Troelsenfoldager1745

Knowledge was associated with the 4th year of study (adjusted odds ratio [aOR] 4.1, 95% CI 1.6-10.3; P less then .001). Attitude was associated with the female sex (aOR 0.7, 95% CI 0.5-1; P=.04) and TV or radio shows (aOR 1.1, 95% CI 0.6-2.1; P=.01). Practices were associated with the ≥24 years age category (aOR 1.5, 95% CI 1.1-2.1; P=.02) and online courses (aOR 1.8, 95% CI 1.1-3.2; P=.03). In total, 592 (80%) medical students were willing to participate in frontline care if called upon. Conclusions Medical students in Uganda have sufficient knowledge of COVID-19 and will be a large reservoir for health care response when the need arises.Background Psychophysiological stress and decreased well-being are relevant issues during prolonged social isolation periods. Relaxation practices may represent helpful exercises to cope with anxiety and stressful sensations. Objective The aim of this research protocol is to test whether remote relaxation practices such as natural sounds, deep respiration, and body scan meditation promote relaxation and improved emotional state and reduce psychomotor activation and the preoccupation related to the coronavirus disease (COVID-19) pandemic. Methods The study population will consist of 3 experimental groups that will randomly receive one of 3 internet-based audio clips containing a single session of guided breathing exercise, guided body scan exercise, or natural sounds. The participants will listen to the fully automated audio clip for 7 minutes and complete pre-post self-assessment scales on their perceived relaxation, psychomotor activation, level of worry associated with COVID-19, and emotional state. At the end of the session, the participants will also be asked to provide qualitative reports on their subjective experiences. Results Analyses will be performed to test the differences in the efficacy of the different audio clips in an internet-based intervention on 252 participants (84 per group), investigating whether natural sounds or remote guided practices such as deep respiration and body scan meditation positively enhance the participants' perceived psychological state. Conclusions The study will provide information on if and to what extent guided practices can help in reducing psychological side effects related to social isolation during the COVID-19 pandemic. International registered report identifier (irrid) PRR1-10.2196/19236.Physicians, nurses, and other healthcare providers initiated the #GetMePPE movement on Twitter to spread awareness of the shortage of personal protective equipment (PPE) during the COVID-19 pandemic. Dwindling supplies, such as face masks, gowns and goggles, and inadequate production to meet increasing demands, has left healthcare workers and patients at risk. The momentum of this Twitter hashtag resulted in a petition to urge public officials to address the PPE shortage through increased funding and production. Simultaneously, GetUsPPE.org was launched by a collaborative of physicians and software engineers to develop a digital platform for the donation, request, and distribution of multi-modal sources of PPE. GetUsPPE.org and #GetMePPE merged in an attempt to combine public engagement and advocacy on social media with the coordination of PPE donation and distribution. Within ten days, over 1800 hospitals and PPE suppliers were registered in a database that allowed for the rapid coordination and distribution of scarce and in-demand materials. One month after its launch, the organization has distributed hundreds of thousands of items of PPE and built a database of over 6,000 PPE requesters[1] The call for action on social media and the rapid development of this digital tool created a productive channel for the public to contribute to the healthcare response to COVID-19 in meaningful ways. #GetMePPE and GetUsPPE.org were able to mobilize individuals and organizations outside of the healthcare system to address the unmet needs of the medical community. The success of GetUsPPE.org demonstrates the potential of digital tools as a platform for larger healthcare institutions (table 1) to rapidly address urgent issues in healthcare. Aloxistatin purchase In this manuscript, we outline this process and discuss key factors determining success.The distributed stabilization problem is studied in this article for a group of heterogeneous second-order agents in the strong-weak competition network containing three kinds of relationships among agents 1) cooperation; 2) strong competition; and 3) weak competition. The entire network satisfies the structural balance condition which can be partitioned into two subnetworks, while the strong and weak competitions are alternate actions on the agents from different subnetworks. To stabilize such heterogeneous networked systems in a distributed way, the switched system approach is developed and utilized in this article, where it is revealed that distributed stabilization can be achieved provided that the ratio on the activating periods of strong and weak competition is chosen appropriately. As an extension, a periodical switching law is taken into account to simplify the design process, where the periodical competition function is introduced correspondingly and several effective sufficient conditions are attained. Finally, the derived analytical results are demonstrated by performing numerical simulations.Cost-sensitive learning methods guaranteeing privacy are becoming crucial nowadays in many applications where increasing use of sensitive personal information is observed. However, there has no optimal learning scheme developed in the literature to learn cost-sensitive classifiers under constraint of enforcing differential privacy. Our approach is to first develop a unified framework for existing cost-sensitive learning methods by incorporating the weight constant and weight functions into the classical regularized empirical risk minimization framework. Then, we propose two privacy-preserving algorithms with output perturbation and objective perturbation methods, respectively, to be integrated with the cost-sensitive learning framework. We showcase how this general framework can be used analytically by deriving the privacy-preserving cost-sensitive extensions of logistic regression and support vector machine. Experimental evidence on both synthetic and real data sets verifies that the proposed algorithms can reduce the misclassification cost effectively while satisfying the privacy requirement.