关键词： COVID-19 pandemic   Mental health   Social determinants of health   Resilience   Social support   Discrimination   SOCIAL SUPPORT   COVID-19   CRISIS   TIMES   CONSEQUENCES   RESILIENCE   CHALLENGES   DISORDER  

摘要： BackgroundThe COVID-19 pandemic and related disruptive consequences in the economic, health, and educational sectors have impacted people's lives, contributing to a context of increased economic and social vulnerability. The pandemic has revealed and accentuated social inequalities and discrimination based on racial or ethnic origin. This study aimed to contribute to the promotion of the mental health and well-being of migrant populations living in Portugal via the definition of an analytical framework and recommendations emerging from the EQUALS4COVID19 *** gather information on the impact of the COVID-19 pandemic and resilience determinants among immigrants, a mixed-methods approach was implemented in 2022, combining a cross-sectional survey targeting immigrant adults in Portugal, focus groups with immigrants, focus groups with healthcare professionals, and in-depth individual interviews with stakeholders involved in the implementation of measures related to mental health and well-being during the pandemic. The analysis followed an integrated framework; quantitative data informed the script of qualitative data collection methods, and qualitative analysis informed the reinterpretation of quantitative *** survey with 604 Brazilian and Cape Verdean immigrants revealed that gender (being a woman) was associated with both psychological distress and depression-related symptomatology and that the perception of discrimination was a major risk factor for psychological suffering, while perceived social support and individuals' resilience characteristics were protective factors. Qualitative data provided deeper insights into these findings, revealing the ways mental health is affected by social structures, such as gender and ethnic hierarchies. Migrants tend to work in precarious jobs requiring physical presence, which, together with dense housing conditions, puts them at higher risk of infection. The deterioration of the economic conditions of the

关键词： COVID-19   TEC   MIS-C  

摘要： BackgroundTransient erythroblastopenia of childhood (TEC) is an acquired, self-limited pure red cell aplasia that usually occurs in children 4 years old and younger. This clinical condition has been priorly described to be linked to numerous viral and immunologic mechanisms. COVID-19, caused by the coronavirus SARS-CoV-2, was initially discovered in China in December 2019. The disease quickly spread worldwide, resulting in *** PresentationThis manuscript reports a new clinically relevant condition associated to COVID-19, describing a child with clinical and biochemical signs of Pure Red Blood cells aplasia and complete absence of erythroblasts at the bone marrow needle aspiration with signs of erythrophagocytosis, resembling morphological signs such as in hemophagocytic lymphohistiocytosis (HLH), temporally associated to SARS-CoV-2 *** report highlights a newly described continuum laboratory and clinical spectrum of immune/hematological dysregulations secondary to SARS-CoV-2. SARS-CoV-2 infection-linked TEC has never been described in literature, but, according to our findings, should be considered in all the patients with transient erythroblastopenia without congenital red blood cell abnormalities and serology negative for major infections associated with TEC. This condition must be considered in the same spectrum of MIS-C and the inter-links among the two clinical manifestations, as well as a potential interdependence among them, should be considered in the future.

摘要： Covid-19 is a contagious disease transferable from one person to another. The modes of transmission include direct contact with COVID-affected patients. Covid-19 creates a substantial impact globally. The World Health Organization affirms Covid-19 is an epidemic disease. The indications of COVID-19 were analogous to other viral effects, including cough, cold, fever, diarrhoea, difficulty in breathing, etc. The traditional method is used to recognize the covid through the swab test. A cutting-edge technology employed in the prognosis of many diseases is machine intelligence. There are many algorithms used for recognition and classification. An instrument for machine learning that performs feature evaluation and prediction tasks is a decision tree. The symptoms of COVID-19 were used to predict the severity of COVID-19. Ten symptoms were identified for prediction. The decision tree uses entropy and the Gini index for constructing the tree that evaluates various features. Also, ordinal encoding and one-hot encoding were used for pre-processing the dataset. One-hot encoding improves the model’s performance, which has an F1 score of 80.64% and accuracy, precision, TPR, and FPR values of 84%, 94.2%, 72.8%, and 95.28 per cent, respectively. The AUC for Gini using one-hot encoding outperforms well as 0.86.

关键词： COVID-19 vaccination programme   COVID-19 vaccines   Costing analysis   Immunisation economics   Budgeting and planning  

摘要： BackgroundThe COVID-19 vaccination programme in South Africa was rolled out in February 2021 via five delivery channels- hospitals, primary healthcare (PHC), fixed, temporary, and mobile outreach channels. In this study, we estimated the financial and economic costs of the COVID-19 vaccination programme in the first year of roll out from February 2021 to January 2022 and one month prior, in one district of South Africa, the West Rand *** and economic costs were estimated from a public payer's perspective using top-down and ingredient-based costing approaches. Data were collected on costs incurred at the national level and from the West Rand district. Total cost and cost per COVID-19 vaccine dose were estimated for each of the five delivery channels implemented in the district. In addition, we estimated vaccine delivery costs which we defined as total cost exclusive of vaccine procurement *** financial and economic costs were estimated at US$8.5 million and US$12 million, respectively; with a corresponding cost per dose of US$15.31 (financial) and US$21.85 (economic). The two biggest total cost drivers were vaccine procurement which contributed 73% and 51% to total financial and economic costs respectively, and staff time which contributed 10% and 36% to total financial and economic costs, respectively. Total vaccine delivery costs were estimated at US$2.1 million (financial) and US$5.7 million (economic); and the corresponding cost per dose at US$3.84 (financial) and US$10.38 (economic). Vaccine delivery cost per dose (financial/economic) was estimated at US$2.93/12.84 and US$2.45/5.99 in hospitals and PHCs, respectively, and at US$7.34/20.29, US$3.96/11.89 and US$24.81/28.76 in fixed, temporary and mobile outreach sites, respectively. Staff time was the biggest economic cost driver for vaccine delivery in PHCs and hospitals while per diems and staff time were the biggest economic cost drivers for vaccine delivery in the three ou

摘要： Image classification is a supervised machine learning that can be used to classify the images based on certain factors. Classification takes an image as input and then generates one or more labels that correspond to what it sees in the image. Image classification takes the image as input and interprets what it sees. The major goal of this work involves the use of Deep Learning (DL) approaches. This research work identifies COVID-19 dataset as a critical issue for medical practitioners nowadays because of its rapid dissemination. To address this issue, medical practitioners have tried a variety of tools and approaches to detect and restrict the spread of COVID-19. The best method for identifying COVID-19 is currently using CT (Computed Tomography) scan and X-ray imaging. This diagnostic procedure is particularly accurate since it can see organs in three dimensions. Unfortunately, this procedure requires a radiologist to detect the sickness and takes a long time, medical practitioners will lose crucial time. This research creates a method to detect the coronavirus automatically using DL algorithms. The purpose of this study is to give medical professionals a search tool for CT scans and X-ray images of lungs with COVID-19 infection. DL algorithms such as AlexNet, ResNet, Inception and VGG16 are addressed in this work that helps to automate the classification process. This research work incorporates the CT scans and X-ray images that helps to detect the COVID-19. Experimental result shows that VGG obtains good result compared to other algorithms. VGG obtains 94 % accuracy for X-ray images and 93% accuracy for CT-scan dataset and outperforms best result compared to other algorithms.

关键词： COVID-19 crisis   community college   technical college   racial equity   critical content analysis   CRITICAL RACE THEORY   STUDENTS   PATHWAYS   ACCESS  

摘要： In this study, we interrogated the efforts adopted by community and technical colleges in a Midwestern state to address racial inequities since the onset of COVID-19. We analyzed such efforts reported in institutional newsletters and associated media through critical content analysis supported by text mining techniques. Our findings demonstrate a notable reckoning with racial inequities on the part of institutional leaders and stakeholders. However, many initiatives remain short-term solutions and are detached from a holistic equity focus. This study challenges community and technical college leadership to reimagine their policies, structures, and practices toward advancing racial equity.

关键词： Autism   COVID-19   remote working   inclusive education   autistic teachers   agency   PERSPECTIVES   SCHOOL   VIEW  

摘要： The Covid-19 pandemic resulted in major upheavals in the school education sector, particularly during periods of "lockdown" and remote working. While the impact of these changes on pupils, parents and school staff, both nationally and internationally, has been well-documented, there has been scant consideration of the effects on disabled educators. In addition, the mixed nature of the existing research findings in the special education context - revealing positives, as well as negatives from Covid-related adjustments - suggests the need to learn broader lessons from the impact of these measures. In this article, we report on findings from in-depth, semi-structured interviews with 21 autistic school staff working in a range of roles in schools in the UK. Using thematic analysis, participants' experiences of lockdown were analysed. Importantly, these were also compared with participants' experiences outside of the pandemic. The resulting five themes - sensory issues and the working environment; communication and social barriers and freedoms; stresses and strengths associated with technology; navigating home/school boundaries; and modalities of change - revealed some of the significant difficulties autistic school staff typically experience in their work, as well as the factors that intersect with these. Although periods of Covid-related measures created additional stresses for participants, they also provided a respite from the usual pressures, and indeed enabled their strengths - including an ability to manage change - to be more manifest. Valuable insights, which could benefit autistic school staff, pupils with special educational needs and disabilities, and the whole school community in the UK and beyond, are discussed.

关键词： COVID-19  

摘要： Effective public health messaging benefits from understanding antecedents to unstable attitudes that are more likely to be influenced. This work investigates the relationship between moral and emotional bases for attitudes towards COVID-19 vaccines and variance in stance. Evaluating nearly 1 million X users over a two month period, we find that emotional language in tweets about COVID-19 vaccines is largely associated with more variation in stance of the posting user, except anger and surprise. The strength of COVID-19 vaccine attitudes associated with moral values varies across foundations. Most notably, liberty is consistently used by users with no or less variation in stance, while fairness and sanctity are used by users with more variation. Our work has implications for designing constructive pro-vaccine messaging and identifying receptive audiences. © 2024, CC BY-NC-ND.

关键词： INFORMATION-SEEKING   CANCER COMMUNICATION   SUBJECTIVE NORMS   FEAR APPEALS   HEALTH   MISINFORMATION   ASSOCIATION   PERCEPTION   BELIEF   PRISM  

摘要： The COVID-19 pandemic has brought unprecedented challenges to healthcare and public health messaging in the United States. One area of focus has been vaccination uptake among Black Americans, who have experienced COVID-19 deaths disproportionate to their share of the United States population, raising questions about the processes involved in vaccination perceptions and behaviors. Guided by the Risk Information Seeking and Processing model, this study explored the roles of medical mistrust and social media as a source of risk information in Black Americans' vaccine hesitancy. Survey results from a YouGov panel sample of Black Americans (n = 1,136; 53.5% female) showed that social media use and medical mistrust were positively associated with belief in misinformation related to the COVID-19 vaccine, which, in turn, was positively related to vaccine hesitancy through perceived information insufficiency and information seeking intentions. Furthermore, we found that belief in misinformation and subjective norms toward anti-vaccination also serially mediated the association between social media use and medical mistrust with vaccine hesitancy. Theoretical and practical implications are discussed.

关键词： CHILDREN