Search Results (3,690)

Background/Objectives: Early Childhood Intervention (ECI) services are critical for supporting children with developmental needs and their families. Despite an established legislative framework, challenges related to accessibility, equity, resources, and standardization of practices persist. This study aimed to describe the perspectives of early intervention professionals in Portugal regarding current barriers, facilitators, and priority areas for improvement within the system. Methods: A descriptive study was conducted involving 82 professionals working in early intervention in Portugal. Data were collected using a survey specifically developed by the research team, grounded in a comprehensive literature review and professional expertise. The instrument was validated through a Delphi Panel with two rounds involving six experts in ECI. Data from open-ended questions were analyzed using content analysis, identifying categories and sub-categories to describe the responses, and descriptive statistics for the closed-ended questions. Results: Professionals highlighted the need to update the National ECI System (SNIPI), improve accessibility, and ensure equitable access to early intervention services. Participants reported limited resources, a lack of standardization in practices, and emphasized the importance of professional training and continuous professional development. The findings also pointed to the urgent need for investment and functional and structural restructuring of early intervention services. Various barriers and facilitators were identified. Conclusions: The study provides valuable insights into the perspectives of early intervention professionals, identifying critical areas for policy improvement, resource allocation, and practice standardization. Full article

Objective: To evaluate the level of knowledge, attitudes, and practices of Hungarian general practitioners (GPs) concerning human papillomavirus (HPV) infection, cervical cancer prevention, and HPV vaccination, and to identify physician-level factors associated with proactive recommendation practices. Methods: A cross-sectional nationwide survey was conducted between 30 April and 1 June 2024. The online questionnaire was distributed to practicing Hungarian GPs listed in the National Health Insurance Fund database. Anonymous responses were collected on demographic data, knowledge of HPV transmission and oncogenic potential, awareness of vaccination guidelines, and clinical counseling habits. Descriptive and inferential statistical analyses were performed. A total of 413 responses were received. Results: Most respondents were female (72.6%) with an average of 22.4 ± 9.6 years of professional experience. Although 89.8% correctly identified the causal link between HPV and cervical cancer, only 56.2% were aware of the complete vaccination schedule recommended for adolescents initiating after age 15. Knowledge scores were significantly higher among female physicians, urban practitioners, and those with postgraduate preventive medicine training. While the overall attitude toward HPV vaccination was positive (mean 4.6/5), 38.4% of respondents reported parental hesitancy as a common barrier, often citing misinformation regarding vaccine safety (64.9%) and lack of perceived need for boys (58.7%). Regression analysis revealed that familiarity with WHO and national vaccination guidelines independently predicted proactive vaccine recommendation (β = 0.43, p < 0.001). Conclusions: Hungarian general practitioners demonstrate good baseline awareness of HPV and its oncogenic role; however, knowledge gaps persist regarding vaccination schedules and counseling practices. Enhancing continuous medical education and communication training could strengthen GPs’ role as key advocates in HPV vaccine promotion. Full article

The objective of this work is to propose a simulation strategy for production planning that is compatible with the dynamism of natural gas processing, especially under an open-market arrangement, in which several scheduling simulations must be performed within short time horizons. In such contexts, traditional first-principles-based approaches, although accurate, require prohibitive computational times, motivating the need for an alternative simulation strategy. This work thus proposes a data-driven model built with the aid of machine learning and applied in a case study with historical data from the largest gas processing site in Brazil: Cabiúnas Petrobras asset. Main plant flowrates were selected: 18 targets and 44 input candidates—1282 observations from three and a half years of operation. Principal Component Analysis was used for order reduction, keeping the 22 main principal components. A forward neural network (2 hidden layers and 225 neurons per layer) was built from training/test sets randomly selected and optimized hyperparameters—learning rate (0.001533) and batch size (8). Training converged in roughly 200 epochs (Adam optimizer), with early stop triggered by the validation set. A mean absolute error of 0.0017 (test set) and R² = 0.72 were found, a promising result considering plant complexity and data simplicity. Results showed a particularly good fit for lighter products (sales gas and natural gas liquid), also indicating an opportunity for further work by including inputs related to liquid fractionation. Full article

Background/Objectives: The rising prevalence of extended-spectrum beta-lactamase (ESBL)-producing pathogens has emerged as a significant challenge in the treatment of pyelonephritis. This study aims to determine the frequency of ESBL-producing agents in hospitalized patients with pyelonephritis, identify associated risk factors, and assess the appropriateness of empirical antimicrobial therapy. Methods: This prospective study included patients hospitalized with pyelonephritis in the Infectious Diseases Clinic of Ankara Training and Research Hospital between 1 October 2022 and 29 February 2024. Demographic features, comorbidities, urinary system pathologies, history of urinary tract interventions, hospitalization more than one month prior, antibiotic use within the previous three months, and prior urinary tract infections were compared between patients infected with ESBL-producing and non-ESBL-producing organisms. Antimicrobial susceptibility profiles and the appropriateness of empirical treatments were evaluated. Statistical analyses were performed using SPSS version 25.0, with p< 0.05 considered statistically significant. Results: Escherichia coli (n = 142) and Klebsiella spp. (n = 43) were isolated in 180 of 204 patients. ESBL positivity was detected in 95 patients (52.7%). In the multivariate logistic regression analysis, male sex (p = 0.038) and hospitalization more than one month prior (p = 0.016) were identified as independent risk factors for ESBL positivity, while prior antibiotic use in the last three months showed a borderline association (p = 0.055) and did not reach statistical significance. ESBL production was not associated with prolonged hospitalization; however, bacteremia significantly increased length of stay (p< 0.001). Antimicrobial susceptibility rates were markedly lower in the ESBL-positive group. The appropriateness of empirical therapy was also significantly reduced, with piperacillin–tazobactam being the most frequently inappropriate agent due to high resistance rates and unnecessary broad-spectrum use. Conclusions: ESBL-producing pathogens were highly prevalent among hospitalized patients with pyelonephritis. The low appropriateness of empirical therapy in ESBL-positive cases underscores the need for careful evaluation of ESBL risk factors prior to treatment initiation, as ESBL rates may approach 50%. Full article

Class imbalance in tabular datasets poses a challenge for machine learning classification tasks, often leading to biased models that underperform in predicting minority class instances. This study presents a comparative analysis of synthetic data generation methods for addressing class imbalance in tabular data. We evaluate four augmentation approaches—Synthetic Minority Over-sampling Technique (SMOTE), Gaussian Copula, Tabular Variational Autoencoder (TVAE), and Conditional Tabular Generative Adversarial Network (CTGAN)—using the University of California Irvine (UCI) Bank Marketing dataset, which exhibits a class imbalance ratio of approximately 7.88:1. Our experimental framework assesses each method across three dimensions: statistical fidelity to the original data distribution evaluated through four complementary metrics (marginal numerical similarity, categorical distribution similarity, correlation structure preservation, and Kolmogorov–Smirnov test), machine learning utility measured through classification performance, and minority class detection capability. Results indicate that all augmentation methods achieved statistically significant improvements over the baseline ($p<0.05$). SMOTE achieved the highest recall (54.2%, a 117.6% relative improvement over the baseline) and F1-Score (0.437, +22.4% over the baseline) for minority class detection, while Gaussian Copula provided the highest composite fidelity score (0.930) with competitive predictive performance. A weak negative correlation ($\rho =-0.30$) between composite fidelity and classification performance was observed, suggesting that higher statistical fidelity does not necessarily translate to better downstream task performance. Deep learning-based methods (TVAE, CTGAN) showed statistically significant improvements over the baseline (recall: +58% to +63%) but underperformed compared to simpler methods under default configurations, suggesting the need for larger training samples or more extensive hyperparameter tuning. These findings offer reference points for practitioners working with moderately imbalanced tabular data with limited minority class samples, supporting the selection of generation strategies based on specific requirements regarding data fidelity and classification objectives. Full article

Objective: The current study assessed outcomes of a continuing professional education program aimed at managing job-related stress to assist employees with recognizing and managing burnout and enhancing both productivity and overall well-being. Study Design: This study outlines the implementation of a needs assessment survey and the development of a non-credit training course for working professionals that addressed risks of burnout, suicidality, and self-care strategies to support mental health in the workplace. Methods: The sample for the current study consisted of 398 predominantly mid- to senior-level professionals. Participants were divided into two cohorts. The first cohort completed a structured needs assessment survey between June 2023 and July 2023 and provided ideas for curriculum development. The second cohort participated in synchronous, instructor-led virtual training sessions and completed pre- and post-training questionnaires between January 2024 and June 2024. A mixed-method content analysis was conducted to identify recurring themes and their frequency in course questionnaires. Results: Findings suggest that the training successfully expanded participants’ understanding of signs of burnout and of new approaches to improve well-being in the workplace including forming friendships, engaging in mindfulness activities, and taking time off for a mental health day. Conclusions: Future research should explore the long-term impacts of such interventions and compare delivery methods, including virtual and in-person formats, to determine the most effective approaches for promoting mental well-being at work. Full article

Background/Objectives: Sexual health is a crucial yet often overlooked aspect of nursing care, and nursing students often lack the communication skills needed to discuss it. Although several instruments are available to evaluate students’ attitudes and barriers, evidence on culturally adapted tools for Serbian nursing students remains limited. Therefore, the study aimed to assess the psychometric properties of the Serbian version of the SA-SH-Ext and to explore nursing students’ attitudes and barriers to sexual health communication. Methods: A sequential mixed-methods design was used. A total of 180 nursing students completed the SA-SH-Ext and SABS scales, followed by psychometric analysis including exploratory and confirmatory factor analyses and reliability testing. Semi-structured interviews with 20 students were thematically analysed to explore experiences and communication challenges. Results: Factor analysis yielded a four-factor model with the factors Being Comfortable, Communication with People with Disabilities, Future Patient and Working Relations, and Education and Competence, which explained 60.6% of the variance. The scale demonstrated strong internal consistency. Male and younger students reported higher comfort levels. Qualitative findings revealed discomfort, limited training, and fear of patient reactions, especially when discussing sexual health with older, disabled, or terminally ill patients. Conclusions: The Serbian SA-SH-Ext is a valid and reliable tool for assessing readiness to address sexual health. Despite positive attitudes, students face significant barriers. Integrating structured education into nursing curricula is essential to building competence and reducing stigma around sexual health in clinical practice. Full article

The adoption of Lean Construction has become established as an effective strategy for improving efficiency and reducing waste in construction projects; however, its implementation faces numerous challenges. The objective of this study is to analyze, through a systematic literature review, the critical factors that influence the implementation of Lean Construction, based on the analysis of 52 articles published between 2015 and 2025. The search was conducted in the Scopus database, and the selection of studies followed the PRISMA methodology. The results are organized into six key aspects that influence the execution of construction projects, grouped into three main categories: human, which includes managerial (42.3%) and educational (21.2%) factors; technical, which encompasses technological (23.1%) and safety (5.8%) factors; and management-related factors, comprised of financial (1.9%) and logistical (5.8%) factors. The evidence shows a greater relevance of human-related factors, followed by associated technical factors and, to a lesser extent, management-related factors. Taken together, these results highlight the need to address these factors through integrated strategies, training programs, and committed leadership to achieve a successful and sustainable implementation of Lean Construction. Full article

This study demonstrates the power of combining unmanned aerial vehicle (UAV) imagery and deep learning (DL) for monitoring forest regeneration, specifically focusing on young white pine (Pinus strobus). Using high-resolution three-band RGB and five-band multispectral orthomosaics derived from UAV flights, 20 DL object-detection models were evaluated within ArcGIS Pro 3.4 software (Esri Inc., Redlands, CA, USA). The models were tested across study sites in St. Lawrence County, NY, to assess performance on three distinct size classes of white pine, each stratified into low, medium, and high density areas. The Faster R-CNN (F-RCNN) model, particularly when trained with image rotation and no augmentation, significantly outperformed others, achieving an average precision of 0.88 across both imagery types. Subsequent confusion matrix analysis yielded 91% and 90% overall accuracy in medium and high-density white pine blocks, respectively. These findings validate the use of UAV-DL systems as an accurate and efficient tool for operational white pine regeneration assessment, reducing the need for labor-intensive fieldwork. Full article

Background/Objectives: Mentoring is defined as a supportive relationship between an experienced professional (mentor) and a less experienced individual (mentee), influencing skill development, professional confidence, and psychological well-being. This systematic review addresses the question: “Can support from a senior colleague positively impact junior healthcare workers?” Methods: Following PRISMA 2020 guidelines, a systematic literature search was performed (January 2004–December 2024) in Web of Science, PubMed, and Scopus databases, yielding 399 studies. Results: After rigorous screening and quality assessment using the QuADS checklist, 74 studies were included in the final analysis. The reviewed articles span various healthcare fields, including nursing, medicine, and midwifery, utilizing qualitative, quantitative, observational, and mixed-methods approaches. Key findings highlight the mentor’s role in academic and emotional support; fostering clinical and transversal skills such as communication, collaboration, and problem-solving; and enhancing self-efficacy, resilience, and autonomy, particularly during transitional or emotionally demanding periods. Challenges identified include the need for inclusive environments and standardized mentoring models. Conclusions: Overall, mentoring supports the professional and personal growth of junior healthcare professionals and contributes positively to training quality and clinical work. However, issues regarding equitable access, program standardization, and the need for further research to establish consolidated guidelines remain. Full article

Adequate ventilation is essential for maintaining indoor environmental quality in schools, where ventilation standards are often based on an indoor concentration of human-generated carbon dioxide (CO₂) above ambient levels. Low-cost non-dispersive infrared (NDIR) CO₂ sensors offer a practical solution for ventilation monitoring, yet variability between sensors can compromise accuracy, particularly when applications depend on the determination of precise concentration differences. This study evaluates the performance of twenty-three low-cost CO₂ sensors, developing normalisation functions to improve comparability across sensors, introducing an accessible methodology for on-site sensor calibration without the need for laboratory-grade reference equipment. The sensors were co-located for three independent test periods in 2025 representing typical school internal conditions in Ireland. Pre-normalisation analysis showed strong linearity (coefficient of determination (R²) = 0.999) but notable variability, with a mean root mean square error (RMSE) of 18.3 ppm and 0.45% of measurements outside manufacturers stated accuracy. Normalisation models were trained and validated using a leave-one-period-out approach. Regression-based correction yielded the greatest improvement, reducing RMSE by 16%. When applied to the full dataset, final correction factors reduced RMSE by 27%, out-of-range measurements by 43%, and proportional bias by 31%. Corrected sensors demonstrated highly consistent performance, particularly within the CO₂ ranges most relevant for classroom ventilation assessment, with an RMSE = 7.4 parts per million (ppm) at ambient concentrations and 11.9 ppm at concentrations below 1500 ppm. Field-based co-location in the deployment environment across full CO₂ cycles, combined with a network-derived global reference, produced effective correction factors. Performance declined marginally above 1500 ppm and during dynamic occupancy, while overall accuracy remained strong. The study presents a practical and accessible methodology for evaluating and normalising low-cost CO₂ sensors without specialised laboratory equipment, supporting reliable ventilation assessments in schools. Full article

Despite the progress made in recent decades, sexism is a prevalent problem today and has permeated society at a systemic level, including education. This study seeks to analyse the perception of sexism by senior secondary school teachers and trainee teachers. The research was carried out using qualitative methodology, using a semi-structured interview as a data collection instrument. The analysis consisted of the collection of teachers’ experiences for subsequent evaluation and comparison. The results obtained describe similarities between the groups in the perception of sexism in the classroom, but distinguish the justification behind sexist behaviour, and make explicit the lack of specific training for teachers in both groups in the area of sexism in science. These results point to the perceived need for more comprehensive training on sexism among secondary school teachers, according to the experiences and interpretations reported by the participants. Full article

Introduction: Respiratory rehabilitation programs for geriatric patients with chronic obstructive pulmonary disease (COPD) after COVID-19 require a precise assessment of needs and an individualized approach. However, there is a lack of specific recommendations for aerobic training in this patient group. Objective: The study aimed to compare two types of aerobic training—continuous and interval—and to determine which one is more effective and should be included in the respiratory rehabilitation program for geriatric patients with COPD after COVID-19. Methods: Of the 480 patients examined, 80 were included in the study. All patients underwent exercise tolerance tests (6-Minute Walk Test—6MWT) and functional performance tests (get-up-and-go test—TUG) before and after a 3-week intensive respiratory rehabilitation program. Results: Both types of training—interval and continuous—contributed to improved exercise tolerance and functional fitness in patients. However, analysis of the differences between the groups showed that continuous training with increasing exercise intensity resulted in significantly greater improvements in distance covered during the 6MWT, energy expenditure (METs), and TUG test time (p < 0.05). Conclusions: Continuous training on a cycle ergometer is more effective in the rehabilitation of geriatric patients with COPD after COVID-19 and should be included in therapeutic programs. Full article

With the growth of large language models (LLMs), there are increasing calls to interpret their behavior through the prism of analogies to human cognitive mechanisms. At the same time, scientific literature points to the fundamental limitations of these systems, describing them, among other things, as models that generate a superficial simulation of reasoning without real access to semantic meanings (“stochastic parrots” or “illusion of reasoning”). This paper proposes an innovative, modular benchmark for assessing the cognitive competence of LLMs, integrating three complementary dimensions of language processing: factual, syntactic, and logical. Eight language models (LLama 3.2, Mistral 7B, LLama 3:8B, Gemini 2.5 Flash, ChatGPT-3, ChatGPT-4o mini, ChatGPT-4, and ChatGPT-5) were tested using a uniform procedure with context reset after each interaction and a three-point scoring scheme (0/0.5/1). The results obtained showed a clear advantage for the largest models in tasks based on general knowledge and formal transformations known from training, with a significant decrease in effectiveness, regardless of model size, in tasks requiring conjunctive reasoning based solely on new, local premises. Importantly, unstable but measurable corrective abilities of some models were also observed after feedback, suggesting the presence of reactive mechanisms, but were insufficient to consider them systems capable of cognitive self-reflection. The combined analysis indicates that LLMs effectively simulate syntax and logic rules when the task corresponds to recognizable formal patterns, but fail in situations requiring the construction of new, coherent chains of beliefs and symbolic inferences, which undermines the thesis of their cognitive “understanding”. The results justify the need to create more complex and semantically restrictive evaluation frameworks that will allow distinguishing statistical fit from systemic, multi-stage formal reasoning. The proposed benchmark is a step towards a more multidimensional and diagnostic evaluation of LLMs, shifting the focus from “will the model respond correctly?” to “why and under what conditions is the model able to reason?” Full article

Background: Low-intake dehydration is frequent among institutionalised older adults and is associated with high morbidity–mortality and healthcare costs. Its prevention requires effective strategies and professional and institutional coordination. Objective: This study aims to explore the knowledge on the identification and prevention of dehydration, as well as the management of hydration by healthcare professionals and management in a nursing home. Methods: This exploratory qualitative study with a phenomenological approach convened two focus groups with 18 nurses and assistants alongside two semi-structured interviews with managers. The content analysis addressed five dimensions: knowledge; identification of dehydration; prevention of dehydration; barriers and facilitators; and actions proposed to improve hydration. Results: Participants recognised the importance of hydration but reported barriers including limited training, absence of specific protocols, and imprecise record systems. Facilitators included hydration reminders, improved accessibility to water, sensorial resources, promotion of independence, social activities, and institutional support for preventive strategies. Conclusions: These findings show that preventing and managing dehydration in nursing homes is complex and can be influenced by organisational and structural factors. The nursing team plays a central role in detecting dehydration early and implementing personalised strategies to promote fluid intake, while managerial support strengthens their effectiveness. Improving staff training, developing practical guidelines, and refining record systems may help address the identified barriers and enhance person-centred hydration management aligned with residents’ needs. Full article