Dammbeck1979

The emergence of the novel coronavirus disease (COVID-19) in early 2020 led to the sudden temporary closure of K-12 schools across the United States. Schools were tasked with providing remote instruction to students, and many of these children continued to require mental and behavioral health services provided by school psychologists. In this study, 675 school psychologists were surveyed across the United States to examine how their roles and responsibilities changed as a result of COVID-19. Participants reported the perceived impact of COVID-19 on students' mental health and difficulty serving students and families, as well as their concerns and recommendations pertaining to school reentry. Overall, respondents in this study reported that their roles and responsibilities notably changed because of COVID-19. Participants noted their belief that children and educators will need increased mental health support upon returning to school. Implications for future practice and research are discussed.The COVID-19 pandemic has forced governments to impose major restrictions on individual freedom in order to stop the spread of the virus. With the successful development of a vaccine, these restrictions are likely to become obsolete-on the condition that people get vaccinated. However, parts of the population have reservations against vaccination. While this is not a recent phenomenon, it might prove a critical one in the context of current attempts to manage the COVID-19 pandemic. Consequently, the task of designing policies suitable for attaining high levels of vaccination deserves enhanced attention. In this study, we use data from the Eurobarometer survey fielded in March 2019. They show that 39% of Europeans consider vaccines to cause the diseases which they should protect against, that 50% believe vaccines have serious side effects, that 32% think that vaccines weaken the immune system, and that 10% do not believe vaccines are tested rigorously before authorization. We find that-even when controlling for important individual-level factors-ideological extremism on both ends of the spectrum explains skepticism of vaccination. We conclude that policymakers must either politicize the issue or form broad alliances among parties and societal groups in order to increase trust in and public support for the vaccines in general and for vaccines against COVID-19 in particular, since the latter were developed in a very short time period and resulted-in particular in case of the AstraZeneca vaccine-in reservations because of the effectiveness and side effects of the new vaccines.

The online version contains supplementary material available at 10.1007/s11077-021-09428-0.

The online version contains supplementary material available at 10.1007/s11077-021-09428-0.This article explores why governments do not respond to public compliance problems in a timely manner with appropriate instruments, and the consequences of their failure to do so. Utilising a case study of Italian vaccination policy, the article considers counterfactuals and the challenges of governing health policy in an age of disinformation. It counterposes two methods of governing vaccination compliance discipline, which uses public institutions to inculcate the population with favourable attitudes and practices, and modulation, which uses access to public institutions as a form of control. The Italian government ineffectively employed discipline for a number of years. Epistemological and organisational constraints stymied its efforts to tackle a significant childhood vaccination compliance problem. 5-Azacytidine research buy With a loss of control over the information environment, vaccinations were not served well by exogenous crises, the sensationalism of the news cycle and online misinformation. Hampered by austerity, lack of capacity and epistemic shortcomings, the Italian government did not protect the public legitimacy of the vaccination programme. Instead of employing communications to reassure a hesitant population, they focused on systemic and delivery issues, until it was too late to do anything except make vaccinations mandatory (using modulation). The apparent short-term success of this measure in generating population compliance does not foreclose the need for ongoing governance of vaccine confidence through effective discipline. This is evident for the COVID-19 vaccination campaign, with many Italians still indicating that they would not accept a vaccine despite the devastation that the disease has wrought throughout their country.COVID-19, as an infectious disease, has shocked the world and still threatens the lives of billions of people. Early detection of COVID-19 patients is an important issue for treating and controlling the disease from spreading. In this paper, a new strategy for detecting COVID-19 infected patients will be introduced, which is called Distance Biased Naïve Bayes (DBNB). The novelty of DBNB as a proposed classification strategy is concentrated in two contributions. The first is a new feature selection technique called Advanced Particle Swarm Optimization (APSO) which elects the most informative and significant features for diagnosing COVID-19 patients. APSO is a hybrid method based on both filter and wrapper methods to provide accurate and significant features for the next classification phase. The considered features are extracted from Laboratory findings for different cases of people, some of whom are COVID-19 infected while some are not. APSO consists of two sequential feature selection stages, namely; Initialiagnose strategies as it introduce the maximum accuracy with the minimum time penalty.COVID-19 leads to radiological evidence of lower respiratory tract lesions, which support analysis to screen this disease using chest X-ray. In this scenario, deep learning techniques are applied to detect COVID-19 pneumonia in X-ray images, aiding a fast and precise diagnosis. Here, we investigate seven deep learning architectures associated with data augmentation and transfer learning techniques to detect different pneumonia types. We also propose an image resizing method with the maximum window function that preserves anatomical structures of the chest. The results are promising, reaching an accuracy of 99.8% considering COVID-19, normal, and viral and bacterial pneumonia classes. The differentiation between viral pneumonia and COVID-19 achieved an accuracy of 99.8%, and 99.9% of accuracy between COVID-19 and bacterial pneumonia. We also evaluated the impact of the proposed image resizing method on classification performance comparing with the bilinear interpolation; this pre-processing increased the classification rate regardless of the deep learning architectures used. We c ompared our results with ten related works in the state-of-the-art using eight sets of experiments, which showed that the proposed method outperformed them in most cases. Therefore, we demonstrate that deep learning models trained with pre-processed X-ray images could precisely assist the specialist in COVID-19 detection.Sleep fragmentation refers to the disruption of sleep architecture with poor quality of sleep despite optimal duration of sleep. Sleep fragmentation has been shown to have multiple effects on different body systems. This article reviews the effect of sleep fragmentation on the rate of atherosclerosis which has been linked to comorbidities like myocardial infarction, stroke, and coronary artery disease with an aim to educate patients regarding the importance of sleep hygiene and to incorporate a good amount and quality of sleep as life style modification along with diet and exercise.The Diabetes Prevention Program (DPP) is an evidence-based lifestyle intervention proven to reduce/delay diabetes onset with diet change, physical activity, and modest weight loss. However, access to the program is limited in low-resource communities. Having health profession students facilitate DPP groups as a service learning course-credit opportunity may benefit their interprofessional training while also expanding DPP access in underserved communities. We sought to use student reflections to identify themes to assist with program evaluation and to inform program refinements. Students (N=95) from the University of Missouri-Kansas City (UMKC) medical, physician assistant, and pharmacy programs led DPP groups in urban Kansas City African American churches alongside church health liaisons as part of an interprofessional service-learning course. Students reported creating satisfying, ongoing relationships with participants; developing a deeper understanding of obstacles to weight loss; and learning the role of other health professionals in the care of patients. They also identified obstacles to successful program implementation, such as needing less time in training and having equal participation from students across their interprofessional teams. Students learned important lessons by leading the DPP, but interprofessional service-learning courses have multiple obstacles to successful delivery. Still, this approach has great potential to increase access to the DPP in African American communities and promote skill development in health profession students.Sepsis is a condition that can progress to serious illness and even death. The diagnosis of sepsis is difficult because no unique biomarker exists. With this, health care providers must rely on clinical diagnostic criteria to guide diagnosis. Systemic Inflammatory Response Syndrome (SIRS) criteria have been used for diagnosis since 1992. The more recent attempt to replace SIRS with the quick Sequential Organ Failure Assessment (qSOFA) for assessment of potentially septic patients is troublesome. The qSOFA was designed as a prognostic and not diagnostic tool. Using established processes of evidence-based medicine, it is shown herein that qSOFA fails to meet the definition of a diagnostic assessment tool. Thus, the SIRS assessment should remain the gold standard tool for detecting patients at risk of "sepsis."We examined the interaction between race and labor induction in cesarean delivery in a cohort of 600,000 deliveries in the Cerner Health Facts database. Black women had higher likelihood cesarean (28.9 vs. 26.5%) and lower likelihood of induction of labor at delivery compared to white women (27.2 vs. 32.5%). Induction modified the association between race and cesarean-Black women (odds ratio=1.36, 95% confidence interval 1.30, 1.43) who were induced had significantly increased odds of cesarean delivery.Polypharmacy, or the daily use of five or more medications, is well documented in older adults and linked to negative outcomes such as medication errors, adverse drug reactions, and increased healthcare utilization. Like older adults, people with multiple sclerosis (PwMS) are susceptible to polypharmacy, owing to the variety of treatments used to address individual multiple sclerosis (MS) symptoms and other comorbidities. Between 15-65% of PwMS meet criteria for polypharmacy; in this population, polypharmacy is associated with increased reports of fatigue, subjective cognitive impairment, and reduced quality of life. Despite evidence of adverse outcomes, polypharmacy among PwMS remains a neglected area of research. This article examines the current literature regarding polypharmacy in MS, as well as implications for clinical practice and directions for future research.