is, farmers’ beliefs about the usefulness and benefits of drought adaptation strategies—as a mediating variable. A mechanism model is constructed to test how information acquisition ability relates to behavior indirectly through this perception. We apply a threshold regression model to identify potential nonlinear associations, finding that the relationship between information acquisition ability and drought response behaviors becomes stronger once a certain threshold is surpassed. Additionally, we employ the Item Response Theory (IRT) model to measure the intensity and quality of farmers’ adaptation behaviors more accurately. These findings provide theoretical insights and empirical evidence for enhancing agricultural resilience, while acknowledging that causality cannot be definitively established due to the cross-sectional nature of the data. The study also offers useful guidance for policymakers seeking to strengthen farmers’ access to information, improve value recognition of adaptive actions, and promote sustainable agricultural development in underdeveloped areas. Full article

Robust mean-geometric mean (MGM) linking methods enable reliable group comparisons in item response theory models under fixed and sparse differential item functioning. This article evaluates six alternative standard error and confidence interval (CI) estimation methods across four MGM linking approaches. Our Simulation Study demonstrates that CIs based on the delta method or bootstrap procedures using the normal distribution or empirical quantiles exhibit highly inflated coverage rates. In contrast, CIs derived from a weighted least squares estimation problem, as well as basic and bias-corrected bootstrap methods, yield satisfactory coverage rates in most simulation conditions for robust MGM linking. Full article

Biomedical research training initiatives need rigorous evaluation to achieve national goals of supporting a robust workforce in the biomedical sciences. Higher science self-efficacy is associated with the likelihood of pursuing a science-related research career, but we know little about the psychometric properties of this construct. In this study, we report on a comprehensive validation study of the Science Self-Efficacy Scale using a robust sample of 10,029 undergraduates enrolled across 11 higher education institutions that were part of a biomedical training initiative funded by the National Institutes of Health in the United States. We found the scale to be unidimensional with an Omega hierarchical (ω_h) reliability coefficient of 0.86 and a marginal reliability of 0.91. Within the item response theory framework, we did not detect variation in item parameters across undergraduates’ race/ethnicity; however, one item had parameters that varied across gender identity. We determined that the Science Self-Efficacy Scale can be employed across undergraduates enrolled in science, and researchers can use the scale across a diverse group of students. Implications include ensuring that the scale functions consistently across diverse populations, enhancing the validity of conclusions that can be drawn from survey data analysis. Validating this construct with item response theory models strengthens its use for future research. Full article

Background/Objectives: Chat Generative Pretrained Transformer (ChatGPT) is a useful resource for individuals working in the healthcare field. This paper will include descriptions of several ways in which ChatGPT-4 can achieve greater accuracy in its diagnosis and treatment plans for ulcerative colitis (UC) and Crohn’s disease (CD) by following the guidelines set out by the European Crohn’s and Colitis Organization (ECCO). Methods: The survey, which comprised 102 questions, was developed to assess the precision and consistency of respondents’ responses regarding the UC and CD. The questionnaire incorporated true/false and multiple-choice questions, with the objective of simulating real-life scenarios and adhering to the ECCO guidelines. We employed Likert scales to assess the responses. The inquiries were put to ChatGPT-4 on the initial day, the 15th day, and the 180th day. Results: The 51 true or false items demonstrated stability over a six-month period, with an initial accuracy of 92.8% at baseline, 92.8% on the 15th day, and peaked to 98.0% on the 180th day. This finding suggests a negligible effect size. The accuracy of the multiple-choice questions was initially 90.2% on Day 1, reached its highest point at 92.2% on Day 15, and then decreased to 84.3% on Day 180. However, the reliability of the data was found to be suboptimal, and the impact was deemed negligible. A modest, transient increase in performance was observed at 15 days, which subsequently diminished by 180 days, resulting in negligible effect sizes. Conclusions: ChatGPT-4 demonstrates potential as a clinical decision support system for UC and CD, but its assessment is marked by temporal variability and the inconsistent execution of various tasks. Essential initiatives that should be carried out before involving artificial intelligence (AI) technology in IBD trials are routine revalidation, multi-rater comparisons, prompt standardization, and the cultivation of a comprehensive understanding of the model’s limitations. Full article