关键词： COVID-19 pandemic   Frequency analysis   Future of work   Hashtag research framework   Human resource management   Labour market   Sentiment analysis   Social media analysis   Topic analysis   Twitter   Work -life balance   TRADITIONAL OFFICE   FAMILY INTERFACE   HEALTH   PERFORMANCE   SATISFACTION   MANAGEMENT   EMPLOYEES   CONFLICT   OUTCOMES   PARENTS  

摘要： This research examines the perceptions of Twitter users regarding the prevalent topics within Work-Life Balance communication before and after the COVID-19 pandemic. The pressing questions surrounding current labour market drivers are addressed, particularly regarding the ongoing Fourth Industrial Revolution and the COVID-19 pandemic's impact on communicated themes, particularly in the Human Resource Management field, where Work-Life Balance has emerged as a key concept. Social media platforms like Twitter are pivotal in fostering discussions on Work-Life Balance in society. Over the past decade, Twitter has evolved into a significant research platform researchers utilise in more than ten thousand research articles. The online discourse on Twitter raises awareness of the importance of balancing work and personal life. The COVID-19 pandemic has unveiled new facets of Work-Life Balance, with social media as a key platform for discussing these issues. This research uses Social Media Analysis based on the Hashtag Research framework. A total of 1,768,628 tweets from 499,574 users were examined, and frequency, topic, and sentiment analysis were conducted. Pre-pandemic, the most communicated Work-Life Balance topics were performance and time management, while recruitment and employee development were identified post-pandemic. Pre-pandemic, the highest proportion of negative sentiment was time management and mental health prevention, shifting to time, employee development, and mental health prevention post-pandemic. Despite the limitations of our research, a proposed redefinition of the concept is also presented, including a design for an integrated Work-Life Balance model based on topics communicated by Twitter users. Given the need for a more robust approach to redefining the concept and developing an integrative WorkLife Balance model, the article provides fresh insights for future research.

关键词： coronavirus disease-2019   antipsychotic concentration   mental disorder   therapeutic drug monitoring   clozapine   METABOLIZING-ENZYMES   SERUM CONCENTRATIONS   IN-VITRO   POLYMORPHISMS   ARIPIPRAZOLE   INFLAMMATION   OLANZAPINE   CESSATION   SMOKING   CYP3A5  

摘要： Purpose: Examine the alterations in antipsychotic concentrations following coronavirus disease-2019 (COVID-19) infection among hospitalized patients with mental disorders and conduct an analysis of the factors influencing these changes. Methods: Data were collected from inpatients at Beijing Huilongguan Hospital between December 12, 2022, and January 11, 2023, pre- and post-COVID-19. Based on the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, 329 inpatients with mental disorders were included (3 with incomplete data excluded). Primary outcomes assessed changes in antipsychotic concentrations pre- and post-COVID-19, while secondary outcomes examined factors linked to concentration increases and antipsychotic dose adjustments. Results: Clozapine (P < 0.001), aripiprazole (P < 0.001), quetiapine (P = 0.005), olanzapine (P < 0.001), risperidone (P < 0.001), and paliperidone (P < 0.001) concentrations increased post-COVID-19 in patients with mental disorders. Notably, clozapine concentration surpassing pre-infection levels was highest. Clozapine users were more likely to adjust their dose (50.4%) compared to olanzapine (17.5%) and other antipsychotics. Moreover, traditional Chinese patent medicines and antibiotics during COVID-19 infection were associated with antipsychotic reduction or withdrawal (OR = 2.06, P = 0.0247; OR = 7.53, P = 0.0024, respectively). Conclusion: Antipsychotic concentrations in hospitalized patients with mental disorders increased after COVID-19 infection, that may be related not only to COVID-19, but also to the use of Chinese patent medicines during infection. The pre-infection concentration and types of antipsychotics, patient's gender, and combination of traditional Chinese medicine or antibiotics, were factors found to correlate with increased drug concentrations and necessitate dose adjustments.

关键词： COVID-19   Mapper graph   Data visualization   Clustering   Malaysia   TIME  

摘要： Huge amounts of data have been collected from various sources during the COVID-19 pandemic, providing a unique opportunity for analysis, data-driven modelling, and machine learning in understanding the complexity of COVID-19 more effectively and make informed decisions. To keep with the expanding quantity and complexity of data while employing minimal assumptions, a topological data analysis tool known as the Mapper algorithm is used to explore Malaysia's daily confirmed cases, deaths, and vaccination data from the onset of the pandemic to June 2022 via data visualization and clustering. A support vector-based feature selection and a heuristic approach for fine-tuning parameters internally within the algorithm are conducted. Two anomalous groups of nodes with exceptionally high case numbers emerged respectively for Delta and Omicron dominant periods in the Mapper graphs for daily data. Selangor cumulative cases have been found to be numerically dissimilar from other states from August 2021 onwards. The evolution of Mapper graphs revealed unique early COVID-19 progression in Johor, Negeri Sembilan, and Kuala Lumpur in the first half of 2020, followed by a significant increase in confirmed cases in Sabah in September 2020. Clusters identified by the Mapper algorithm are comparable with those obtained from principal component analysis and hierarchical clustering. Still, the hierarchical clustering does not further subdivide Selangor data into two to three separate clusters as the Mapper algorithm does. This research provides valuable insights for comprehending the pandemic timeline in Malaysia via the Mapper algorithm, which serves as a highly compact data visualization technique.

关键词： Karachi stock exchange   KSE-100 index   COVID-19   Machine learning   Performance metrics   MARKET   IMPACT   OIL   GOLD  

摘要： The COVID-19 pandemic has great effects for economies internationally. This study studies the interconnection between COVID-19 metrics and Pakistan's premier stock exchange, the Karachi Stock Exchange (KSE) with the object of identifying the most effective machine learning (ML) model for predicting KSE developments in the pandemic. Our investigation periods the peak COVID-19 period from March 1, 2020, to November 26, 2021, applying data from both the KSE 100 index and COVID-19 associated variables. Five various ML methods were applied involving Linear Regression (LR), K-Nearest Neighbors (KNN), Random Forest (RF), Regression Tree (Rtree), and Support Vector Machine (SVM) and measured their performance employing critical accuracy metrics such as Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-squared (R2). The outcomes discover that the RF model outperformed its equivalents realizing an R2 of 0.91 with k = 5. These results conflict with a previous study that supported a negative impact of COVID-19 on improved stock markets. The visions from this study can assist investors in managing strategic investment decisions and assist policymakers in making measures to reduce the pandemic's effects on the stock market.

关键词： Native American   Public health   COVID-19   Cultural respect   Qualitative research   Rural  

摘要： Background: Native American communities suffered disproportionately negative effects during the COVID-19 pandemic, yet no research has explored the experiences of rural Eastern Region Native Americans. Methods: Informed by the Native Reliance Model and Indigenous Standpoint Theory, we conducted a qualitative descriptive project in the Spring and Summer of 2022; data included semistructured interviews and focus groups with 24 individuals representing five South Carolina tribal groups. Findings: Thematic analysis yielded four emergent themes: 1) "Let's just finish the Indians off": Pandemic distrust rooted in historical and contemporary Native American experiences; 2) "We have been misled": Making sense of conflicting public health information; 3) "I'm not giving it to some innocent person": COVID-19 mitigation behaviors as Native American cultural practice; and 4) "We put the plan in place": Self-advocacy and action as a source of Native American pride and responsibility. Interpretation: These participants demonstrated resiliency grounded in family and tribal ties, even in the face of personal losses, economic struggles, and healthcare barriers. To strike a balance between cultural traditions and public health recommendations, public health practitioners should 1) build partnerships with community leaders, elders, and tribal health authorities to facilitate the development of culturally respectful interventions that address specific health concerns in the context of historical traumas; and 2) implement alternative methods of communication and engagement to ensure equitable access to healthcare services in rural locations, such as collaborating with community organizations, deploying mobile health units, and utilizing traditional channels of communication within these communities.

关键词： SINGLE  

摘要： Background. COVID-19 is the major cause of serious acute respiratory complications, leading to strong perturbation of cytokine release and high levels of interleukin-6. In this work, we assessed the efficacy and safety of Tocilizumab in Tunisian patients with severe COVID-19. Materials and Methods. We enrolled in this retrospective study 255 critically ill patients in the intensive care unit of the military hospital of Tunis. These patients were categorized into a standard group (n = 148) and a Tocilizumab group (n = 107). Medical records were gathered and analyzed between September, 2020 and September, 2021. Results. No significant difference was detected in clinical and biological parameters at admission between the two studied groups. Interestingly, we revealed here a significant improvement in respiratory parameters as well as biological findings such as hemoglobin (p=0.001), platelets (p=0.005), CRP (p=0.0001), fibrinogen (p=0.0001), and creatinine (p=0.002). Regarding the Simplified Acute Physiology Score II and 28-day mortality, we showed a significant decrease of these two parameters following 48 hr of drug administration (p=0.0001, respectively). No significant difference between the two groups regarding complications except the renal failure and the need for renal replacement therapy, which are higher in the standard group as compared to patients treated with Tocilizumab (p=0.02; p=0.0001, respectively). Conclusions. Treatment with TCZ seems to be safe and effective in reducing the mortality rate in severe COVID-19 patients.

关键词： e-learning   COVID-19 pandemic   Bibliometric analysis   VOSviewer   Citespace   GOOGLE SCHOLAR   EDUCATION  

摘要： The COVID-19 pandemic has considerably affected e-learning, leading to a surge in research output from 2020 to 2022. This increase is attributed to the rapid transition to online education, presenting challenges and opportunities for teachers. The shift to online education has also prompted the exploration of innovative approaches and educators' as well as learners' perceptions and experiences in e-learning. In this study, the research output on e-learning from 2020 to 2022 has been analyzed to reveal the major research trends. The study employs bibliometric techniques to explore the data retrieved from Scopus. Particularly, an analysis of metadata such as the geographical distribution of publications, authorship, keywords, and the impact of works has been conducted. The results reveal the most influential authors and works, as well as the emerging topics in the field. The data has been processed by utilizing bibliometric tools such as VOSviewer, Citespace, and Harzing's Publish or Perish.

关键词： COVID-19 pandemic   anxiety   cortical surface area   cortical thickness   depression   stress  

摘要： BACKGROUND:According to person-by-environment models, individual differences in traits may moderate the association between stressors and the development of psychopathology; however, findings in the literature have been inconsistent and little literature has examined adolescent brain structure as a moderator of the effects of stress on adolescent internalizing symptoms. The COVID-19 pandemic presented a unique opportunity to examine the associations between stress, brain structure, and psychopathology. Given links of cortical morphology with adolescent depression and anxiety, the present study investigated whether cortical morphology moderates the relationship between stress from the COVID-19 pandemic on the development of internalizing symptoms in familial high-risk adolescents. METHODS:Prior to the COVID-19 pandemic, 72 adolescents (27M) completed a measure of depressive and anxiety symptoms and underwent magnetic resonance imaging. T1-weighted images were acquired to assess cortical thickness and surface area. Approximately 6-8 months after COVID-19 was declared a global pandemic, adolescents reported their depressive and anxiety symptoms and pandemic-related stress. RESULTS:Adjusting for pre-pandemic depressive and anxiety symptoms and stress, increased pandemic-related stress was associated with increased depressive but not anxiety symptoms. This relationship was moderated by cortical thickness and surface area in the anterior cingulate and cortical thickness in the medial orbitofrontal cortex such that increased stress was only associated with increased depressive and anxiety symptoms among adolescents with lower cortical surface area and higher cortical thickness in these regions. CONCLUSIONS:Results further our understanding of neural vulnerabilities to the associations between stress and internalizing symptoms in general, and during the COVID-19 pandemic in particular.

关键词： COVID-19   Compartment model   Mediation effect   Policy evaluation   Linear and nonlinear regressions   NONPHARMACEUTICAL INTERVENTIONS   COUNTRIES   JANUARY   IMPACT  

摘要： By collecting various control policies taken by 127 countries/territories during the first wave of COVID-19 pandemic until July 2nd, 2020, we evaluate their impacts on the epidemic dynamics quantitatively through a combination of the multiple linear regression, neural-network-based nonlinear regression and sensitivity analysis. Remarkable differences in the public health policies are observed across these countries, which affect the spreading rate and infected population size to a great extent. Several key dynamical features, like the normalized cumulative numbers of confirmed/cured/death cases on the 100th day and the half time, show statistically significant linear correlations with the control measures, which thereby confirms their dramatic impacts. Most importantly, we perform the mediation analysis on the SEIR-QD model, a representative of general compartment models, by using the structure equation modeling for multiple mediators operating in parallel. This, to the best of our knowledge, is the first of its kind in the field of epidemiology. The infection rate and the protection rate of the SEIR-QD model are confirmed to exhibit a statistically significant mediation effect between the control measures and dynamical features of epidemics. The mediation effect along the pathway from control measures in Category 2 to four dynamical features through the infection rate, highlights the crucial role of nucleic acid testing and suspected cases tracing in containing the spread of the epidemic. Our data-driven analysis offers a deeper insight into the inherent correlations between the effectiveness of public health policies and the dynamic features of COVID-19 epidemics.

关键词： COVID-19 incidence   Carbon monoxide   Post -pandemic era   Spatiotemporal pattern   Vaccination   AIR-POLLUTION   DAILY MORTALITY   INFLAMMATION   MARKERS  

摘要： Background: Previous research has established carbon monoxide (CO) as a significant air pollutant contributing to coronavirus disease 2019 (COVID-19) transmission. The spatiotemporal heterogeneity in the relationship between short-duration CO exposure and COVID-19 incidence remain underexplored. Investigating such heterogeneity plays a crucial role in designing region-specific cost-effective public health policies, exploring the reasons for heterogeneity, and understanding the temporal trends in the association between CO and an emerging infectious disease such as COVID-19. Methods: The 49 states of the continental United States (U.S.) were examined in this study. Initially, we developed time-series generalized additive models (GAMs) for each state to assess the preliminary correlation between daily COVID-19 cases and short-term CO exposure from April 1, 2020, to December 31, 2021. Subsequently, the correlations were compiled utilizing Lerouxprior-based conditional autoregression (LCAR) to achieve a smoothed spatial distribution. Finally, we integrated a time-varying component into the GAM and LCAR to analyze temporal correlations and illuminate the factors contributing to spatiotemporal heterogeneity. Results: Our analysis revealed that, across the 49 states, a 10-ppb increase in CO concentration was associated with a 1.33 % (95%CI: 0.86%-1.81 %) increase in COVID-19 cases on average. Furthermore, spatial variability was noted, with weaker correlations observed in the central and southeastern regions, stronger associations in the northeastern regions, and negligible associations in the western regions. Temporally, the correlation was not significant from April 2020 to June 2021, but began to increase steadily thereafter until the end of 2021. Additionally, vaccination and temperature were determined to be potential causes contributing to the heterogeneity, indicating stronger positive associations in areas with higher vaccination rates and temperatures. Conclusion: