Search Results (180)

Despite extensive research on early childhood education (ECE) quality at the preschool level, toddler settings remain comparatively understudied, particularly in Chile and Latin America. Research suggests that quality ECE strengthens child development, while low-quality services can be harmful. ECE quality comprises structural features like ratios and classroom resources, and process features related to interactions within classrooms. This study examines how process and structural quality indicators are related in nurseries serving disadvantaged backgrounds. Data were collected from 51 Chilean urban classrooms serving children aged 12–24 months. Classrooms were evaluated using the Classroom Assessment Scoring System (CLASS) for toddlers, questionnaires, and checklists. Latent Profile Analysis identified process quality patterns, while multinomial regression examined associations with structural quality indicators. The results revealed low-to-moderate process quality across classrooms (M = 4.78 for Emotional and Behavioral Support; M = 2.35 for Engaged Support for Learning), with three distinct quality clusters emerging. Marginally significant differences were found between high- and low-performing clusters regarding classroom space (p = 0.06), number of toys (p = 0.08), and staff educational credentials (p = 0.01–0.07). No significant differences emerged for group sizes or adult-to-child ratios, which are heavily regulated in Chile. These findings underscore the need to strengthen quality assurance mechanisms ensuring all children access quality ECE. Full article

Background: Risk assessment in hyperthyroidism remains challenging. The aim of the present study is to determine the influence of hyperthyroid metabolic status on blood clotting and an increased risk of thrombosis. Methods: This prospective study included 50 patients after radical thyroidectomy and ablative radioiodine therapy because of thyroid carcinoma who were compared with 50 control subjects in a euthyroid metabolic state. Latent hyperthyroid patients with basal thyroid-stimulating hormone (TSH) ≤ 0.15 mU/L on levothyroxine hormone therapy were included. The control group was selected to match the patient group based on age and sex. The evaluation data were collected using laboratory coagulation tests and patient questionnaires. A bleeding and a thrombosis score were determined. Results: The coagulation parameters between the patient and control groups showed statistically significant differences. In particular, the patients’ group showed a significantly shortened activated partial thromboplastin time (aPTT/p = 0.009) and a significantly higher plasminogen activator inhibitor 1 (PAI-1/p < 0.001) compared to the control group. Age, sex, and medication use were not found to influence the patients’ laboratory results. Only body mass index was higher in the patient group than in the control group. Conclusions: Our results support a shift in the coagulation system in latent hyperthyroid metabolism towards increased coagulability and reduced fibrinolysis. A latent hyperthyroid metabolic state appears to be associated with an increased risk of thrombosis. Further prospective cohort studies with large patient populations are needed to verify the association between (latent) hyperthyroidism and thromboembolic events as well as to determine therapeutic anticoagulation or to adjust the indication for exogenous administration of thyroid hormone. Full article

(1) Background: This study used latent profile analysis (LPA) to investigate family patterns of paternal and maternal responses to adolescents’ discrete emotions (happiness, sadness, and anger) and examined the relationship between these profiles and demographic factors, as well as adolescents’ emotion adjustment (emotion regulation and depressive symptoms). (2) Methods: A sample of 666 adolescents reported parental responses and their emotional adjustment; their mothers provided family information. (3) Results: (a) The LPA identified four profiles for adolescent happiness, including high enhancing but low dampening and neglect from both parents (Consistent Supportive); low enhancing but high dampening and neglect from both parents (Consistent Unsupportive); low to moderate scores on each response from both parents (Consistent Disengaging); and high maternal dampening and neglect but relatively low scores on the paternal response (Inconsistent). There were two profiles for sadness (Consistent Supportive, Consistent Unsupportive) and three for anger (Consistent Supportive, Consistent Unsupportive, Consistent Disengaging). (b) Parents with boys, higher incomes, better education, and greater marital satisfaction were likely to be classified into the Consistent Supportive profile across emotions. (c) When adolescents perceived their parents with the Consistent Supportive profile, they would show the best emotional adjustment; while for parents with the Inconsistent profile (for happiness) and the Consistent Unsupportive profile, the adolescents had the poorest outcome. Interestingly, adolescents who perceived their parents as fitting the Consistent Disengaging profile (especially for anger) exhibited comparatively less adverse adjustment. (4) Implications: A person-centered approach highlights different patterns of emotion socialization, underscores the importance of fostering parental cooperation and supportive responses to adolescents’ happiness, and suggests that joint disengagement from anger may promote healthier emotional development. Full article

Aggressiveness among schoolchildren can be shaped by specific school-related situations that elicit stress. Accordingly, this study aimed to identify differentiated profiles of secondary school students based on the levels of aggressive behavior they exhibit. A further objective was to examine whether various stress-related factors differ significantly across these behavioral profiles. The sample consisted of 386 secondary school students (M = 13.73; SD = 1.14), of whom 52.6% were female. Data were collected using the Aggression Questionnaire (AQ) by Buss and Perry and the School Situation Survey (SSS) developed by Helms and Gable. Latent profile analysis revealed three distinct profiles: (a) students exhibiting high levels of aggressive behavior (Cluster 1), (b) students showing moderate levels of aggressive behavior (Cluster 2), and (c) students displaying low levels of aggressive behavior (Cluster 3). Students in the high-aggression profile reported significantly higher scores on most sources of stress compared to their peers in the moderate and low aggression profiles. From an educational standpoint, these findings underscore the importance of addressing school-related stressors, as they appear to play a critical role in influencing student behavior. Full article

This study explores the potential of unsupervised machine learning algorithms to identify latent cardiac risk profiles by analyzing ECG-derived parameters from two general groups: clinically healthy individuals (Norm dataset, n = 14,863) and patients hospitalized with heart failure (patients’ dataset, n = 8220). Each dataset includes 153 ECG and heart rate variability (HRV) features, including both conventional and novel diagnostic parameters obtained using a Universal Scoring System. The study aims to apply unsupervised clustering algorithms to ECG data to detect latent risk profiles related to heart failure, based on distinctive ECG features. The focus is on identifying patterns that correlate with cardiac health risks, potentially aiding in early detection and personalized care. We applied a combination of Uniform Manifold Approximation and Projection (UMAP) for dimensionality reduction and Hierarchical Density-Based Spatial Clustering (HDBSCAN) for unsupervised clustering. Models trained on one dataset were applied to the other to explore structural differences and detect latent predispositions to cardiac disorders. Both Euclidean and Manhattan distance metrics were evaluated. Features such as the QRS angle in the frontal plane, Detrended Fluctuation Analysis (DFA), High-Frequency power (HF), and others were analyzed for their ability to distinguish different patient clusters. In the Norm dataset, Euclidean distance clustering identified two main clusters, with Cluster 0 indicating a lower risk of heart failure. Key discriminative features included the “ALPHA QRS ANGLE IN THE FRONTAL PLANE” and DFA. In the patients’ dataset, three clusters emerged, with Cluster 1 identified as potentially high-risk. Manhattan distance clustering provided additional insights, highlighting features like “ST DISLOCATION” and “T AMP NORMALIZED” as significant for distinguishing between clusters. The analysis revealed distinct clusters that correspond to varying levels of heart failure risk. In the Norm dataset, two main clusters were identified, with one associated with a lower risk profile. In the patients’ dataset, a three-cluster structure emerged, with one subgroup displaying markedly elevated risk indicators such as high-frequency power (HF) and altered QRS angle values. Cross-dataset clustering confirmed consistent feature shifts between groups. These findings demonstrate the feasibility of ECG-based unsupervised clustering for early risk stratification. The results offer a non-invasive tool for personalized cardiac monitoring and merit further clinical validation. These findings emphasize the potential for clustering techniques to contribute to early heart failure detection and personalized monitoring. Future research should aim to validate these results in other populations and integrate these methods into clinical decision-making frameworks. Full article

Psychological distress and musculoskeletal pain are interconnected with poor functional outcomes. This study sought to classify common phenotypes of psychological distress in an orthopaedic sports population and assess differences in functional outcomes using the Prediction of Referral and Outcome (OSPRO-YF) tool. This was a cross-sectional study on 411 operative patients from a single sports surgeon’s clinical practice with completed OSPRO-YF questionnaires. Latent class analysis was employed to construct distress phenotypes based on binary measures for 11 single-construct psychological questionnaires, spanning two negative and one positive domains of pain-associated psychological distress. Functional outcome measures, including numerical pain scores, the Patient-Reported Outcomes Measurement Information System (PROMIS), the Single Assessment Numeric Evaluation (SANE) American Shoulder and Elbow Surgeons Score (ASES), and the International Knee Documentation Committee Subjective Knee Form (IKDC), were compared. Four psychological distress clusters were derived: low distress (LD-1; n = 111), low self-efficacy (LS-2; n = 101), negative pain coping, low self-efficacy (NP-3; n = 99), and high distress (HD-4; n = 100), with increasing yellow flags proceeding from LD-1 to HD-4. The mean numerical pain scores were highest in HD-4 and lowest in LD-1 and LS-2 (4.6 vs. 2.7 and 2.0, respectively; p < 0.001). The PROMIS depression scores were highest in HD-4 compared to NP-3, LS-2, and LD-1 (57.0 vs. 48.9 vs. 45.6 vs. 46.0; p < 0.001). Phenotyping patients based on OSPRO-YF distress indicators provides an initial framework of the psychological distress burdening the average orthopaedic sports surgical patient population and may aid in targeted psychological treatments. Full article

Less socially adaptive behaviors have often been underestimated in university students, with limited research addressing their impact on academic functioning. This study aimed to identify distinct profiles of social functioning difficulties in university students and to examine their associations with academic engagement, learning difficulties, and psychological distress. A cross-sectional, web-based survey was conducted with 540 undergraduate university students (mean age = 23.06, SD = 6.53; 89.7% female). Participants completed standardized self-report assessments of social functioning (SRS-2), academic engagement (SAES), learning difficulties (Vinegrad Plus), and psychological distress (GAD-7, PHQ-9). Latent profile analysis (LPA), based on ASD-related traits, revealed two latent profiles: one reflecting non-social functioning difficulties (311 participants, 57.6%—Profile 1) and another reflecting social functioning difficulties (229 participants, 42.4%—Profile 2), while binomial regression analyses examined their associations with academic outcomes. Participants in Profile 2 scored significantly higher than those in Profile 1 across all SRS-2 variables—awareness, cognition, communication, motivation, and restricted interests and repetitive behavior (p = 0.001)—indicating greater overall functioning in these domains. Students in the Non-social functioning difficulties profile showed higher levels of academic engagement in all areas. In contrast, students in the Social functioning difficulties profile experienced more self-reported learning challenges (p = 0.001), anxiety (p = 0.001), and depression (p = 0.001), underscoring the significant differences in social, academic, and emotional outcomes between the two profiles. These findings underscore the impact of vulnerability to social functioning difficulties on academic engagement, highlighting the need for tailored support systems within higher education settings. Full article

Objectives: Feeding and eating disorders (FED) represent a major public health issue and are the second leading cause of death among psychiatric conditions in children and adolescents. Psychopathological comorbidities play a significant role in the onset and persistence of FED, yet research on their underlying structure remains limited. This study explores the psychiatric comorbidities associated with FED, focusing on common etiopathogenetic factors and their clinical implications. Methods: Data were retrospectively collected from the Italian Regional Center for FED in the Emilia-Romagna Region between June 2023 and April 2024. Diagnoses were assigned following DSM-5 criteria using the Italian version of the semi-structured K-SADS-PL diagnostic interview. Principal component analysis (PCA) was performed to identify latent psychological dimensions underlying FED psychopathology, retaining five components based on the scree plot. Additionally, an analysis of covariance (ANCOVA) was conducted to examine differences in factor scores across FED subtypes, while adjusting for potential confounders. Results: Seventy-two participants were included (mean age: 14.6 years; mean BMI: 18.3 kg/m²; male-to-female ratio: 1:8). Diagnoses were distributed as follows: 63.9% anorexia nervosa (AN), 13.9% other specified feeding and eating disorder (OSFED), 6.9% avoidant restrictive food intake disorder (ARFID), 4.2% binge eating disorder (BED), 4.2% unspecified feeding and eating disorder (UFED), and 2.7% bulimia nervosa (BN). All participants met the criteria for at least one psychiatric comorbidity. Identified psychopathological clusters include the following: (1) mood disorders (66.5%); (2) anxiety disorders (87.5%); (3) obsessive–compulsive and related disorders (47.2%); (4) neurodevelopmental disorders, i.e., attention-deficit/hyperactivity disorder (ADHD) (30.5%); (5) disruptive and impulse-control disorders (13.9%); and (6) psychotic symptoms (40.3%). No instances of tic or elimination disorders were detected. Conduct disorder was more prevalent among UFED, BED, and BN patients compared to other FED (p = 0.005), and moderate/severe ADHD was associated with higher body mass index (BMI) (p = 0.035). PCA revealed distinct psychological dimensions underlying FED, while ANCOVA indicated significant differences in factor scores across FED subtypes, supporting the presence of shared transdiagnostic mechanisms. Conclusions: This study highlights the complex interplay between FED and psychiatric comorbidities, emphasizing the need for early intervention and personalized treatment approaches. The dimensional structure identified through PCA suggests that common psychopathological factors may drive FED development, and ANCOVA findings support their differential expression across FED types. Future research should further investigate these transdiagnostic mechanisms to optimize clinical care. Full article

This study investigates the moderating effect of language attitude on the relationships among social media addiction, social pain, and internet trolling among college students. A sample of 891 students from various colleges and universities completed validated measures assessing their levels of social media addiction, social pain, internet trolling, and language attitude. Using a latent variable approach within a multigroup structural equation modeling (SEM) framework, participants were divided into three groups (high, medium, and low language attitude) based on their language attitude scores. The SEM analysis revealed distinct patterns across groups. In the high language attitude group, both social media addiction and social pain significantly predicted internet trolling, with standardized regression coefficients of 0.564 and 0.728, respectively. In the medium language attitude group, the predictive effects remained significant; however, the magnitude of the coefficients decreased markedly (0.264 for social media addiction and 0.562 for social pain). In contrast, in the low language attitude group, neither social media addiction nor social pain emerged as significant predictors of internet trolling. Interestingly, the covariance between social media addiction and social pain remained consistent across the three groups, suggesting a stable interrelationship irrespective of language attitude level. These findings imply that language attitude plays a crucial moderating role in the interplay among social media addiction, social pain, and internet trolling. Specifically, higher levels of language attitude appear to amplify the effects of social media addiction and social pain on internet trolling behavior, while lower levels attenuate these associations. The results underscore the importance of considering individual differences in language attitudes when developing intervention strategies aimed at mitigating problematic online behaviors among college students. Full article

Using data from Round 11 of the European Social Survey, covering Switzerland, this article employs Structural Equation Modeling to investigate the associations between psychosocial factors and pro-environmental behavior. Based on the Theory of Planned Behavior, which integrates subjective norms, perceived behavioral control, and attitudes to predict behavioral intentions and actual pro-environmental behaviors. The findings reveal that perceived social norms are strong predictors of pro-environmental behavioral intentions, illustrating the power of expectations from other people on the propensity to act sustainably. However, the effects of attitudes toward the environment and perceived behavioral control on behavioral intention were minimal or unobserved. It indicates that respondents may support environmental and sustainability issues, but this is often not matched by strong intentions to act. Additionally, the perceived control over pro-environmental behavior does not directly affect actual behavior. This suggests that external barriers may prevent individuals from performing sustainable behavior, even if they are motivated. The measurement invariance across gender groups was also examined in the study, which is essential to confirm the equivalence of measurements, and observed differences in latent variables could not be an artifact due to potential measurement bias. The findings suggest that subjective norms and pro-environmental behaviors are more likely to be higher among women, while men scored higher on perceived control. This indicates that women internalize social expectations stronger than men do to act, but for men, they feel able to act, but this confidence does not imply action. This research focuses on the significance of social influences in ecologically friendly action and on the discrepancies between intention and behavior. This study adds to our understanding of the determinants of pro-environmental behavior, providing relevant insights for policymakers to inform more effective interventions that close the gap between awareness and action. Full article

With the advancement of agricultural automation, the demand for road recognition and understanding in agricultural machinery autonomous driving systems has significantly increased. To address the scarcity of instance segmentation data for rural roads and rural unstructured scenes, particularly the lack of support for high-resolution and fine-grained classification, a 20-class instance segmentation dataset was constructed, comprising 10,062 independently annotated instances. An improved StyleGAN2-ADA data augmentation method was proposed to generate higher-quality image data. This method incorporates a decoupled mapping network (DMN) to reduce the coupling degree of latent codes in W-space and integrates the advantages of convolutional networks and transformers by designing a convolutional coupling transfer block (CCTB). The core cross-shaped window self-attention mechanism in the CCTB enhances the network’s ability to capture complex contextual information and spatial layouts. Ablation experiments comparing the improved and original StyleGAN2-ADA networks demonstrate significant improvements, with the inception score (IS) increasing from 42.38 to 77.31 and the Fréchet inception distance (FID) decreasing from 25.09 to 12.42, indicating a notable enhancement in data generation quality and authenticity. In order to verify the effect of data enhancement on the model performance, the algorithms Mask R-CNN, SOLOv2, YOLOv8n, and OneFormer were tested to compare the performance difference between the original dataset and the enhanced dataset, which further confirms the effectiveness of the improved module. Full article

This research aims to investigate the roles of ontology and Semantic Web Technologies (SWT) in modern knowledge representation and data management. By analyzing a dataset of 10,037 academic articles from Web of Science (WoS) published in the last 6 years (2019–2024) across several fields, such as computer science, engineering, and telecommunications, our research identifies important trends in the use of ontologies and semantic frameworks. Through bibliometric and semantic analyses, Natural Language Processing (NLP), and topic modeling using Latent Dirichlet Allocation (LDA) and BERT-clustering approach, we map the evolution of semantic technologies, revealing core research themes such as ontology engineering, knowledge graphs, and linked data. Furthermore, we address existing research gaps, including challenges in the semantic web, dynamic ontology updates, and scalability in Big Data environments. By synthesizing insights from the literature, our research provides an overview of the current state of semantic web research and its prospects. With a 0.75 coherence score and perplexity = 48, the topic modeling analysis identifies three distinct thematic clusters: (1) Ontology-Driven Knowledge Representation and Intelligent Systems, which focuses on the use of ontologies for AI integration, machine interpretability, and structured knowledge representation; (2) Bioinformatics, Gene Expression and Biological Data Analysis, highlighting the role of ontologies and semantic frameworks in biomedical research, particularly in gene expression, protein interactions and biological network modeling; and (3) Advanced Bioinformatics, Systems Biology and Ethical-Legal Implications, addressing the intersection of biological data sciences with ethical, legal and regulatory challenges in emerging technologies. The clusters derived from BERT embeddings and clustering show thematic overlap with the LDA-derived topics but with some notable differences in emphasis and granularity. Our contributions extend beyond theoretical discussions, offering practical implications for enhancing data accessibility, semantic search, and automated knowledge discovery. Full article

Background: The traditional presentation of results of cognitive test and surveys using simple percentages or average score obscures topics failed or mastered by test takers. However, the Rasch technique revolutionises the presentation of a test result by connecting respondent latent knowledge (or ability) with the test items using Wright maps. Aim: To assess nurses and midwives’ knowledge of managing primary postpartum haemorrhage using a Wright map Methods: A twelve-item dichotomous (YES/NO) computer-based test developed from the recently updated WHO’s treatment bundle was presented to the respondents for fifteen minutes. A nine-member panel reviewed the test to ensure clarity and relevance to Nigeria’s public maternity setting. All the respondents were nurses and midwives with previous experience of responding to primary postpartum haemorrhage. Ethical approval was provided by the University of Huddersfield and the nurses’ association. After eight weeks of data collection, both descriptive and inferential analyses were conducted using a Wright map. Results: The 180 responses analysed on a Wright map showed that the lowest, average and highest measures to be 476.3 logits, 495.9 logits and 521.7 logits respectively. Also, 178 (98%) respondents incorrectly answered the question on the source of treatment evidence but correctly answered that uterine atony is the main cause of postpartum haemorrhage. However, all the respondents who scored below average (495.6 logits) incorrectly answered the question on oxytocin as the best uterotonic. Conclusions: Wright map analysis confirms the problem of the inadequate knowledge of maternity staff as a major barrier to the effective treatment of maternal bleeding. A significant difference was found in the knowledge of the treatment among the three levels of maternity settings, implying the need for effective educational intervention strategies. Full article

Maintaining proper hand hygiene is crucial in preventing the spread of infections and other communicable diseases. It is imperative to determine the factors that affect the likelihood of initiating and maintaining the recommended handwashing behavior, especially during a pandemic. This quantitative, secondary study employed a latent profile analysis (LPA) to identify the different attitudes toward behavior change based on the Multi-Theory Model (MTM) framework in the context of following the guidelines provided by the Centers for Disease Control and Prevention (CDC) during the COVID-19 pandemic. Data were collected from 602 college students at a large university in the southern region of the United States (U.S.) in 2020. Seven distinct profiles were identified, each reflecting unique attitudes toward following the recommended handwashing guidelines. Age (p < 0.01) and gender (p < 0.01) disparities were observed between profiles. This study is the first to apply LPA within the MTM framework and provides new insights for the development of targeted interventions based on the construct score profile. Full article

Innovation is recognized as a key source of competitive advantage for organizations and a driver of societal well-being. Therefore, managing and quantifying innovation is necessary to fully leverage its potential. Although many innovation measurement tools exist, contextual differences limit their applicability, highlighting the need for tools tailored to regional and national specificities. This study aimed to develop a measurement tool for the Ecuadorian context, utilizing the Capacities, Results, and Impacts (CRI) model questionnaire for data collection and Item Response Theory (IRT) as a processing method. The CRI model offers a comprehensive framework for assessing innovation potential, enabling a dynamic understanding of this potential while highlighting nuances specific to regional contexts, particularly in Ecuador. Complementing this, IRT—a statistical framework for measuring latent traits—offers several advantages over classical methodologies, such as Classical Test Theory (CTT) and simple aggregate scoring methods. Unlike classical approaches, which often lack precision at extreme ability levels and are heavily sample-dependent, IRT provides item-level analysis, ensures parameter invariance across samples, and maintains accuracy across a wide range of latent trait levels. Together, these methodologies ensure a highly reliable and context-specific innovation measurement tool. Six IRT models were fitted using data from a national multisector sample of 321 organizations, providing a multidimensional measurement scale specialized in measuring innovation potential in Ecuador. These scales were later applied in a case study on the Information and Communication Technology (ICT) sector in Quito, Ecuador. The findings showed that ICT organizations had higher innovation potential than other industries in Ecuador but faced weaknesses in areas like access to funding. Based on these results, targeted strategies were proposed to address these weaknesses and foster innovation within the ICT sector. This research contributes to the field of managing innovation in Ecuador and the broader Latin American region by developing a context-specific and adaptable tool to benchmark innovation, guide organizational strategies, and shape public policy. While the ICT sector was identified as a key driver for addressing Ecuador’s innovation challenges, this research provides valuable contributions toward tackling these challenges and fostering innovation in the country. Full article