Search Results (1,133)

Background: Quality-of-life (QoL) assessment has become an essential component of outcome evaluation in oral and maxillofacial surgery (OMFS), particularly in interventions with functional implications for breathing, sleep, and oro-facial performance. Existing instruments often lack specificity for the postoperative OMFS population. This study aimed to develop and psychometrically validate the OMFS-QoL-18 questionnaire, a condition-oriented patient-reported outcome measure designed for postoperative assessment. Methods: A cross-sectional validation study was conducted on 226 adult patients evaluated 6–12 months after orthognathic or function-oriented OMFS procedures. Internal consistency was assessed using Cronbach’s alpha, and reproducibility using the intraclass correlation coefficient (ICC) based on a two-way random-effects model with absolute agreement. The internal structure of the instrument was explored through an exploratory dimensionality analysis using Principal Component Analysis (PCA), including Kaiser–Meyer–Olkin (KMO) testing and Bartlett’s test of sphericity. Descriptive statistics were calculated for item and domain scores. Results: The OMFS-QoL-18 demonstrated good internal consistency (Cronbach’s α = 0.789; standardized α = 0.783) and satisfactory reproducibility (ICC = 0.81; 95% CI: 0.74–0.87). The exploratory dimensionality analysis suggested a multidimensional structure, with five components explaining 67.1% of the total variance. Item clustering was broadly consistent with the predefined conceptual domains, including respiratory comfort, sleep quality, daytime function, oro-maxillofacial function, and global satisfaction. Given the use of PCA as a component-based method, these findings are interpreted as preliminary evidence of dimensional organization rather than confirmation of latent constructs. Conclusions: The OMFS-QoL-18 demonstrated good internal consistency and preliminary evidence of a coherent factor structure. These findings support its use as a promising condition-specific instrument, pending further validation studies. Further multicenter and longitudinal validation studies are warranted to confirm structural stability and responsiveness over time. Full article

Background/Objectives: Sleep disturbances have an impact on children’s physical and psychological development, yet little is known about the changes and factors influencing sleep after PICU discharge. To explore the trajectory of changes in sleep quality of critically ill children and to identify factors affecting sleep quality three months after Pediatric Intensive Care Unit (PICU) discharge. Methods: A longitudinal observation study was conducted between November 2022 and November 2023 at a tertiary children’s hospital. The Children’s Sleep Habits Questionnaire (CSHQ) was used at six time points: PICU-admission (T0), 1 week (T1), 2 weeks (T2), 1 month (T3), 2 months (T4), and 3 months (T5) after PICU discharge. The CSHQ is a 33-item parent-report outcome measure evaluating sleep problems. Total scores range between 33 and 99 points. A score of ≤41 indicates normal sleep; a score of >41 indicates sleep disturbance. Data were analyzed using the latent category growth model, univariate analysis, and multifactorial logistic regression. Results: Parents of 237 children completed all follow-up surveys. Prevalence of sleep disorders at T0-T5 of children with a score >41 were 46.5%, 83.5%, 69.7%, 54.3%, 50.2%, and 51.7%, respectively. General linear modeling revealed significant changes in CSHQ scores over time (F = 63.77, p < 0.05). The trajectories of identifying sleep changes were divided into three latent categories: High Sleep Disorder Group (n = 15, 6.33%), Moderate Sleep Disorder Group (n = 110, 45.2%), and No Sleep Disorder Group (n = 115, 48.52%). The trajectories were significantly different among preschool age, baseline sleep habit scores, surgery, and length-of-stay in pediatric wards (p < 0.05). The child’s age and surgical history were independent factors of sleep disturbance. Conclusions: The observed peak in sleep disturbances at 1-month post-PICU suggests that this period may be a critical window to develop and implement targeted interventions to improve sleep. The persistent sleep disorders highlight the need for long-term monitoring. Full article

Background/Objectives: Motor competence is a key indicator of children’s developmental readiness and an important component of health and well-being education. It is conceptualized as a latent construct shaped by both individual and contextual factors. The objective of this study was to examine the influence of sex, age and class context on motor competence, with particular emphasis on skill-specific and contextual variability. Methods: Motor competence was assessed in 312 Greek primary school children aged 6–12 years (156 girls) using the Movement Assessment Battery for Children–Second Edition. Standard scores for manual dexterity, aiming–catching, and balance were analyzed using a multilevel modeling approach. Results: Balance showed the highest standard scores, while manual dexterity was the lowest-performing domain. Boys outperformed girls in aiming–catching, with a modest effect. Age effects were domain-specific, with relative age within the classroom negatively associated with manual dexterity but not with other domains. Class-level factors explained substantial variance, indicating heterogeneity across classes. Conclusions: Motor competence in primary school children is strongly domain-specific and meaningfully associated with classroom context. Manual dexterity emerges as a potential priority for curriculum development, and age-related effects appear to operate selectively across domains. Full article

Parental involvement is widely recognized as a key component of effective schooling, particularly in educational environments that emphasize community, developmental continuity, and holistic pedagogy. Alternative education models such as Waldorf schools have expanded internationally, yet empirical evidence on how parents perceive and structure their experiences within these institutions remains limited. This study investigates parental perceptions of Waldorf education in Hungary through a nationwide questionnaire survey of 585 parents whose children attend Waldorf schools. To explore the latent structure of parental evaluations, Exploratory Factor Analysis was conducted, followed by Confirmatory Factor Analysis to test the stability of the resulting model. The analyses identified four coherent dimensions of parental experience: Trust and Pedagogy, Community and Engagement, Perceived Long-Term Educational Prosperity, and Information and Transparency. Additional analyses examined how these dimensions vary according to institutional characteristics, parental participation in school community activities, and intentions regarding long-term enrollment. The results indicate that pedagogical trust constitutes a relatively stable evaluative dimension across institutions, while perceptions related to community engagement, long-term educational prospects, and transparency are more strongly associated with institutional maturity. Parents who intend to remain in Waldorf education until the completion of upper secondary schooling report consistently higher evaluations across all dimensions. By empirically identifying the structure of parental experiences in a European alternative education context, the study contributes to research on parental engagement, school choice, and the institutional cultures of alternative schooling. Full article

Rework continues to be a critical issue in construction projects, contributing to cost escalation, schedule delays, and compromised quality. While earlier studies have identified isolated causes such as design deficiencies, communication failures, and inadequate workmanship, the structural relationships among these factors have not been sufficiently examined. This study investigates the interdependencies among major rework causation domains using Structural Equation Modelling (SEM) based on survey responses from 200 construction professionals. A total of 43 observed variables, identified through an extensive literature review, were grouped into four latent constructs: contractor-related, owner-related, design-related, and resource/workforce-related factors. Confirmatory Factor Analysis (CFA) was conducted to validate the measurement model, followed by structural path analysis to examine causal linkages. The findings reveal that design-related and owner-related factors exert the most significant direct and indirect influence on rework, followed by contractor- and workforce-related factors. The proposed model demonstrates satisfactory goodness-of-fit indices, confirming its reliability and applicability. Compared to conventional ranking and fuzzy-based approaches, SEM provides a more systematic and comprehensive understanding of rework dynamics. The findings provide practical guidance for project managers and decision-makers by identifying the most critical drivers of rework, enabling targeted mitigation strategies and improved resource allocation to enhance overall construction project performance. Full article

In the context of the aesthetic economy and the rapid development of digital intelligence, product design is increasingly required to address not only functional performance but also users’ emotional needs. However, due to the ambiguity and subjectivity of perceptual requirements, it remains difficult to accurately translate user emotions into specific design solutions. To address this challenge, this study proposes an integrated Kansei Engineering–machine learning framework for optimizing product design. First, user perceptual data are collected through questionnaires and interviews, and key perceptual imagery words are extracted using the Latent Dirichlet Allocation (LDA) model and factor analysis. Then, product design elements are systematically decomposed, and their relative importance is determined using the fuzzy analytic hierarchy process (FAHP). Based on this, a mapping relationship between perceptual imagery and design elements is established. Subsequently, the XGBoost model is employed to predict and optimize design element combinations. The optimized design schemes are further generated using AIGC technology and validated through eye-tracking experiments and subjective evaluations.The results show that the proposed method achieves high predictive accuracy (R² = 0.87) and significantly improves the emotional expression of product design. This study contributes to the integration of Kansei Engineering and machine learning by providing a data-driven approach for emotional design optimization, offering theoretical, practical, and strategic guidance for intelligent product design in industrial contexts. Full article

The adoption of disruptive technologies has become increasingly critical for organizations, particularly following the global shifts prompted by the COVID-19 pandemic. Despite the potential benefits, many organizations, including those in the Fast-Moving Consumer Goods (FMCG) industry, face significant hurdles in this transition. Consequently, this study aims to understand the primary challenges and enabling factors influencing the adoption of disruptive technologies for sustainable digital transformation within the South African FMCG sector. A quantitative methodology was employed, utilizing a questionnaire for data collection. Data from 102 respondents were analyzed using SPSS version 28, involving descriptive statistics (mean item score) to rank factors and exploratory factor analysis (EFA) to identify underlying constructs, and a reliability test was carried out with a score of 0.7. Key challenges identified include high initial costs and poor collaboration. Prominent enabling factors include top management commitment and operational cost reduction. The EFA revealed significant underlying challenge dimensions such as “Infrastructural and Resources Constraints” and “Human Factors Constraints,” and enabling dimensions including “Organizational Commitment and Strategy” and “Leadership.” The study concludes with key implications for promoting successful adoption. The adoption of disruptive technologies has become a strategic imperative for sustainable digital transformation (SDT), particularly in emerging markets such as South Africa’s FMCG sector. This study investigates the key challenges and enabling factors shaping technology adoption within this context. A quantitative methodology was employed, using a structured questionnaire distributed to 102 professionals across FMCG organizations in Gauteng. Exploratory factor analysis (EFA) revealed latent dimensions within both challenges and enablers, which were then interpreted through the lens of Rogers’ Diffusion of Innovation (DOI) theory. To enhance analytical clarity, a matrix model was developed linking factor dimensions to DOI attributes such as relative advantage, complexity, compatibility, trialability, and observability. The study found that high initial costs, poor collaboration, and human capability gaps significantly impede adoption, while strong leadership, strategic alignment, and operational cost savings facilitate it. The findings underscore the need for systemic interventions that address not only technical readiness but also leadership, organizational culture, and structural alignment. Practical implications are outlined for both policy and management, particularly in leveraging DOI attributes to accelerate digital transformation, as well optimize innovation diffusion within resource-constrained environments. For the future, the study proposed a hybrid methodology incorporating qualitative interviews to enhance depth and suggests longitudinal tracking to capture temporal shifts in transformation maturity. Full article

In the realm of intelligent transportation systems (ITS), achieving optimal system performance relies heavily on the acquisition of comprehensive and high-quality spatio-temporal traffic data. In practical data-gathering processes, factors such as sensor malfunctions or communication interruptions often lead to incomplete or missing data records, which in turn substantially hinder the advancement of ITS applications. To address missing spatio-temporal data, a widely adopted paradigm involves the Latent Factorization of Tensors (LFT) model. Traditional LFT frameworks often employ the standard L₂ metric in their learning objective, making them easily affected by abnormal data points. Moreover, impulse noise frequently arises in sensors and communication scenarios. To address these limitations, this paper develops an Adaptive L_p,ϵ-norm-incorporated Latent Factorization of Tensors (L_p,ϵLFT) model founded on two-fold concepts: (a) constructing a generalized objective function grounded in the L_p,ϵ-norm distance to enhance robustness against outliers; (b) realizing the self-adaptation of model hyper-parameters through a fuzzy controller to enhance model practicality. Experimental evaluations on six traffic speed datasets derived from multiple metropolitan traffic networks demonstrate that the proposed L_p,ϵLFT model yields significantly higher imputation accuracy and superior computational efficiency compared with seven state-of-the-art approaches. Full article

Voluntary turnover represents a critical challenge in essential public services, where workforce attrition affects both employee well-being and service quality. The primary objective of this study was to identify the psychosocial predictors of well-being profiles and turnover intention among public transport workers, using the Job Demands–Resources model as a theoretical framework. A cross-sectional study design was employed, with 131 employees of an Italian public transport company completing a questionnaire assessing turnover intention and key psychosocial factors (job satisfaction, perceived work-related stress, work engagement, meaning of work, and perceived workplace safety). The analytical strategy integrated Latent Profile Analysis (LPA), logistic regression, and path analysis. LPA identified two distinct well-being profiles: a “low well-being profile,” with high perceived stress and low engagement and meaning of work; and a “high well-being profile,” with low stress and high engagement and work meaning. Logistic regression analyses showed that satisfaction with pay and the intrinsic nature of work tasks predicted membership in the high well-being profile. Path analysis indicated that profile membership significantly predicted turnover intention, with employees in the high well-being profile reporting lower turnover intention. Additionally, satisfaction with supervision, perceived workplace safety, and age showed direct effects on turnover intention. These findings highlight the organizational and psychological resources that can increase employee well-being and retention in the public transport sector, offering insights for preventive interventions and for promoting safer and more sustainable public transport systems. Full article

To unveil the underlying risk structures in complex industrial systems, this paper proposes a hybrid analytical framework that integrates BERTopic modeling, a large language model (LLM), and social network analysis (SNA). This framework aims to extract systemic safety intelligence from unstructured accident reports. It first employs BERTopic to identify latent causal topics based on 745 Chinese accident investigation reports and utilizes DeepSeek-V3.1 (LLM) for semantic refinement and causal mapping of these topics. Subsequently, a semantic network of causal keywords based on positive pointwise mutual information (PPMI) is constructed, and its topological structure is analyzed using SNA methods. The study identifies and analyzes five major risk communities: confined spaces, fire, mining, construction, and road traffic. It reveals that accident causation exhibits the small-world characteristics of multi-factor coupling and non-linearity, with core risk nodes concentrated in systemic inducements such as organizational management and compliance deficiencies. The results demonstrate that this framework effectively identifies the latent systemic risk patterns embedded within the texts, providing methodological support for developing sustainable safety management mechanisms based on design for safety. Full article

Objectives: Cardiovascular diseases (CVDs) represent the main global burden of morbidity and mortality, with an accelerated epidemiological transition in regions such as Latin America. The university environment constitutes a period of critical vulnerability due to increased sedentary lifestyles and cardiometabolic risk factors. The objective of this study was to evaluate the cardiovascular risk profile in a university community in the central Andean region of Colombia using anthropometric, haemodynamic and biochemical indicators. Methods: A cross-sectional, observational, and analytical study was conducted on a sample of n = 143 participants (students, teachers, and administrators) aged between 18 and 80 years. Haemodynamic parameters (SBP, DBP, MAP), anthropometric parameters (BMI, % body fat, waist-to-height ratio [WC/W]) and lipid profile were evaluated. Statistical analysis included multiple linear regression models to determine predictors of systolic blood pressure (SBP). Results: Significantly higher levels of SBP were found in the older age groups compared with the younger age groups, reaching stage 1 hypertension levels in the sixth decade. The biochemical profile revealed metabolic deterioration with an atherogenic index (TC/HDL) consistently above the clinical threshold (>4.5) in all groups. The regression model BMI was identified as the statistical predictor with the strongest association with SBP variability in the sample (β = 1.18), followed by age (β = 0.28). A marked sexual dimorphism was observed, with men presenting early haemodynamic risk, while women experienced an accelerated post-menopausal tension and metabolic crisis. Conclusions: The university community presents latent cardiometabolic vulnerability closely linked to modifiable anthropometric factors. These findings underscore the urgency of implementing institutional preventive health policies and weight control intervention programmes to mitigate the future burden of chronic diseases on campus. Full article

Background: Physical literacy is a multidimensional construct encompassing physical competence, confidence, motivation, knowledge, and lifelong engagement in physical activity. The Perceived Physical Literacy Instrument (PPLI) has been widely used internationally; however, previous adolescent validations have been based on a reduced 9-item version originally developed for teachers. This study aims to evaluate the validity and test–retest reliability of a Spanish adaptation of the original 18-item PPLI in Spanish adolescents aged 11–18 years. Methods: A multi-phase validation study was conducted with 869 Spanish adolescents (421 females). The procedure included: (1) translation and cultural adaptation, (2) Exploratory Factor Analysis (EFA; n = 290), Confirmatory Factor Analysis (CFA; n = 579) and invariance analyses, and (3) test–retest reliability assessment. Results: EFA supported a three-factor solution comprising 15 items. CFA showed standardized factor loadings ranging from 0.62 to 0.89, indicating that the latent constructs were adequately represented. Although the 15-item model showed acceptable fit, a 5-item unidimensional short form was developed due to limitations in the three-dimensional models. This short form demonstrated good model fit (scaled RMSEA = 0.073; scaled CFI = 0.992; SRMR = 0.026), adequate convergent validity (AVE = 0.558), high reliability (ω = 0.821), moderate test–retest stability (ICC = 0.69), and full configural, metric, and scalar longitudinal invariance. Conclusions: The 15-, 9-, and 5-item versions of the PPLI are valid and reliable options. The 15-item version allows comprehensive assessment and domain-level interpretation. The 9-item version facilitates comparability with previous international research. The 5-item version may be useful in contexts with time constraints but may not be the preferred choice for comprehensive assessment of physical literacy in clinical or detailed pedagogical diagnostic settings. Full article

Background/Objectives: Patients with breast cancer show substantial heterogeneity in terms of psychological adjustment following diagnosis. We aimed to characterize longitudinal trajectories of quality of life (QoL) and depressive symptoms during the first 18 months post-diagnosis and to identify robust clinical, psychosocial, and behavioral predictors associated with distinct adjustment pathways. Methods: Women (N = 538; mean age 55.4 years; range 40–70) with operable breast cancer (stages I–III) were drawn from the multicenter BOUNCE cohort. QoL (Global Health Status/QoL scale of the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire Core 30) and depressive symptoms (depression subscale of the Hospital Anxiety and Depression Scale) were assessed at baseline and months 3, 6, 9, 12, 15 and 18. Latent class growth analysis and growth mixture modeling identified distinct trajectory classes. Associations between early predictors and trajectory membership were examined using logistic regression combined with elastic net regularization. Results: Depression trajectories demonstrated heterogeneity, with groups characterized by persistent resilience (59.7%), stable moderate/high (25.3%), delayed onset (5.0%), and recovery (10.0%). QoL trajectories ranged from stable excellent (13.2%) and stable high (40.7%) to moderate (31.4%) and persistent low/deteriorating (6.9%), as well as a distinct recovering trajectory (7.8%). Trajectory differentiation was primarily driven by psychological resources, symptom burden, functional status, and coping processes, alongside specific contributions from clinical factors. Conclusions: Distinct subgroups of women with breast cancer follow divergent adjustment pathways. These findings highlight the multidimensional nature of resilience and support the need for tailored interventions that promote long-term well-being beyond simple risk reduction. Full article

This study evaluates the factors associated with municipal solid waste management performance under a circular economy approach in the municipalities of Cajamarca, Peru. A hybrid analytical design was applied to 120 municipal observations, combining partial least squares structural equation modeling to estimate the measurement and structural properties of four latent constructs—legal-regulatory framework, institutional capacity, operational management, and perceived performance—and XGBoost with SHAP to explore predictive classification of participation in circular economy training. The structural results indicate that operational management plays the central articulating role in linking regulation and institutional capacity to perceived performance, whereas the predictive component showed only modest out-of-sample discrimination (AUC-ROC = 0.519). Overall, the findings suggest that the proposed hybrid pipeline is more informative for explanatory integration and variable-importance analysis than for strong predictive discrimination under the current specification. Full article

Background: The integration of artificial intelligence (AI) into lung cancer screening offers significant potential; however, public adoption of AI-assisted lung cancer screening remains inconsistent and poorly understood. A robust understanding of the psychological and social determinants underlying adoption is critical to inform evidence-based implementation strategies. Objective: This study aims to identify the key factors that influence the public’s Behavioral Intention (BI) to adopt AI-assisted lung cancer screening. We built on the Unified Theory of Acceptance and Use of Technology (UTAUT) and integrated Doctor–Patient Trust and Perceived Risk into the framework to examine the associations between these medically specific factors, together with traditional adoption variables, and the public’s BI. Methods: A cross-sectional survey was conducted among 971 residents in China from September to November 2025. Based on the extended UTAUT, a measurement instrument was developed and refined through expert consultations and pilot testing. Exploratory factor analysis (EFA) was performed to validate the questionnaire’s construct validity. Hypothesis testing was then carried out via Partial Least Squares Structural Equation Modeling (PLS-SEM) to evaluate the measurement model and examine the structural relationships among latent constructs. Results: EFA results indicated a seven-factor structure (KMO = 0.897, p < 0.001). The structural model accounted for 35.0% of the variance in BI. Social Influence (β = 0.292, p < 0.001), Facilitating Conditions (β = 0.156, p < 0.001), Performance Expectancy (β = 0.101, p = 0.004), Doctor–Patient Trust (β = 0.107, p = 0.002) were positively associated with BI, while Perceived Risk (β = −0.106, p < 0.001) showed a negative association. Furthermore, Doctor–Patient Trust was significantly and negatively associated with Perceived Risk (β = −0.168, p < 0.001), suggesting a potential mediating pathway from trust to intention (Indirect Effect = 0.018, p = 0.003). Conclusions: Adoption of AI-assisted lung cancer screening appears to be associated not only with perceived utility but also with trust in medical professionals and Perceived Risk. These findings suggest the importance of integrating technological innovation with strategic public education and tailored communication strategies to foster its adoption. Public health interventions should leverage physician endorsements and promote AI awareness to support informed, trust-based engagement with AI technologies. Full article