Role of Empathy and Lifelong Learning Abilities in Physicians and Nurses Who Work in Direct Contact with Patients in Adverse Working Conditions
Artículo Materias > Biomedicina Universidad Europea del Atlántico > Investigación > Artículos y libros Abierto Inglés Empathy and lifelong learning are two professional competencies that depend on the four principles of professionalism: humanism, altruism, excellence, and accountability. In occupational health, there is evidence that empathy prevents work distress. However, in the case of lifelong learning, the evidence is still scarce. In addition, recent studies suggest that the development of lifelong learning varies in physicians and nurses and that it is sensitive to the influence of cultural stereotypes associated with professional roles. This study was performed with the purpose of determining the specific role that empathy and lifelong learning play in the reduction in occupational stress. This study included a sample composed by 40 physicians and 40 nurses with high dedication to clinical work in ambulatory consultations from a public healthcare institution in Paraguay. Somatization, exhaustion, and work alienation, described as indicators of occupational stress, were used as dependent variables, whereas empathy, lifelong learning, gender, discipline, professional experience, civil status, and family burden were used as potential predictors. Three multiple regression models explained 32% of the variability of somatization based on a linear relationship with empathy, lifelong learning, and civil status; 73% of the variability of exhaustion based on a linear relationship with empathy, somatization, work alienation, and discipline; and 62% of the variability of work alienation based on a linear relationship with lifelong learning, exhaustion, and discipline. These findings indicate that empathy and lifelong learning play important roles in the prevention of work distress in physicians and nurses. However, this role varies by discipline. metadata Delgado Bolton, Roberto C.; San-Martín, Montserrat y Vivanco, Luis mail SIN ESPECIFICAR, SIN ESPECIFICAR, luis.vivanco@uneatlantico.es (2022) Role of Empathy and Lifelong Learning Abilities in Physicians and Nurses Who Work in Direct Contact with Patients in Adverse Working Conditions. International Journal of Environmental Research and Public Health, 19 (5). p. 3012. ISSN 1660-4601
|
Texto
ijerph-19-03012.pdf Available under License Creative Commons Attribution. Descargar (5MB) | Vista Previa |
Resumen
Empathy and lifelong learning are two professional competencies that depend on the four principles of professionalism: humanism, altruism, excellence, and accountability. In occupational health, there is evidence that empathy prevents work distress. However, in the case of lifelong learning, the evidence is still scarce. In addition, recent studies suggest that the development of lifelong learning varies in physicians and nurses and that it is sensitive to the influence of cultural stereotypes associated with professional roles. This study was performed with the purpose of determining the specific role that empathy and lifelong learning play in the reduction in occupational stress. This study included a sample composed by 40 physicians and 40 nurses with high dedication to clinical work in ambulatory consultations from a public healthcare institution in Paraguay. Somatization, exhaustion, and work alienation, described as indicators of occupational stress, were used as dependent variables, whereas empathy, lifelong learning, gender, discipline, professional experience, civil status, and family burden were used as potential predictors. Three multiple regression models explained 32% of the variability of somatization based on a linear relationship with empathy, lifelong learning, and civil status; 73% of the variability of exhaustion based on a linear relationship with empathy, somatization, work alienation, and discipline; and 62% of the variability of work alienation based on a linear relationship with lifelong learning, exhaustion, and discipline. These findings indicate that empathy and lifelong learning play important roles in the prevention of work distress in physicians and nurses. However, this role varies by discipline.
| Tipo de Documento: | Artículo |
|---|---|
| Palabras Clave: | somatization; exhaustion; work alienation; empathy; lifelong learning; nursing; medicine |
| Clasificación temática: | Materias > Biomedicina |
| Divisiones: | Universidad Europea del Atlántico > Investigación > Artículos y libros |
| Depositado: | 03 Oct 2022 12:41 |
| Ultima Modificación: | 17 Jul 2023 23:30 |
| URI: | https://repositorio.uneatlantico.es/id/eprint/3741 |
Acciones (logins necesarios)
 |
Ver Objeto |
en
close
Enzymatic treatment shapes in vitro digestion pattern of phenolic compounds in mulberry juice
The health benefits of mulberry fruit are closely associated with its phenolic compounds. However, the effects of enzymatic treatments on the digestion patterns of these compounds in mulberry juice remain largely unknown. This study investigated the impact of pectinase (PE), pectin lyase (PL), and cellulase (CE) on the release of phenolic compounds in whole mulberry juice. The digestion patterns were further evaluated using an in vitro simulated digestion model. The results revealed that PE significantly increased chlorogenic acid content by 77.8 %, PL enhanced cyanidin-3-O-glucoside by 20.5 %, and CE boosted quercetin by 44.5 %. Following in vitro digestion, the phenolic compound levels decreased differently depending on the treatment, while cyanidin-3-O-rutinoside content increased across all groups. In conclusion, the selected enzymes effectively promoted the release of phenolic compounds in mulberry juice. However, during gastrointestinal digestion, the degradation of phenolic compounds surpassed their enhanced release, with effects varying based on the compound's structure.
Peihuan Luo mail , Jian Ai mail , Qiongyao Wang mail , Yihang Lou mail , Zhiwei Liao mail , Francesca Giampieri mail francesca.giampieri@uneatlantico.es, Maurizio Battino mail maurizio.battino@uneatlantico.es, Elwira Sieniawska mail , Weibin Bai mail , Lingmin Tian mail ,
Luo
<a class="ep_document_link" href="/17819/1/1-s2.0-S2214804325000679-main%20%281%29.pdf"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>
en
open
What works in financial education? Experimental evidence on program impact
Financial education is increasingly essential for safeguarding both individual and corporate well-being. This study systematically reviews global financial education experiments using a dual-method framework that integrates a deep learning classifier with advanced multivariate statistical techniques. Our analysis indicates that while short-term improvements in financial literacy are common, such gains tend to diminish over time without ongoing reinforcement. Moreover, the limited impact of digital innovations and monetary incentives suggests that successful financial education depends on more than simply deploying technological solutions or extrinsic rewards. Overall, this review provides valuable insights into the evolving landscape of financial education in a dynamic economic context and underscores the need for sustainable strategies that secure lasting improvements in financial literacy.
Gonzalo Llamosas García mail , Cristina Mazas Pérez-Oleaga mail cristina.mazas@uneatlantico.es,
García
en
close
Epigallocatechin gallate (EGCG) is the most abundant polyphenol in tea. Owing to the different fermentation degrees, differences in polyphenol composition of water extracts of green tea, white tea, oolong tea, and black tea occur, and affect health value. This study revealed that the content of EGCG decreases with the increase in the degree of fermentation. In tea with a high fermentation degree, EGCG was stably present in the form of ammoniation to yield nitrogen-containing EGCG derivative (N-EGCG). The content of N-EGCG in tea was negatively correlated with the content of EGCG. Furthermore, the content of l-serine and L-threonine in tea was positively and negatively correlated with N-EGCG and EGCG levels, respectively, suggesting that they may participate in the formation of N-EGCG as nitrogen sources. This study proposes a new fermentation-induced polyphenol-amino acid synergistic mechanism, which provides a theoretical basis for the study of the biotransformation reaction mechanism of tea polyphenols.
Yuxuan Zhao mail , Jingyimei Liang mail , Wanning Ma mail , Mohamed A. Farag mail , Chunlin Li mail , Jianbo Xiao mail ,
Zhao
<a class="ep_document_link" href="/17858/1/s41598-025-18979-8.pdf"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>
en
open
Detection and classification of brain tumor using a hybrid learning model in CT scan images
Accurate diagnosis of brain tumors is critical in understanding the prognosis in terms of the type, growth rate, location, removal strategy, and overall well-being of the patients. Among different modalities used for the detection and classification of brain tumors, a computed tomography (CT) scan is often performed as an early-stage procedure for minor symptoms like headaches. Automated procedures based on artificial intelligence (AI) and machine learning (ML) methods are used to detect and classify brain tumors in Computed Tomography (CT) scan images. However, the key challenges in achieving the desired outcome are associated with the model’s complexity and generalization. To address these issues, we propose a hybrid model that extracts features from CT images using classical machine learning. Additionally, although MRI is a common modality for brain tumor diagnosis, its high cost and longer acquisition time make CT scans a more practical choice for early-stage screening and widespread clinical use. The proposed framework has different stages, including image acquisition, pre-processing, feature extraction, feature selection, and classification. The hybrid architecture combines features from ResNet50, AlexNet, LBP, HOG, and median intensity, classified using a multilayer perceptron. The selection of the relevant features in our proposed hybrid model was extracted using the SelectKBest algorithm. Thus, it optimizes the proposed model performance. In addition, the proposed model incorporates data augmentation to handle the imbalanced datasets. We employed a scoring function to extract the features. The Classification is ensured using a multilayer perceptron neural network (MLP). Unlike most existing hybrid approaches, which primarily target MRI-based brain tumor classification, our method is specifically designed for CT scan images, addressing their unique noise patterns and lower soft-tissue contrast. To the best of our knowledge, this is the first work to integrate LBP, HOG, median intensity, and deep features from both ResNet50 and AlexNet in a structured fusion pipeline for CT brain tumor classification. The proposed hybrid model is tested on data from numerous sources and achieved an accuracy of 94.82%, precision of 94.52%, specificity of 98.35%, and sensitivity of 94.76% compared to state-of-the-art models. While MRI-based models often report higher accuracies, the proposed model achieves 94.82% on CT scans, within 3–4% of leading MRI-based approaches, demonstrating strong generalization despite the modality difference. The proposed hybrid model, combining hand-crafted and deep learning features, effectively improves brain tumor detection and classification accuracy in CT scans. It has the potential for clinical application, aiding in early and accurate diagnosis. Unlike MRI, which is often time-intensive and costly, CT scans are more accessible and faster to acquire, making them suitable for early-stage screening and emergency diagnostics. This reinforces the practical and clinical value of the proposed model in real-world healthcare settings.
Roja Ghasemi mail , Naveed Islam mail , Samin Bayat mail , Muhammad Shabir mail , Shahid Rahman mail , Farhan Amin mail , Isabel de la Torre mail , Ángel Gabriel Kuc Castilla mail angel.kuc@uneatlantico.es, Debora L. Ramírez-Vargas mail debora.ramirez@unini.edu.mx,
Ghasemi
en
close
Evidence suggests that first- and second-generation mindfulness-based interventions (MBIs) can improve body image concerns in adolescents and adults. However, a systematic review of such interventions is lacking. The aim of this study is to synthesize evidence from randomized controlled trials evaluating the efficacy of both first- and second-generation MBIs in reducing negative body image and enhancing positive body image. Database searches were conducted in PubMed, CoChrane, Proquest Thesis & Dissertations and ScienceDirect up to August 2025, identifying 3394 records. After screening, 43 studies met eligibility criteria (n = 7979) and were evaluated for methodological quality following PRISMA guidelines. Of them, 16 (37.2 %) evaluated first-generation MBIs, while the remaining 27 studies (55.8 %) examined second-generation MBIs, with self-compassion being the most commonly used intervention. Only one study used both generations. Both first- and second-generation interventions demonstrated moderate to large effect sizes in most studies, with 94 % reporting significant improvements in at least one body image outcome. The methodological quality, assessed using the JBI tool, was rated as having either low risk of bias or some concerns in nearly 70 % of the studies. These findings highlight the global efficacy of MBIs for reducing negative body image and improving positive body image, while also underscoring the need for future research to employ more methodologically rigorous designs, multidimensional outcome measures, and greater inclusion of diverse sex, gender, and ethnic groups.
Alba Gutiérrez Cabrero mail , Marian González-García mail marian.gonzalez@uneatlantico.es,
Gutiérrez Cabrero