关键词： Autonomy   COVID-19   Nursing leadership   Nurse management   Qualitative research   Work environment   BURNOUT  

摘要： BackgroundThe global nursing shortages exacerbated by the COVID-19 pandemic necessitated a drastic reorganization in nursing practices. Work routines, the composition of teams and subsequently mundane nursing practices were all altered to sustain the accessibility and quality of care. These dramatic changes demanded a reshaping of the nurses' work environment. The aim of this study was to explore how nurses reshaped their work environment in the early stages of the COVID-19 *** descriptive study comprising 26 semi-structured interviews conducted in a large Dutch teaching hospital between June and September 2020. Participants were nurses (including intensive care unit nurses), outpatient clinic assistants, nurse managers, and management (including one member of the Nurse Practice Council). The interviews were analysed with open, axial, and selective *** identified five themes: 1) the Nursing Staff Deployment Plan created new micro-teams with complementary roles to meet the care needs of COVID-19 infected patients; 2) nurse-led adaptations effectively managed the increased workload, thereby ensuring the quality of care; 3) continuous professional development ensured adequate competence levels for all roles; 4) interprofessional collaboration resulted in experienced solidarity, a positive atmosphere, and increased autonomy for nurses; and, 5) supportive managers reduced nurses' stress and improved work *** study showed that nurses positively reshaped their work environment during the COVID-19 pandemic. They contributed to innovative solutions in an environment of equal interprofessional collaboration, which led to greater respect for their knowledge and competencies, enhanced their autonomy and improved management support.

摘要： The outbreak of COVID-19 has created a global health crisis and an urgent need for accurate and efficient diagnostic tools. This study aims to investigate the potential of deep radiomic analysis in predicting COVID-19 using CT and X-ray images. The study utilizes a large dataset of CT and X-ray images from COVID-19 patients, as well as healthy controls. Deep radiomic analysis is performed on the images using various deep learning algorithms, including convolutional neural networks and autoencoders. The results demonstrate that deep radiomic analysis can accurately distinguish COVID-19 patients from healthy controls with high sensitivity and specificity. The study concludes that deep radiomic analysis has the potential to be a valuabletool in the diagnosis and management of COVID-19, particularly in settings where molecular testing may not be readily available. Our findings suggest that deep radiomic analysis may provide a useful tool for screening and diagnosing COVID-19, especially in areas with limited access to PCR testing. This approach has the potential to facilitate rapid and accurate diagnosis, allowing for earlier treatment and better outcomes for patients.

关键词： PREVALENCE  

摘要： Introduction The outbreak of a global pandemic like COVID-19 has highlighted significant distress around mental health. The burden of mental health issues like anxiety and depression requires evidence-based intervention, especially in low-income settings like Nepal. The study aims to determine the prevalence of anxiety and depression and the factors associated with it among hypertensive *** and methods The quantitative cross-sectional study design was used for this study. The study was conducted among 374 samples from selected wards of Kathmandu Metropolitan using a convenience sampling technique. Face-to-face interviews were conducted using a structured interview schedule. A Chi-square test was used to identify the statistical significance between dependent and independent variables. Binary logistic regression analysis was performed to determine the factors associated with anxiety and *** The prevalence of anxiety and depression among hypertensive patients during the COVID-19 pandemic was 27.8% and 24.3% respectively. According to the results of bivariate logistic regression analysis, smoking/tobacco consumption, staying in quarantine, positive COVID-19 test result, history of COVID-19 positive in the family, History of death due to COVID-19 in the family, visiting a hospital during the COVID-19 pandemic appeared as influencing factors of both anxiety and *** Our findings suggest that COVID-19 has a substantial effect on the mental health of hypertensive patients. This study highlights the need to develop early intervention and coping strategies among this population to minimize the negative impact of COVID-19 on their mental health and well-being.

摘要： Various countries have applied different policies concerning mask usage. Some countries obliged its citizens to use mask in public places while some others only suggested the usage voluntarily. This study discussed the voluntarily mask usage attitude in handling COVID-19 by using game theory perspective. The result of this study showed that game theory helped to describe mask usage attitude in handling COVID-19. The mask usage in game theory perspective is considering infected cost, the mask usage cost and reduction infected cost by mask users, either from virus carriers and receivers. The result shows that the higher mask usage cost towards infected cost, the possibility of mask usage is lesser and the higher infected proportions in certain population, the higher possibility of mask usage.

摘要： Covid19 and Pneumonia is a life-threatening disease that affects lungs in a person causing infection and is often caused by a bacteria known as Streptococcus pneumoniae. According to the World Health Organization (WHO), every one in ten people die in India is due to pneumonia. For COVID-19 detectionand deep learning model have been developed that can accurately detect pneumonia and COVID-19 caseswith the help of frontal chest X-rays. These models use similar architectures to pneumonia detection models, but are trained specifically on COVID-19 cases. One example of such a model is COVID-Net, which was developed specifically for the COVID-19 detection from using chest X-rays. Therefore, establishing an autonomous pneumonia detection system will provide benefit for the purpose of treating the disease with efficiency, especially for rural area. Deep Neural Network had received too much attentionfor the purpose of disease classification. The success of this approach is due to deep learning, in the field of dangerous disease prediction. Moreover, features learned by deep learning model pretrained on vast dataset is of great value in for image segregation tasks. In the study, our evaluation of performance with a pretrained deep learning model used a features extractor, follow with differently classified with classifying pneumonia, normal and covid chest radiographs. increase. there are several deep learning models that had been developed for the pneumonia detection, and most optimal model may depend on various factors. CheXNet, DenseNet, and COVID-Net are some examples of deep learning models that have shown promising results for pneumonia and COVID-19 detection from medical images

关键词： COVID-19 severity   COVID-19 patients' survival   SARS-CoV-2 innate immunity   SARS-CoV-2 adaptive immunity   long COVID   NK CELLS   T-CELLS   SARS-COV-2   IMMUNOGLOBULIN   MACROPHAGES   SELECTION   HEALTH  

摘要： Introduction SARS-CoV-2 pandemic still poses a significant burden on global health and economy, especially for symptoms persisting beyond the acute disease. COVID-19 manifests with various degrees of severity and the identification of early biomarkers capable of stratifying patient based on risk of progression could allow tailored *** We longitudinally analyzed 67 patients, classified according to a WHO ordinal scale as having Mild, Moderate, or Severe COVID-19. Peripheral blood samples were prospectively collected at hospital admission and during a 6-month follow-up after discharge. Several subsets and markers of the innate and adaptive immunity were monitored as putative factors associated with COVID-19 *** More than 50 immunological parameters were associated with disease severity. A decision tree including the main clinical, laboratory, and biological variables at admission identified low NK-cell precursors and CD14+CD91+ monocytes, and high CD8+ Effector Memory T cell frequencies as the most robust immunological correlates of COVID-19 severity and reduced survival. Moreover, low regulatory B-cell frequency at one month was associated with the susceptibility to develop long COVID at six months, likely due to their immunomodulatory *** These results highlight the profound perturbation of the immune response during COVID-19. The evaluation of specific innate and adaptive immune-cell subsets allows to distinguish between different acute and persistent COVID-19 symptoms.

关键词： COVID-19 vaccine uptake   Vaccination acceptance   Vaccination hesitancy   Vaccination experience   COM-B model   C50   I10   I15   M38   PLS-SEM   VALIDITY  

摘要： PurposeMany countries have a significant vaccination hesitancy rate regardless of vaccine prosperity. This study aims to identify factors restricting hesitancy and fostering vaccination intention and uptake against coronavirus in ***/methodology/approachThe study has proposed an extended COM-B model based on the Theoretical Domains Framework to explore critical factors influencing vaccination intention and uptake in Vietnam. A database was collected from 1,015 suitable respondents who had received at least one dose of the COVID-19 vaccine, and ten hypotheses were tested by the partial least squares structural equation *** findings showed that six factors, including knowledge, experience, resource, social influence, belief and reinforcement, have either direct or indirect positive effects on COVID-19 vaccine uptake behavior. The output also indicated that personal experience positively affects vaccination intention and ***/valueThis study contributes to understanding COVID-19 vaccine uptake behavior by identifying several direct and indirect factors of the extended COM-B model that include "knowledge" and "reinforcement" in shaping behavior change. The study adds to the literature on COVID-19 vaccine uptake behavior and could help achieve higher vaccination rates, ultimately leading to better control of the pandemic.

关键词： Informed consent   COVID-19   Clinical trials   Study participation   INFORMED-CONSENT FORMS   CLINICAL-TRIALS   DEVELOPING-COUNTRIES  

摘要： BackgroundThe informed consent process is an important step in conducting ethical clinical trials, as it ensures that research participants are aware of their rights and responsibilities in clinical trials. This study explored participants' perceptions, experiences and the factors motivating their participation in a COVID-19 vaccine trial in South *** descriptive qualitative study was conducted among twenty-five adult participants (18 to 64 years old) who participated in a COVID-19 vaccine trial in South Africa. Three focus group discussions and fifteen semi-structured interviews were carried out. Data were collected at a Clinical Research Site located in Prince Mshiyeni Memorial Hospital, in Umlazi Township, Durban, South Africa, where the COVID-19 vaccine trial participants were initially enrolled. Data were analysed iteratively using a thematic analysis *** key findings emerged: 1) Participants who experienced an event (such as tested positive for COVID-19) during the clinical trial were more likely to talk about the informed consent more thoroughly compared to the other participants. 2) Participants understood the purpose of informed consent process better when it was repeated multiple times throughout the course of the trial. 3) Where participants did not recall or understand various elements of the informed consent, participants were likely to create their own interpretations. 4) Factors influencing participations in trials were reimbursement for participation, access to health care, protection of family members, and ability to socialize without fear of ***, the findings show that the informed consent process should be regarded as an ongoing process rather than a once-off event that only happens at the start of a clinical trial. An understanding of participants' perspectives, experiences, and motivations for participating in clinical trials, can help trial staff strengthen the consent documents and process

关键词： Sickle cell disease   COVID-19   Emergency department   Acute care   Healthcare utilization   Trends   HOSPITALIZATIONS   SURVEILLANCE   VISITS   STATES  

摘要： BackgroundThe emergency department (ED) is a vital source of healthcare for individuals living with sickle cell disease (SCD). Prior research indicates that during the COVID-19 pandemic some individuals with SCD avoided the ED for fear of acquiring COVID-19 or delayed visiting the ED by self-management of symptoms or pain crisis at home. The purpose of the current study was to understand ED utilization rates before and during the pandemic among individuals living with *** conducted a retrospective cohort study using population-based SCD surveillance systems in California, Georgia, Michigan, and Tennessee to assess the impact of the pandemic on ED utilization among people with SCD by (1) analyzing trends in monthly ED utilization from January 2019 - December 2020, with specific attention given to immediate changes at the onset of the pandemic; and (2) calculating changes in the volume of utilization by comparing the total ED visits made from March - December 2020 to the same period in 2019, both overall and by demographic *** all states, a decline in ED utilization during the onset of the pandemic was seen, with the largest decline seen in those under age 10. By December 2020, utilization rates were higher than their lowest observed month of April 2020, but had not fully returned to pre-COVID levels. During the pandemic, ED visits in each state decreased by as much as 25%, and the number of people with any ED utilization decreased by as much as 26%.ConclusionsThis study confirms and extends the existing literature related to the impact of the pandemic on healthcare utilization patterns in the US, in a unique population with increased healthcare needs.

摘要： Clustering is the process of grouping similar data elements together. Clustering algorithms are used in various data mining applications. It is essential to analyse the similar data among large volumes of data. Medical data is one of the important datasets which acquires the help of data clustering algorithm for identifying patient category based on the severity of the disease. In this work, we have presented a system to perform an analysis of covid 19 data using clustering algorithm for healthcare people to know about severely affected covid-19 states. We have used the recent covid-19 dataset for India and evaluated the system with clustering algorithm for ensuring the appropriate recommendation on affected states. The proposed system recommends the highly affected area effectively and it has been experimented with till date datasets.