Bakgrund: I slutet av 2019 upptäcktes ett nytt coronavirus som snabbt spreds globalt. Covid-19 klassades som en pandemi i början av 2020. Hälso- och sjukvården behövde snabbt ställas om och rätta sig efter nya riktlinjer. Till en början hade sjuksköterskor inte tillräckligt med kunskap om det nya viruset vilket påverkade deras omvårdnadsarbete. Syfte: Syftet med studien var att beskriva sjuksköterskors upplevelser av att bedriva omvårdnad under Covid-19 pandemin. Metod: En litteraturöversikt med induktiv ansats genomfördes som inkluderade 13 kvalitativa artiklar. Databaserna CINAHL och PsycInfo användes för sökning efter relevanta artiklar. Ett ramverk för analys beskrivet av Friberg användes vid dataanalys. Resultat: Litteraturöversiktens resultat belyser de komponenter som påverkat sjuksköterskors upplevelser av omvårdnaden under Covid-19 pandemin. Temana ”Nya krav och utmaningar” (att känna rädsla relaterat till smittorisken, att ha bristande kunskap, att arbeta under hög arbetsbelastning) och ”Skyddsutrustningen som en barriär” (att känna trygghet i skyddsutrustningen, att uppleva försvårat arbete med skyddsutrustningen, att kommunicera med skyddsutrustningen) framkom. Slutsats: Rädsla för att bli smittade var en betydande aspekt som påverkade sjuksköterskors omvårdnadsarbete i form av den barriär som skapades till patienterna. Även kunskapsbrist och den höga arbetsbelastningen bidrog till de negativa känsloupplevelser som Covid-19 pandemin medförde. / Background: At the end of 2019, a new coronavirus was discovered, rapidly spreading globally. Covid-19 was classified as a pandemic in early 2020. Healthcare systems needed to be quickly adjusted and adhere to new guidelines. In the beginning, nurses lacked sufficient knowledge about the new virus, which affected their nursing care work. Aim: The aim of the study was to describe nurses' experiences of providing care during the Covid-19 pandemic. Method: A literature review utilizing an inductive approach was conducted, encompassing 13 qualitative articles. The databases CINAHL and PsycInfo were used to identify relevant studies. A framework for analysis described by Friberg was used for data analysis. Results: The literature review's findings illuminate the components that influenced nurses' experiences of nursing care during the Covid-19 pandemic. The themes "New demands and challenges" (feeling fear related to the risk of infection, having insufficient knowledge, working under high workload) and "Protective equipment as a barrier" (feeling safety in protective equipment, experiencing hindered work with protective equipment, communicating with protective equipment) emerged. Conclusion: Fear of infection was a significant factor that impacted nurses' caregiving by creating a barrier between them and the patients. Insufficient knowledge and high workload also contributed to the negative emotional experiences brought by the Covid-19 pandemic.

Bakgrund: I mitten av mars 2020 deklarerade WHO Covid-19 som en pandemi. Till följd av den snabba smittspridningen av viruset ökade patienttrycket på hälso- och sjukvården, många insjuknade och dödstalen eskalerade. För att möta det ökade behovet av palliativ vård gjordes organisatoriska förändringar. Restriktioner och riktlinjer upprättades för att minska smittspridning vilket påverkade sjuksköterskors arbete med vård i livets slutskede under den globala nödsituationen Covid-19 orsakade. Syfte: Syftet med innevarande studie är att belysa sjuksköterskors upplevelser av att ha arbetat med palliativ vård under Covid-19-pandemins globala nödsituation. Metod: Studien är en allmän litteraturöversikt av tio utvalda vetenskapliga artiklar med kvalitativa och kvantitativa ansatser innehållandes kvalitativ data. Artiklarna analyserades enligt Fribergs (2022a) analysmodell. Resultat: Resultatet genererade fem teman: Känsla av att gå emot professionella värderingar, Stärkt samarbete i team, Hög arbetsbelastning, Teknik skapade förutsättningar och Känsla av otillräcklighet och känslomässigt lidande. Konklusion: Sjuksköterskor upplevde känslomässigt lidande relaterat till hög arbetsbelastning och att gå emot sina professionella värderingar i arbetet med palliativ vård. För att ge god palliativ vård behöver sjuksköterskors hälsa främjas och under pandemin upplevde sjuksköterskor att deras känslomässiga lidande lindrades till följd av stöd från kollegor och av känslan att veta att de gjorde ett värdefullt arbete. / Background: In March 2020, the WHO declared Covid-19 a pandemic. As a result of the rapid spread of the virus, patient pressure on healthcare increased, many got sick and the death toll escalated. To meet the increased need for palliative care, organizational changes were made. Restrictions and guidelines were established to reduce the spread of infection, which affected nurses' work with end-of-life care during the global emergency. Aim: The purpose is to highlight nurses' experiences of working with palliative care during the global emergency of the Covid-19 pandemic. Method: The study is a literature review of ten selected scientific articles containing qualitative data. The articles were analyzed according to Friberg's (2022a) analysis model. Findings: The result generated in five themes: Going against professional values, Strengthened cooperation in teams, High workload, Technology created opportunities and Feeling of inadequacy and emotional suffering. Conclusion: Nurses experienced emotional distress related to a high workload and going against their professional values in palliative care work. To provide good palliative care, nurses' health needs to be promoted and during the pandemic nurses felt that their emotional distress was alleviated by support from colleagues and the feeling of knowing they were doing valuable work.

Abstract The Covid –19 pandemic requires sustainable behavioural changes to mitigate the spread of the infection. Thus, people are requested to comply with the recommendations given by the authorities. However, people vary with regard to how well they follow the recommendations. It is therefore of importance to understand the driving forces behind behavioural change. This study aims to investigate how people’s willingness to comply with preventive behaviour during a pandemic is related to the prosocial factor of benevolence.  A cross-sectional study was performed online on an independent sample (N=1014). A correlational analysis was performed between the variable benevolence and degree of willingness for complying with recommendations as well as for the motives for obeying or disobeying the instructions from authorities.  The result showed a significant correlation for the whole study population between how well they followed the recommendations (M = 4.16, S = 0.92) and the levels of benevolence (M = 3.58, S = 0.74) r = 0.22, p = <0.001.  Further, there was a significant correlation between altruistic motives and compliance with recommendations, including the view on taking the vaccine. Our findings add to the concept that prosocial orientation during the covid-19 pandemic in Sweden increases compliance with preventive behaviour.

[pt] A Organização Mundial da Saúde declarou que a resistência aos antibióticos é uma das 10 principais ameaças globais à saúde pública. Entre os fatores que causam a disseminação de bactérias multirresistentes está o uso excessivo de antibióticos em hospitais. Esta tese baseia-se na premissa de que é necessário usar dados históricos para melhorar a prescrição de antibióticos e, assim, reduzir o impacto da resistência em ambientes hospitalares. Seus objetivos específicos incluem a análise de dados para fornecer informações que possam apoiar a prescrição de antibióticos, evitando assim que as taxas de resistência permaneçam elevadas após a pandemia de COVID-19 e prevenindo futuras quebras de protocolo semelhantes.. A tese também investiga as diferenças de desfechos entre a apresentação de bactérias resistentes e não resistentes em infecções adquiridas na comunidade. Para alcançar esses objetivos, os métodos incluem ferramentas de análise de dados, como estatísticas descritivas e inferenciais, Regressão Logística, Mineração de Processos e Mineração de Texto. Os dados incluem informações sobre pacientes internados em Unidades de Terapia Intensiva em hospitais de uma rede privada localizados no Rio de Janeiro, Brasil. A tese é composta por três artigos e descreve ainda uma plataforma desenvolvida para apoiar a prescrição de antibióticos em hospitais. Os resultados da tese revelaram um aumento significativo no consumo de antibióticos durante a pandemia, especialmente durante o segundo e terceiro meses da doença no Brasil. Esse aumento, aliado à alta variabilidade nos tratamentos de pacientes com COVID-19, demonstra que a incerteza em relação à doença levou ao não cumprimento dos protocolos previamente estabelecidos. O meropenem, um antibiótico da classe dos carbapenêmicos, teve o maior número ajustado de doses prescritas para pacientes com COVID-19 nos hospitais analisados. O aumento na prescrição de carbapenêmicos provavelmente explica o aumento observado na resistência a esse antibiótico durante o surto de COVID-19. No período pós-surto, a taxa de resistência aos carbapenêmicos diminuiu, seguindo a queda no consumo desses antibióticos após os primeiros meses da pandemia. No entanto, mesmo com a diminuição, os níveis de resistência pós-surto permaneceram mais altos do que antes da pandemia. Além disso, observou-se que a pandemia alterou outro hábito dos médicos nos hospitais pois o número de exames por paciente aumentou durante a pandemia e, mesmo após o surto da doença, continuou mais alto do que antes da doença. A tese também demonstrou como ferramentas de Mineração de Texto podem ser utilizadas na etapa de tratamento dos dados, possibilitando a inclusão de mais informações nas análises. Constatou-se ainda que, embora um terço dos pacientes admitidos em unidades de terapia intensiva apresentassem bactérias resistentes, não houve evidência de que isso implicasse em maiores chances de mortalidade hospitalar ou sepse em comparação com pacientes com infecções comunitárias por bactérias não resistentes. / [en] The World Health Organization has declared that antimicrobial resistance is one of the top 10 global public health threats facing humanity. Among the factors that cause the dissemination of multidrug-resistant bacteria is the overuse of antimicrobials in hospitals. This thesis is based on the premise that it is necessary to use historical data to improve antimicrobial prescription and thus reduce the burden of antimicrobial resistance in hospital settings. Its specific goals include analyzing data to provide information that can support antimicrobial prescription, thus avoiding antimicrobial resistance rates remaining high after the COVID-19 pandemic and preventing future similar protocol breakdowns. It also investigates the differences in outcomes between presenting resistant vs. non-resistant bacteria in community-acquired infections. To achieve these objectives, the methods include data analysis tools such as descriptive and inferential statistics, Logistic Regression, Process Mining, and Text Mining. The data includes information on patients admitted to Intensive Care Units in hospitals from a private network located in Rio de Janeiro, Brazil. The thesis comprises three articles and describes a CDSS developed to support antimicrobial prescription in hospitals. The thesis s findings revealed a significant increase in antimicrobial consumption and high variability in treatments for COVID-19 patients. Specifically, meropenem, a carbapenem-class antimicrobial, presented the highest adjusted number of doses prescribed for COVID-19 patients in the analyzed hospitals. The escalation in carbapenem prescription probably explains the observed increase in carbapenem resistance during the COVID-19 surge. In the post-surge, the carbapenem resistance rate decreased, following the decrease pattern we found in carbapenem consumption after the first months of the pandemic. Even though there was a decrease in carbapenem resistance, the post-surge levels remained higher than before the surge. Besides, this thesis did not find an association between presenting with antimicrobial-resistant bacteria and higher chances of hospital mortality or sepsis in patients with community-acquired infections.

Background: The COVID-19 pandemic has been affecting everyone’s daily life in unknown measures since its outbreak. Nearly all Universities around the globe were affected. Further, young people and University students in particular, are known to be vulnerable for developing mental disorders. This study aims to examine the mental health social and emotional well-being and perceived burdens of University students during COVID-19 pandemic lockdown in Germany. Materials and Methods: This cross-sectional and anonymous online survey among University students assessed mental health status with standardized measures (depressive symptoms, alcohol and drug consumption, and eating disorder symptoms), attitudes toward the COVID-19 pandemic and perceived burdens, and social and emotional aspects of the pandemic (social support, perceived stress, loneliness, and self-efficacy). Results: In total, N = 3,382 German University students participated. Nearly half of the students (49%) reported that they are worried or very much worried about the COVID-19 pandemic. The majority supports the governmental lockdown measures (85%). A Patient Health Questionnaire-9 (PHQ-9) sum score of 10 or above, indicating clinically relevant depressive symptoms, was reported by 37% (n = 1,249). The PHQ-9 sum score was on average 8.66 (SD = 5.46). Suicidal thoughts were indicated by 14.5%of the participants. Levels of depressive symptoms differed significantly for the different self-rated income changes during the pandemic (increase, decrease, no change in income). Further, levels of depressive symptoms and suicidal ideation differed significantly for students from different faculties. Multiple regression analyses revealed that not being a parent, having no indirect social contact one or two times a week, higher perceived stress, higher experienced loneliness, lower social support, and lower self-efficacy significantly predicted higher scores of depressive symptoms, also higher hazardous alcohol use, and higher levels of eating disorder symptoms. Other aspects of lifestyle such as social and cultural activities, dating, and hobbies were reported to be negatively affected during the pandemic. Conclusion: The present study implies that University students are vulnerable and due to elevated depressive symptoms at risk, being hit hard by the pandemic, but are in general coping adaptively. Low-threshold online interventions promoting help-seeking and also targeting various mental health conditions might bridge the gap the COVID-19 pandemic opened up recently.

No description available.

COVID-19 has taken a major toll on the tourism industry. Traveler confidence has decreased as travel restrictions and fears regarding the virus have increased. As tourist destinations rely more on local tourism to survive the current crisis, communicating COVID-19 procedures is vital to mitigate tourist ́s safety concerns. Therefore, the objective of this study was to use content analysis to identify COVID-19 policy initiatives highlighted within tourism company websites and to compare regions to identify differences in website communication content. The study analyzed 100 various tourism company websites throughout Sweden. The findings and the supporting literature indicating that the websites should have information available about booking policy, operation limitations, social distance policy, and customer/staff responsibility to properly communicate their COVID-19 policies. Overall, the tourism company websites in this study were lacking in the communication of COVID-19 policies. To ameliorate communication of COVID-19 procedures, this study offers recommendations for tourism firms to incorporate.

COVID-19 continues to have a major impact on US health and society. Robust research on the epidemiology of acute and post-acute COVID-19 remains fundamentally important to informing policy makers, scientists, as well as the public. This dissertation reports on the development of a large, diverse, United States general population-based meta-cohort with standardized, prospective ascertainment of SARS-CoV-2 and COVID-19, integrated with comprehensive pre-pandemic phenotyping from 14 extant cohort studies. Meta-cohort data were used to investigate risk and resilience factors for incident severe (hospitalized or fatal) and non-severe COVID-19 and correlates of time-to-recovery from SARS-CoV-2 infection. Results support the major acute and post-acute public health impact of COVID-19 and the vital role of modifiable (e.g., obesity, diabetes, cardiovascular disease) and non-modifiable (e.g., age, sex) risk factors for adverse COVID-19 outcomes. Findings suggest that standard primary care interventions—including obesity and cardiometabolic disease prevention and treatment, depression care, and vaccination—remain fundamental to COVID-19 risk mitigation among US adults. Given its longitudinal design and comprehensive pre-pandemic and pandemic-era measurements, the meta-cohort is well suited to support ongoing work regarding the public health impact of SARS-CoV-2 infection, COVID-19, post-acute sequelae, and pandemic-related social and behavioral changes across multiple health domains.

Background: The COVID-19 pandemic posed an unparalleled challenge to worldwide public health systems, characterized by its high transmissibility and the initial absence of accessible testing, treatments, and vaccines. The deficiency in public awareness and the scarcity of readily available public health information regarding this century's disaster further intensified the critical need for innovative solutions to bridge these gaps. In response, Shaman Labs1,2, leveraging its deep expertise in forecasting for influenza3, Ebola, and various SARS viruses, initiated the development of country-wide COVID-19 projections within weeks following the WHO's declaration of the pandemic4–6. Almost immediately thereafter, it became necessary to create a sophisticated online platform—a system capable of displaying county-specific COVID-19 forecasts, including daily estimated infections, cases, and deaths. This platform was designed to allow users to select any county, state, or national geography and compare it with another, under various scenarios of social distancing measures. Additionally, the architecture of this system was required to facilitate the regular integration of updated data, ensuring the tool's ongoing relevance and utility. Columbia University's data visualization system aimed to communicate epidemiological forecasts to various stakeholders. At the onset of the COVID-19 pandemic, amid escalating uncertainty and the pressing need for reliable data, Dr. Rundle played a pivotal role in briefing key stakeholders on the unfolding crisis. His efforts were directed towards providing Congressman Ron Johnson, Chairman of the U.S. Senate Committee on Homeland Security & Governmental Affairs, and Congresswoman Anna Eshoo, as well as their staff, with up-to-date projections and analyses derived from the Classic Data Visualization tools. Dr. Rundle’s consultative role extended to a diverse array of institutions including the U.S. Army Corps of Engineers, the U.S. Air Force, and the Federal Reserve Board, as well as advising private entities such as Pfizer, MetLife, and Unilever. His expertise facilitated informed planning and response efforts across various levels of government and sectors, underscoring the critical role of sophisticated data visualization from the earliest stages of the pandemic. This Integrated Learning Experience (ILE) examines the development and implementation of the Time Machine platform, focusing on its application in visualizing and analyzing COVID-19 epidemiological forecasts. The study explores methods for improving forecast data presentation, analysis, and accuracy assessment. Methods: The body of this work unfolds through a series of critical chapters that collectively address the multifaceted functionality and impact of the Time Machine platform. Initially, the work focuses on the construction of the Time Machine platform, a web-based R interactive user interface coupled with cloud-based database system, specifically tailored for the intuitive visualization of epidemiological forecasts, detailing the technical and design considerations essential for enabling users to interpret complex data more effectively. Following this, the implementation of a rigorous data-discovery framework is presented, examining case reporting inconsistencies across different regions, using low-level GitHub and Windows scripting technologies, thereby highlighting the significance of accurate data collection and the impact of discrepancies on public health decisions. The narrative then transitions to the implementation of advanced statistical models, such as strictly proper scoring and weighted interval scoring, to assess the accuracy of the forecasts provided by the Time Machine platform, using a dedicated R library and testing with the help of MS Excel sandbox, underscoring the importance of reliable predictions in the management of public health crises. Lastly, a detailed analysis is conducted, encompassing countrywide data (3142 counties) over an extended period (147 weeks), utilizing Generalized Estimating Equations (GEE) to identify key predictors that influence forecast accuracy, offering valuable insights into the factors that either enhance or detract from the reliability of epidemiological predictions. Results: The deployment of the Classic Data Visualization and the subsequent evolution of the Time Machine platform have significantly advanced epidemiological forecast visualization capabilities. The Time Machine platform was designed with an automated data refresh system, allowing for regular updates of epidemiological forecast data and reported actuals. The project developed tools for monitoring and evaluating the quality of public health reporting, aiming to improve the accuracy and timeliness of data used in public health decisions. Additionally, the research implemented methods for standardizing forecast accuracy assessments, including the normalization of scores to enable comparisons across different geographical scales. These approaches were designed to support both local and national-level pandemic response efforts. The accuracy analyses throughout different phases of the pandemic revealed a 42% improvement in forecast accuracy from Phase 1 to Phase 7. Larger populations (27% increase per unit increase on a base-10 logarithmic scale) and higher county-level activity (45% increase from the lowest to the highest quartile) resulted in better estimations. Additionally, the analysis highlighted the significant impact of reporting quality on forecast accuracy. On the other hand, the study identified the challenges in predicting case surges, showing a 27% decline in accuracy during periods of rising infections compared to declining periods. The regression results highlight the potential benefits of improving data collection and providing timely feedback to forecasting teams. Conclusion: This study demonstrates the potential of advanced data visualization and accuracy measurement techniques in improving epidemiological forecasting. The findings suggest that factors such as urbanicity, case reporting quality, and pandemic phase significantly influence forecast accuracy. Further research is needed to refine these models and enhance their applicability across various public health scenarios.

Small molecules are powerful tools to probe biological systems and cure diseases. In the scope of this dissertation, small molecules were applied to study three distinct disease models: cancer, Sedaghatian-type spondylometaphyseal dysplasia (SSMD), and COVID-19. First, encouraged by the recently reported vulnerability of drug-resistant, metastatic cancers to GPX4 (Glutathione Peroxidase 4) inhibition, we examined the basis for nanomolar potency of proof-of-concept GPX4 inhibitors, which revealed an unexpected allosteric binding site. Through hierarchical screening of a lead-optimized compound library, we identified novel small molecules binding to this allosteric site. Second, a homozygous point mutation in the GPX4 gene was identified in three living patients with SSMD. With a structure-based analysis and cell models of the patient-derived variant, we found that the missense variant significantly changed the protein structure and caused substantial loss of enzymatic function. Proposed proof-of-concept treatments were subsequentially validated in patient fibroblasts. Our further structural investigation into the origin of the reduced enzymatic activity revealed a key residue modulating GPX4 enzymatic function. We also found that the variant alters the degradation of GPX4, unveiling the native degradation mechanism of GPX4 protein. Third, driven by the recent urgent need for COVID-19 antiviral therapeutics, we utilized the conservation of 3CL protease substrate-binding pockets across coronaviruses to identify four structurally divergent lead compounds that inhibit SARS-CoV-2 3CL protease. With structure-based optimization, we ultimately identified drug-like compounds with < 10 nM potency for inhibiting the SARS-CoV-2 3CL protease and blocking SARS-CoV-2 replication in human cells.