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Bird Bell posted an update 6 months, 3 weeks ago
The study aimed to investigate measurement invariance of the 10-item Connor-Davidson Resilience Scale (CD-RISC-10) across gender, age, and education. Adults from a general population of Slovenia (N = 431; 58% female; age 18 to 59 years) filled in the CD-RISC-10, the short form of the Mental Health Continuum and the Depression, Anxiety and Stress Scale. Measurement invariance of the proposed one-factor model of CD-RISC-10 by gender, age, and level of education was examined using multiple-group confirmatory factor analysis. The results showed configural, metric, and scalar invariance of the CD-RISC-10 across gender, age, and educational groups. The measure showed satisfactory reliability, positive associations with emotional, psychological, and social well-being, and negative links with negative emotional states. Group differences in latent means suggested higher resilience in men than women, early adults as compared to emerging adults, and people with higher as compared to those with lower level of education. The Slovenian version of the CD-RISC-10 is an acceptably reliable and valid measure of resilience, suitable to detect possible differences between gender, age, and educational groups. Resilience shows favorable associations with enhanced positive mental health and diminished symptoms of mental problems.In this paper we apply a series of Machine Learning models to a recently published unique dataset on the mortality of COVID19 patients. We use a dataset consisting of blood samples of 375 patients admitted to a hospital in the region of Wuhan, China. There are 201 patients who survived hospitalisation and 174 patients who died whilst in hospital. The focus of the paper is not only on seeing which Machine Learning model is able to obtain the absolute highest accuracy but more on the interpretation of what the Machine Learning models provides. We find that age, days in hospital, Lymphocyte and Neutrophils are important and robust predictors when predicting a patients mortality. Furthermore, the algorithms we use allows us to observe the marginal impact of each variable on a case-by-case patient level, which might help practicioneers to easily detect anomalous patterns. This paper analyses the global and local interpretation of the Machine Learning models on patients with COVID19.Anomaly detection is challenging, especially for large datasets in high dimensions. Here, we explore a general anomaly detection framework based on dimensionality reduction and unsupervised clustering. DRAMA is released as a general python package that implements the general framework with a wide range of built-in options. This approach identifies the primary prototypes in the data with anomalies detected by their large distances from the prototypes, either in the latent space or in the original, high-dimensional space. DRAMA is tested on a wide variety of simulated and real datasets, in up to 3000 dimensions, and is found to be robust and highly competitive with commonly used anomaly detection algorithms, especially in high dimensions. The flexibility of the DRAMA framework allows for significant optimization once some examples of anomalies are available, making it ideal for online anomaly detection, active learning, and highly unbalanced datasets. Besides, DRAMA naturally provides clustering of outliers for subsequent analysis.Lumbar spinal stenosis (LSS) is a lumbar disease with a high incidence in recent years. selleck inhibitor Accurate segmentation of the vertebral body, lamina and dural sac is a key step in the diagnosis of LSS. This study presents an lumbar spine magnetic resonance imaging image segmentation method based on deep learning. In addition, we define the quantitative evaluation methods of two clinical indicators (that is the anteroposterior diameter of the spinal canal and the cross-sectional area of the dural sac) to assist LSS diagnosis. To improve the segmentation performance, a dual-branch multi-scale attention module is embedded into the network. It contains multi-scale feature extraction based on three 3 × 3 convolution operators and vital information selection based on attention mechanism. In the experiment, we used lumbar datasets from the spine surgery department of Shengjing Hospital of China Medical University to evaluate the effect of the method embedded the dual-branch multi-scale attention module. Compared with other state-of-the-art methods, the average dice similarity coefficient was improved from 0.9008 to 0.9252 and the average surface distance was decreased from 6.40 to 2.71 mm.Organizational change is a complex and critical issue in higher education today. Changes experienced across institutional systems have both short-term and long-term impact, making this phenomenon ripe for educational leadership research. Many universities and colleges have applied Kotter’s (1995) eight-step change model prescriptively to implement academic initiatives, curriculum revisions, and strategic vision. However, Kotter’s (1995) model has not previously explored ad hoc changes over time and has not been used to study a college live mascot program. Although a decreasing campus tradition, college live mascots have a relationship and impact on a student’s experience and college identification, as well as university engagement with alumni and the public. Therefore, understanding how change is identified and experienced by campus stakeholders of a college live mascot program describe the various complexities and issues that initiate a climate for institutional change. To explain the evolution of a college live mascot program, oral histories across twenty years of campus stakeholders, including student trainers, campus administrators, and external consultants were analyzed using Kotter’s (1995) model. The findings of this study affirmed that the steps associated with Kotter’s (1995) change model, with a specific focus on the first three steps, are relevant for ad hoc changes, and offer implications for higher education change.Hospitals consume most of the health systems’ financial resources. In Portugal, for instance, public hospitals represent more than half of the National Health Service debt and are decisive in their financial insufficiency. Although profit is not the primary goal of hospitals, it is essential to guarantee their financial sustainability to ensure users’ health care and the necessary resources. An analysis of the existing literature shows that researches focus mainly on the hospital’s technical efficiency. The literature has paid little or even no attention to the use of composite indicators in hospital benchmarking studies. This study uses the Benefit of Doubt methodology alongside recent data about Portuguese public hospitals (2013-2017) to understand the factors that contribute to low performance and high indebtedness levels. Our results suggest that hospitals perform better in terms of access (average score 0.982). The group of criteria with the lowest performance was efficiency and productivity (average score 0.
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