Relationships among work-family conflict, organizational silence, peer support, and turnover intention of second child nurses in China

This empirical study is based on a survey among Chinese nurses. Nurses in 216 secondary and tertiary level hospitals were selected using convenience sampling method, and 3,974 valid questionnaires were obtained. Four scales were adopted: Work-Family Conflict Scale, Organizational Silence Scale, Peer Support Scale and Turnover Intention Scale. IBM SPSS Statistics and AMOS were used. The results showed that the work-family conflict, organizational silence and peer support of Chinese nurses were all at medium to high levels. Nurses without children had lower levels of work-family conflict than nurses with a child or children; nurses with one child had lower levels of work-family conflict than nurses with two or more children; compared with nurses without children, nurses with a child or children had lower levels of turnover intention; work-family conflict positively influenced turnover intention; peer support negatively influenced organizational silence and turnover intention; organizational silence positively influenced turnover intention. Work-family conflict and peer support had both direct and indirect effects on turnover intention; organizational silence had a direct effect on turnover intention; and peer support and organizational silence mediated the relationship between work-family conflict and turnover intention. This study explored the formulation of motivational management strategies. The factors influencing nurses' turnover intention and the relationships among them were discussed. The results showed that the number of children influences nurses' work-family conflict, organizational silence, peer support, and turnover intention. This study provides insights for nursing managers to improve nursing management models and health policies.Este estudo é baseado num questionário junto de enfermeiras chinesas, com recurso a amostragem por conveniência. Foram selecionadas enfermeiras de 216 hospitais chineses e validados 3974 questionários. Utilizaram-se quatro escalas para medir as dimensões "conflito trabalho-família", "silêncio organizacional", "apoio dos pares" e "intenção de saída". A análise estatística foi feita com IBM SPSS Statistics e AMOS. Os resultados mostram níveis médios a altos nas dimensões conflito trabalho-família, silêncio organizacional e apoio dos pares. As enfermeiras sem filhos apresentaram níveis mais baixos de conflito trabalho-família do que aquelas que têm filhos. Por outro lado, as enfermeiras com um único filho demonstraram níveis mais baixos de conflito trabalho-família do que as que têm dois ou mais filhos. No que se refere à variável intenção de saída as enfermeiras com uma ou mais crianças mostraram níveis mais baixos de intenção de saída, enquanto que o conflito trabalho-família influenciou positivamente essa intenção. O apoio dos pares influenciou negativamente o silêncio organizacional e a intenção de saída; o silêncio organizacional influenciou positivamente a intenção de saída. O conflito entre trabalho e família e o apoio de pares tiveram efeitos diretos e indiretos na intenção de saída; o silêncio organizacional teve um efeito direto na intenção de saída; o apoio dos pares e o silêncio organizacional mediaram a relação entre o conflito trabalho-família e a intenção de saída. Este estudo explorou a formulação de estratégias de gestão motivacional. A autora discutiu os fatores que influenciam a intenção de saída das enfermeiras, e sua inter-relação. Os resultados mostram que o número de crianças influencia o conflito trabalho-família, o silêncio organizacional, o apoio dos pares e a intenção de saída. Este estudo fornece insights para os gestores de enfermagem com vista à melhoria dos modelos de gestão de enfermagem e das políticas de saúde

Enseñanza remota de emergencia del inglés como lengua extranjera durante el COVID-19: perspectivas desde una universidad en China

Given the circumstances of the global pandemic, universities around China and across the globe have suspended face to face (F2F) classes and transitioned to emergency remote teaching (ERT). University students in China have been the first to go through the whole semester's ERT including College English, a compulsory language course for almost all the first- and second-year students of non-English majors. This article adopted a mixed-methods design, a survey followed by a qualitative visual method, gathered data on students' experience about ERT of College English and presented an investigation into detailed interactive process of the classes. The data analysis on the learners' engagement and the feedback from the learners provided a summary of the key threads of ERT classes. This study demonstrated that students held an extrinsic goal orientation, which did not differ from their face-to-face learning experience. ERT granted students more opportunities for interaction with their instructor and peers, while collaboration among students were limited. The research results can be connected to the larger fabric of global language teaching in crisis context, provide empirical lessons to educators, and help instructors with their future decision-making about technology-supported activities.Dadas las circunstancias de la pandemia mundial, las universidades de China y de todo el mundo han suspendido las clases presenciales (F2F) y han pasado a la enseñanza remota de emergencia (ERT). Los estudiantes universitarios en China han sido los primeros en pasar por el ERT de todo el semestre, incluido el inglés universitario, un curso de idioma obligatorio para casi todos los estudiantes de primer y segundo año de especializaciones no inglesas. Este artículo adoptó un diseño de métodos mixtos, una encuesta seguida de un método visual cualitativo, recopiló datos sobre la experiencia de los estudiantes sobre ERT de inglés universitario y presentó una investigación sobre el proceso interactivo detallado de las clases. El análisis de datos sobre el compromiso de los alumnos y los comentarios de los alumnos proporcionaron un resumen de los hilos clave de las clases de ERT. Este estudio demostró que los estudiantes tenían una orientación de metas extrínseca, que no difería de su experiencia de aprendizaje cara a cara. ERT otorgó a los estudiantes más oportunidades de interacción con su instructor y compañeros, mientras que la colaboración entre los estudiantes fue limitada. Los resultados de la investigación se pueden conectar con el tejido más amplio de la enseñanza de idiomas global en un contexto de crisis, proporcionar lecciones empíricas a los educadores y ayudar a los instructores con su futura toma de decisiones sobre actividades apoyadas por la tecnología.Universidad Pablo de Olavid

Plasma n-3 and n-6 fatty acids and inflammatory markers in Chinese vegetarians

Background: Polyunsaturated fatty acid (PUFA) intake favorably affects chronic inflammatory-related diseases such as cardiovascular disease; however, the relationship between the PUFA and inflammatory factors in the healthy vegetarians were not clear. We aimed to investigate the plasma fatty acids status, and its association with plasma inflammatory factors in Chinese vegetarians and omnivores. Methods: A total of 89 male vegetarians and 106 male omnivores were participated the study. Plasma concentrations of inflammatory factors were detected by ELISA, and as standard methods fatty acids were extracted and determined by chromatography. Results: Compared with omnivores, vegetarians have significant higher interleukin-6 (IL-6), plasma n-6 PUFA, n-6/n-3, and 18:3n-3; while they have significant lower leukotriene B4 (LTB4), cyclo-oxygenase-2 (COX2) and prostaglandin E2 (PGE2), 20:5n-3, 22:5n-3, 22:6n-3, and n-3 PUFA. In vegetarians, plasma 20:4n-6 was significant positively related to TNF-α. LTB4 was significantly positively related to plasma 22:6n-3, and negatively associated with n-6 PUFA. Conclusion: Vegetarians have higher plasma n-6 PUFA and IL-6, but lower LTB4, n-3 PUFA, 22:6n-3, COX2 and PGE2 levels. It would seem appropriate for vegetarians to increase their dietary n-3 PUFA, while reduce dietary n-6 PUFA and thus reduce the risk of chronic inflammatory-related diseases

Generalize Ultrasound Image Segmentation via Instant and Plug & Play Style Transfer

Deep segmentation models that generalize to images with unknown appearance are important for real-world medical image analysis. Retraining models leads to high latency and complex pipelines, which are impractical in clinical settings. The situation becomes more severe for ultrasound image analysis because of their large appearance shifts. In this paper, we propose a novel method for robust segmentation under unknown appearance shifts. Our contribution is three-fold. First, we advance a one-stage plug-and-play solution by embedding hierarchical style transfer units into a segmentation architecture. Our solution can remove appearance shifts and perform segmentation simultaneously. Second, we adopt Dynamic Instance Normalization to conduct precise and dynamic style transfer in a learnable manner, rather than previously fixed style normalization. Third, our solution is fast and lightweight for routine clinical adoption. Given 400*400 image input, our solution only needs an additional 0.2ms and 1.92M FLOPs to handle appearance shifts compared to the baseline pipeline. Extensive experiments are conducted on a large dataset from three vendors demonstrate our proposed method enhances the robustness of deep segmentation models

PlanarTrack: A Large-scale Challenging Benchmark for Planar Object Tracking

Planar object tracking is a critical computer vision problem and has drawn increasing interest owing to its key roles in robotics, augmented reality, etc. Despite rapid progress, its further development, especially in the deep learning era, is largely hindered due to the lack of large-scale challenging benchmarks. Addressing this, we introduce PlanarTrack, a large-scale challenging planar tracking benchmark. Specifically, PlanarTrack consists of 1,000 videos with more than 490K images. All these videos are collected in complex unconstrained scenarios from the wild, which makes PlanarTrack, compared with existing benchmarks, more challenging but realistic for real-world applications. To ensure the high-quality annotation, each frame in PlanarTrack is manually labeled using four corners with multiple-round careful inspection and refinement. To our best knowledge, PlanarTrack, to date, is the largest and most challenging dataset dedicated to planar object tracking. In order to analyze the proposed PlanarTrack, we evaluate 10 planar trackers and conduct comprehensive comparisons and in-depth analysis. Our results, not surprisingly, demonstrate that current top-performing planar trackers degenerate significantly on the challenging PlanarTrack and more efforts are needed to improve planar tracking in the future. In addition, we further derive a variant named PlanarTrack$_{\mathbf{BB}}$ for generic object tracking from PlanarTrack. Our evaluation of 10 excellent generic trackers on PlanarTrack$_{\mathrm{BB}}$ manifests that, surprisingly, PlanarTrack$_{\mathrm{BB}}$ is even more challenging than several popular generic tracking benchmarks and more attention should be paid to handle such planar objects, though they are rigid. All benchmarks and evaluations will be released at the project webpage.Comment: Tech. Repor

Searching Collaborative Agents for Multi-plane Localization in 3D Ultrasound

3D ultrasound (US) is widely used due to its rich diagnostic information, portability and low cost. Automated standard plane (SP) localization in US volume not only improves efficiency and reduces user-dependence, but also boosts 3D US interpretation. In this study, we propose a novel Multi-Agent Reinforcement Learning (MARL) framework to localize multiple uterine SPs in 3D US simultaneously. Our contribution is two-fold. First, we equip the MARL with a one-shot neural architecture search (NAS) module to obtain the optimal agent for each plane. Specifically, Gradient-based search using Differentiable Architecture Sampler (GDAS) is employed to accelerate and stabilize the training process. Second, we propose a novel collaborative strategy to strengthen agents' communication. Our strategy uses recurrent neural network (RNN) to learn the spatial relationship among SPs effectively. Extensively validated on a large dataset, our approach achieves the accuracy of 7.05 degree/2.21mm, 8.62 degree/2.36mm and 5.93 degree/0.89mm for the mid-sagittal, transverse and coronal plane localization, respectively. The proposed MARL framework can significantly increase the plane localization accuracy and reduce the computational cost and model size.Comment: Early accepted by MICCAI 202

Effects of artificial light with different spectral compositions on refractive development and matrix metalloproteinase 2 and tissue inhibitor of metalloproteinases 2 expression in the sclerae of juvenile guinea pigs

Artificial light can affect eyeball development and increase myopia rate. Matrix metalloproteinase 2 (MMP-2) degrades the extracellular matrix, and induces its remodeling, while tissue inhibitor of matrix MMP-2 (TIMP-2) inhibits active MMP-2. The present study aimed to look into how refractive development and the expression of MMP-2 and TIMP-2 in the guinea pigs' remodeled sclerae are affected by artificial light with varying spectral compositions. Three weeks old guinea pigs were randomly assigned to groups exposed to five different types of light: natural light, LED light with a low color temperature, three full spectrum artificial lights, i.e. E light (continuous spectrum in the range of ~390-780 nm), G light (a blue peak at 450 nm and a small valley 480 nm) and F light (continuous spectrum and wavelength of 400 nm below filtered). A-scan ultrasonography was used to measure the axial lengths of their eyes, every two weeks throughout the experiment. Following twelve weeks of exposure to light, the sclerae were observed by optical and transmission electron microscopy. Immunohistochemistry, Western blot and RT-qPCR were used to detect the MMP-2 and TIMP-2 protein and mRNA expression levels in the sclerae. After four, six, eight, ten, and twelve weeks of illumination, the guinea pigs in the LED and G light groups had axial lengths that were considerably longer than the animals in the natural light group while the guinea pigs in the E and F light groups had considerably shorter axial lengths than those in the LED group. Following twelve weeks of exposure to light, the expression of the scleral MMP-2 protein and mRNA were, from low to high, N group, E group, F group, G group, LED group; however, the expression of the scleral TIMP-2 protein and mRNA were, from high to low, N group, E group, F group, G group, LED group. The comparison between groups was statistically significant (p<0.01). Continuous, peaks-free or valleys-free artificial light with full-spectrum preserves remodeling of scleral extracellular matrix in guinea pigs by downregulating MMP-2 and upregulating TIMP-2, controlling eye axis elongation, and inhibiting the onset and progression of myopia

Prevalence of depressive symptoms and correlates among individuals who self-reported SARS-CoV-2 infection after optimizing the COVID-19 response in China

BACKGROUND: The burden of depression symptoms has increased among individuals infected with SARS-CoV-2 during COVID-19 pandemic. However, the prevalence and associated factors of depressive symptoms among individuals infected with SARS-CoV-2 remain uncertain after optimizing the COVID-19 response in China. METHODS: An online cross-sectional survey was conducted among the public from January 6 to 30, 2023, using a convenience sampling method. Sociodemographic and COVID-19 pandemic-related factors were collected. The depression symptoms were assessed using the Patient Health Questionnaire-9 (PHQ-9). Logistic regression analysis was performed to explore the associated factors with depressive symptoms. RESULTS: A total of 2,726 participants completed the survey. The prevalence of depression symptoms was 35.3%. About 58% of the participants reported experiencing insufficient drug supply. More than 40% of participants reported that they had missed healthcare appointments or delayed treatment. One-third of participants responded experiencing a shortage of healthcare staff and a long waiting time during medical treatment. Logistic regression analysis revealed several factors that were associated with depression symptoms, including sleep difficulties (OR, 2.84; 95% CI, 2.34-3.44), chronic diseases (OR, 2.15; 95% CI, 1.64-2.82), inpatient treatment for COVID-19 (OR, 3.24; 95% CI, 2.19-4.77), with COVID-19 symptoms more than 13 days (OR, 1.30, 95% CI 1.04-1.63), re-infection with SARS-CoV-2 (OR, 1.52; 95% CI, 1.07-2.15), and the increased in demand for healthcare services (OR, 1.32; 95% CI, 1.08-1.61). CONCLUSION: This study reveals a moderate prevalence of depression symptoms among individuals infected with SARS-CoV-2. The findings underscore the importance of continued focus on depressive symptoms among vulnerable individuals, including those with sleeping difficulties, chronic diseases, and inpatient treatment for COVID-19. It is necessary to provide mental health services and psychological interventions for these vulnerable groups during the COVID-19 epidemic

Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge

The emergence of deep learning has considerably advanced the state-of-the-art in cardiac magnetic resonance (CMR) segmentation. Many techniques have been proposed over the last few years, bringing the accuracy of automated segmentation close to human performance. However, these models have been all too often trained and validated using cardiac imaging samples from single clinical centres or homogeneous imaging protocols. This has prevented the development and validation of models that are generalizable across different clinical centres, imaging conditions or scanner vendors. To promote further research and scientific benchmarking in the field of generalizable deep learning for cardiac segmentation, this paper presents the results of the Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation (M&Ms) Challenge, which was recently organized as part of the MICCAI 2020 Conference. A total of 14 teams submitted different solutions to the problem, combining various baseline models, data augmentation strategies, and domain adaptation techniques. The obtained results indicate the importance of intensity-driven data augmentation, as well as the need for further research to improve generalizability towards unseen scanner vendors or new imaging protocols. Furthermore, we present a new resource of 375 heterogeneous CMR datasets acquired by using four different scanner vendors in six hospitals and three different countries (Spain, Canada and Germany), which we provide as open-access for the community to enable future research in the field