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The COVID-19 pandemic has resulted in school closures and distancing requirements that have disrupted both work and family life for many. Concerns exist that these disruptions caused by the pandemic may not have influenced men and women researchers equally. Many medical journals have published papers on the pandemic, which were generated by researchers facing the challenges of these disruptions. Here we report the results of an analysis that compared the gender distribution of authors on 1893 medical papers related to the pandemic with that on papers published in the same journals in 2019, for papers with first authors and last authors from the United States. Using mixed-effects regression models, we estimated that the proportion of COVID-19 papers with a woman first author was 19% lower than that for papers published in the same journals in 2019, while our comparisons for last authors and overall proportion of women authors per paper were inconclusive. A closer examination suggested that women's representation as first authors of COVID-19 research was particularly low for papers published in March and April 2020. Our findings are consistent with the idea that the research productivity of women, especially early-career women, has been affected more than the research productivity of men. [ABSTRACT FROM AUTHOR]

Background: A large number of papers regarding coronavirus disease 2019 (COVID-19) and epilepsy have been published since the declaration of the COVID-19 pandemic. However, there is no bibliometric analysis on these papers. In this study, we aimed to analyze the bibliometric characteristics of these papers, thus identifying the trends and future directions of COVID-19 and epilepsy research. Methods: Scientific papers regarding COVID-19 and epilepsy were retrieved through searches of the Web of Science Core Collection database. Title, authors, contributing institute, country, source journal, times cited, and additional information were extracted from each selected paper. Microsoft Excel 2019 and GraphPad Prism 8 were used to analyze the extracted data and export the bar charts and tables whilst VOSviewer software was used to perform and visualize co-authorship analysis and co-occurrence analysis of keywords. Results: A total of 317 papers regarding COVID-19 and epilepsy were included in the final analysis. Epilepsy & Behavior published the largest number of papers (n = 84). J. Helen Cross and Naoto Kuroda were the most prolific authors (n = 13 each). The United States (n = 88) and the University of London (n = 23) were the country and organization with the most contributions, respectively. The strongest authors' collaborations were between Giovanni Assenza and Jacopo Lanzone and between J. Helen Cross and Nathalie Jette. Selected author keywords were organized into seven clusters, and the keywords in clusters 1 and cluster 4 had the largest average appearing year of any clusters. Conclusion: This is the first bibliometric analysis of papers regarding COVID-19 and epilepsy. Our results showed that the United States was the leading country whilst J. Helen Cross was the most influential scholar in COVID-19 and epilepsy research. psychological consequences of COVID-19, and the safety of COVID-19 vaccines for people with epilepsy, are possible areas for future research on COVID-19 and epilepsy. [ABSTRACT FROM AUTHOR]

Since the early 2010s, the production of industrial wood pellet for overseas exports has grown considerably in the Southern United States (U.S.). In order to meet the increasing demand for woody biomass feedstock, the wood pellet mills in the U.S. South consume roundwood pulpwood along with low-valued logging residues and sawmill wood wastes. By estimating empirical roundwood pulpwood market models based on the bi-monthly time-series data, this study estimates the impacts of wood pellet production, precipitation, and the COVID-19 Pandemic on the hardwood and softwood pulpwood stumpage prices in 12 southeastern Atlantic coastal micro-markets. Results suggest that the wood pellet industry has led to an increase in pine pulpwood prices, but no statistically significant impact is found on the hardwood pulpwood prices. Rainfall is found to be a positive determinant of the roundwood pulpwood prices. Furthermore, since March 2020, the COVID-19 Pandemic is estimated to lead about 6% decline in both pine and hardwood pulpwood prices. This study provides empirical evidence and additional insights on the timber market dynamics and how small roundwood markets have reacted to the additional wood demand from a growing bioenergy industry in the U.S. South. • The wood pellet industry competes with pulp and paper mills for woody biomass. • The wood pellet industry has led to an increase in pine pulpwood prices. • No significant impact of wood pellets is found on the hardwood pulpwood prices. • The COVID-19 Pandemic has led to decline pulpwood stumpage prices. [ABSTRACT FROM AUTHOR]

EU-US data transfers for health research remain a particularly thorny issue in view of the stringent rules of the EU General Data Protection Regulation (GDPR) and the challenges related to US mass surveillance programs, particularly the manner in which US law enforcement and national security agencies can access personal data originating from the EU. Since the entry into force of the GDPR, evidence of impeded collaborations is increasing, particularly in the case of sharing data with US public institutions. The adoption of a new EU-US adequacy decision in July 2023 does not hold the promise for a long-lasting solution due to the risks of being challenged and invalidated – yet again – at the Court of Justice of the EU. As the research community is calling for answers, the new proposal for a European Health Data Space regulation may hold a key to solving some of the existing issues. In this paper, we critically discuss the current rules and outline a possible way forward for transfers between public bodies. [ABSTRACT FROM AUTHOR]

Background: Since the beginning of the COVID-19 pandemic, a great number of papers have been published in the pediatric field. Objective: We aimed to assess research around the globe on COVID-19 in the pediatric field by bibliometric analysis, identifying publication trends and topic dissemination and showing the relevance of publishing authors, institutions, and countries. Methods: The Scopus database was comprehensively searched for all indexed documents published between January 1, 2020, and June 11, 2020, dealing with COVID-19 in the pediatric population (0-18 years). A machine learning bibliometric methodology was applied to evaluate the total number of papers and citations, journal and publication types, the top productive institutions and countries and their scientific collaboration, and core keywords. Results: A total of 2301 papers were retrieved, with an average of 4.8 citations per article. Of this, 1078 (46.9%) were research articles, 436 (18.9%) were reviews, 363 (15.8%) were letters, 186 (8.1%) were editorials, 7 (0.3%) were conference papers, and 231 (10%) were categorized as others. The studies were published in 969 differentjournals, headed by The Lancet. The retrieved papers were published by a total of 12,657 authors from 114 countries. The most productive countries were the United States, China, and Italy. The four main clusters of keywords were pathogenesis and clinical characteristics (keyword occurrences: n=2240), public health issues (n=352), mental health (n=82), and therapeutic aspects (n=70). Conclusions: In the pediatric field, a large number of articles were published within a limited period on COVID-19, testifying to the rush to spread new findings on the topic in a timely manner. The leading authors, countries, and institutions evidently belonged to the most impacted geographical areas. A focus on the pediatric population was often included in general articles, and pediatric research about COVID-19 mainly focused on the clinical features, public health issues, and psychological impact of the disease. [ABSTRACT FROM AUTHOR]

Inspired by pandemic-transformed instruction, this paper examines the digital accessibility of five tech tools used in information literacy sessions, specifically for students who use assistive technologies such as screen readers. The tools are Kahoot!, Mentimeter, Padlet, Jamboard, and Poll Everywhere. First, we provide an overview of the Americans with Disabilities Act (ADA) and digital accessibility definitions, descriptions of screen reading assistive technology, and the current use of tech tools in information literacy instruction for student engagement. Second, we examine accessibility testing assessments of the five tech tools selected for this paper. Our data show that the tools had severe, significant, and minor levels of digital accessibility problems, and while there were some shared issues, most problems were unique to the individual tools. We explore the implications of tech tools' unique environments as well as the importance of best practices and shared vocabularies. We also argue that digital accessibility benefits all users. Finally, we provide recommendations for teaching librarians to collaborate with campus offices to assess and advance the use of accessible tech tools in information literacy instruction, thereby enhancing an equitable learning environment for all students. [ABSTRACT FROM AUTHOR]

The emergence of the COVID-19 pandemic significantly changed the prenatal care experience, specifically regarding medical appointments and social opportunities. It is critical to capture this change through the narratives of pregnant people, particularly those of marginalized populations, whose voices may often be underrepresented in the literature. This mixed-methods paper summarizes the experiences of 40 pregnant Black/African American (AA) women during the COVID-19 pandemic. A cross-sectional, online survey was administered between 2020 and 2021 to assess prenatal health and the impacts of the COVID-19 pandemic on patients' pregnancy experience. Coping behaviors during the pandemic were self-reported using the COPE-IS. Univariate analyses were conducted. An additional analysis of participants (n = 4) was explored through a week-long qualitative exercise using a photo documentation procedure. Photo-Elicitation Interviews (PEI) were conducted to capture and center their pandemic pregnancy experiences. Sources of stress during the pandemic varied, with the most common being financial concerns (n = 19, 47.5%). Over half of the sample (n = 18, 54.5%) self-reported increases in their positive coping behaviors during the pandemic, such as communicating with friends and family, talking to healthcare providers, listening to music, and engaging in spiritual practices–such as prayer. The four PEI study participants reflected on the impacts of social distancing on their prenatal experience and mentioned hospital and provider-related weariness due to their race. The findings of this study suggest that during the COVID-19 pandemic, Black/AA pregnant women in Charlotte, NC used social support, mindfulness practices, self-advocacy, and health literacy to navigate challenges present during their prenatal health experience. This paper highlights the personal, social, and structural experiences of pregnant women during a public health crisis so that responsive and effective programs or policies can be planned in the future. [ABSTRACT FROM AUTHOR]

At the beginning of the COVID-19 pandemic, the entire world was waiting for a medical solution (for example, vaccines) in order to return to normality. Sanitary restrictions changed our consumption behaviors and feelings. Therefore, this paper analyzes the stochastic properties of consumer sentiment during the COVID-19 episode and the appearance of vaccines against the virus in December 2020 in the United States of America. This study adds a new dimension to the literature because it is the first research paper that uses advanced methodologies based on fractional integration and fractional cointegration analysis to understand the statistical properties of these time series and their behavior in the long term. The results using fractional integration methodologies exhibit a high degree of persistence, finding behavior of mean reversion during the pandemic episode. Therefore, the shock duration in consumer sentiment will be transitory, recovering to its previous trend in the short run. Focusing on the cointegrating part, we arrive at two main conclusions. First, an increase in total vaccination produces a positive reaction or impact on the behavior of consumers. On the other hand, an increase in new COVID-19 cases negatively affects the behavior of the consumer. [ABSTRACT FROM AUTHOR]

In this paper, we present findings from four separate studies using different data sources and methods to examine Chinese attitudes toward the United States amid the COVID-19 pandemic. The empirical results consistently indicate a marked and significant decline in Chinese attitudes toward the US between late 2019 and the end of 2022. Using a quasi-experimental design and granular survey data that exploit daily variations in public opinion, we offer additional evidence that the decline in Chinese attitudes toward the United States followed a distinct pattern not true for Chinese attitudes toward other countries. Specifically, the rise in Chinese unfavorability toward the United States closely corresponded to the heightened Chinese attention to the pandemic's progression in the United States. These results collectively suggest a causal effect of COVID-19, shedding light on how public health crises, international relations, and media jointly shape the increasing enmity between the two great powers. [ABSTRACT FROM AUTHOR]

The end of a pandemic is as much a political act as biological reality. It is over not simply when case counts or deaths are reduced to some objectively determined acceptable level but also when, and if, the public accepts the stories that politicians and health officials tell about it. This paper has three aims. First, to develop the concept of a pandemic illness narrative – a public narrative that makes the experience of an outbreak meaningful to a community of people and explains when it will be finished. Using the case of the United States, the paper then examines how American state organisations and public health officials tried to disseminate a version of the 'restitution illness narrative' to make sense out of the COVID-19 pandemic and explain how it would ultimately end. Lastly, the paper describes the factors that made this narrative ultimately implausible to the American public. As most Americans are now seemingly indifferent about the pandemic, it has ended in the United States without ever actually being narratively concluded. [ABSTRACT FROM AUTHOR]

Education is of vital importance for the development and growth of a society. Due to the advancement in educational technologies, there have been significant changes in teaching and learning processes. The digital divide has impacted significantly on teaching and learning, at present, especially after the COVID-19 pandemic. Considering this transformation, a scientometric analysis of the global literature on the digital divide in education was conducted in this study. The bibliographic data was gathered through the Scopus database for the period 2001-2021, and a total of 1865 publications were retrieved. VOSviewer and Gephi visualization software were used to analyze data and create network maps. The findings revealed that the average number of citations per paper (ACPP) for the study period is 15.51, and the relative citation impact (RCI) ranges from 0.21 in 2003 to 19.81 in 2021. Among published articles, the contribution of co-authors is greater than that of single authors. Furthermore, the United States was the leading country, with 613 publications on the digital divide. With 11 publications on the digital divide in education, Pick, J.B., from the School of Business, University of Redlands, United States, was the most prolific author. It is believed that these findings would benefit researchers worldwide, as scientometric mapping is an exciting method that provides a comprehensive overview of a scientific issue and insights for future research. [ABSTRACT FROM AUTHOR]

Purpose: The authors aim to analyze the inequality in accessing distance learning during COVID-19 school closures. Design/methodology/approach: The authors use the Household Pulse Survey, which is an effort by the United States (US) government to measure the well-being of American families during the COVID-19 pandemic. The authors employ a regression analysis to estimate the inequality in accessing distance learning by race and household income. Findings: Disadvantaged children from nonwhite and low-income families have much less access to distance learning, including less access to online classes, digital devices and the Internet. Schools are critical providers of the Internet and digital devices to children from disadvantaged households. Schools and parents devote more attention to these nonwhite children by spending extra time on their learning activities. Originality/value: This paper estimates the inequality in accessing distance learning during the COVID-19 crisis. Also, the authors analyze the responses of schools and parents toward this inequality. [ABSTRACT FROM AUTHOR]

In this paper, we propose a Susceptible–Infected–Removal (SIR) model with time fused coefficients. In particular, our proposed model discovers the underlying time homogeneity pattern for the SIR model's transmission rate and removal rate via Bayesian shrinkage priors. MCMC sampling for the proposed method is facilitated by the nimble package in R. Extensive simulation studies are carried out to examine the empirical performance of the proposed methods. We further apply the proposed methodology to analyze different levels of COVID-19 data in the United States. [ABSTRACT FROM AUTHOR]

The COVID-19 epidemic upended social and economic life in the United States. To reduce transmission, people altered their mobility and interpersonal contact, and state and local governments acted to induce social distancing through across-the-board policies. The epidemic and the subsequent social distancing response led to high unemployment and to efforts to reopen the economy using more-targeted virus mitigation policies. This paper makes five contributions to studying epidemic policy and mobility. First, we review COVID-19 research on mobility, labor markets, consumer behavior, and health. Second, we sketch a simple model of incentives and constraints facing individuals. Third, we propose a typology of government social distancing policies. Fourth, we review new databases measuring cellular mobility and contact. Fifth, we present regression evidence to help disentangle private versus policy-induced changes in mobility. During the shutdown phase, large declines in mobility occurred before states adopted stay-at-home (SAH) mandates and in states that never adopted them, suggesting that much of the decline was a private response to the risk of infection. Similarly, in the reopening phase mobility increased rapidly, mostly preceding official state reopenings, with policies explaining almost none of the increase. [ABSTRACT FROM AUTHOR]

This paper shows how class shaped service workers' experiences of administrative burdens during the COVID-19 pandemic. I use the pandemic and pandemic-related shutdowns as a pseudo natural experiment in which job loss was applied to a set of workers from different class backgrounds and with different class locations, workers who then turned to the state for assistance. Drawing on 46 interviews I conducted with service workers across the United States from May to October of 2020, I use Bourdieu's theory of the habitus to show how class background shaped the administrative burdens workers encountered. Workers' class origins left them with distinct approaches to bureaucracy that translated into disparate experiences of administrative burdens when workers sought unemployment insurance benefits. As a result, compared to workers from middle-class backgrounds, workers from working-class backgrounds more often experienced housing difficulties, dangerous work, and challenges to their sense of integrity. [ABSTRACT FROM AUTHOR]

Background: The coronavirus (COVID-19) pandemic presented a significant stressor on the public health system in the United States. While we know the immediate effects of the pandemic on public health programming, no literature has examined the resultant long-term impact on programmatic capacity for sustainability. This paper aims to identify the impact that the COVID-19 pandemic had on state tobacco control program's capacity for sustainability. Methods: From December 2018 to January 2022, we conducted 46 technical assistance calls with tobacco control program employees from 11 states. Calls were audio recorded and professionally transcribed. We analyzed calls (n = 20) that took place during the COVID-19 pandemic. Thematic analysis focused on the impact the COVID-19 pandemic had on tobacco control program's capacity for sustainability. Results: We identified six domains of sustainability that were impacted by COVID-19: (1) funding stability; (2) organizational capacity; (3) partnerships; (4) communication; (5) strategic planning; and (6) program adaptation. Conclusions: Our study is the first to identify the impact of the pandemic on capacity for sustainability of tobacco control programs. Having an understanding of COVID-19's influence on these sustainability domains could help with future public health programming during significant public health events and emergency preparedness. ClinicalTrials.gov Identifier: NCT03598114. Registration date: Retrospectively registered 02-07-2018. [ABSTRACT FROM AUTHOR]

This study employs the panel vector autoregressive (PVAR) model to examine the spillover effect of US unconventional monetary policy on inflation and non-inflation targeting emerging markets post credit crunch and during COVID-19 from 2000Q1 to 2020Q4. Unlike other analyses, this paper adds to the existing body of knowledge by employing a dummy variable to represent the United States' quantitative easing. Other included control variables are equity prices, the federal reserve rate, the exchange rate, central bank assets and the short-term interest rate. This paper estimated two-panel VARs, Model one and Model two, for inflation and non-inflation targeting emerging markets, respectively. Model one consists of eight inflation-targeting markets, and Model two consists of four non-inflation-targeting countries. Other included control variables are equity prices, the federal reserve rate, the nominal effective exchange rate, and the central bank policy rate. According to the empirical results, the US unconventional monetary policy induces a surge in the exchange rate and a decrease in the central bank policy rate for both inflation and non-inflation targeting emerging markets. However, there was no significant impact on the equity prices. The empirical results are statistically significant, robust, and consistent with previous studies except for the response of equity prices. Unconventional monetary policy is effective in steering macroeconomic variables in developed economies. The monetary policymakers in emerging markets must also use the currency reserve to stabilise the macroeconomic variables in response to US unconventional monetary policy shocks. [ABSTRACT FROM AUTHOR]

External shocks, like the climate catastrophe or the COVID-19 pandemic, as well as intrinsic fallacies like the securitization of bad debt leading up to the financial crisis in 2008, point to the need for updating our monetary and financial systems. Ensuring their adequacy and resilience is an important factor for sustainability at large. This paper examines the definitions of "money" and "currency" in financial legislation as a foundational factor in achieving systemic resilience by allowing or hampering monetary innovation and diversity. From the unencumbered vantage point that the practice of complementary currencies offers, definitions of the terms "money" and "currency" are here traced through the laws and regulations of the United States of America, from the beginnings of modern banking to the recent rulings on crypto-currencies. They are both found to be used and defined in contradictory ways that are inapt even in regard to conventional modern banking practices, let alone when applied to novelty in payment, issuance and valuation. Consequently, this paper argues that basic legal definitions need to be reviewed and consolidated to enable the innovation and diversification in monetary systems needed for long term macro-economic stability. With this in mind, a terminology that is consistent with monetary practice--current, past and future--as well as the procedural difficulties of reforming laws and regulations is proposed. [ABSTRACT FROM AUTHOR]

The coronavirus pandemic led to a severe economic shock in the United States. This paper uses a unique survey data collected early on in the pandemic to measure economic deprivation among individuals. The Federal Reserve Board fielded a Survey of Household Economics and Decision-making (SHED) in April 2020. This survey is used to compile data on four indicators of economic deprivation, namely: i) Overall financial condition, ii) Loss of employment, iii) Reduction in income and iv) Inability to pay bills in full. Data on these indicators is compiled for each individual and is used in a novel way to construct a set of multidimensional deprivation indices. These indices measure the overlap of deprivations experienced by an individual. Results show that almost 25 percent of the respondents faced hardships in at least two of the four indicators. More than 13 percent of adults reported their inability to pay monthly bills and struggled to make ends meet financially. One in four respondents had lower income compared to income from previous month. The economic shock affected Hispanics in a more profound way. More than 37 percent Hispanics reported hardship in two or more indicators and 8 percent reported hardship in all four indicators. Higher proportion of young adults and those without a college degree suffered multiple hardships. The paper highlights the plight of Americans during the early months of the economic crisis set in motion amid the coronavirus pandemic and sheds light on how economic disparities deepened along racial/ethnic lines. [ABSTRACT FROM AUTHOR]

Introduction: The United States has been unsuccessful in containing the rapid spread of COVID‐19. The complex epidemiology of the disease and the fragmented response to it has resulted in thousands of ways in which spread has occurred, creating a situation where each community needs to create its own local, context‐specific learning model while remaining compliant to county or state mandates. Methods: In this paper, we demonstrate how cross sector collaborations can use the Cynefin Framework, a tool for decision‐making in complex systems, to guide community response to the COVID‐19 pandemic. Results: We explore circumstances under which communities can inhabit each of the four domains of systems complexity represented in the Cynefin framework: simple, complicated, chaotic, and complex, and describe the decision‐making process in each domain that balances health, economic, and social well‐being. Conclusion: This paper serves as a call to action for the creation of community learning systems to improve community resilience and capacity to make better‐informed decisions to address complex public health problems during the pandemic and beyond. [ABSTRACT FROM AUTHOR]

This paper will identify existing models of professional regulatory reform that could ensure a single licensing process that results in multi-jurisdictional licensure. The paper will also distinguish models or ad hoc arrangements that fail to achieve such a result. This is a timely topic in Canada because its professional regulatory framework is being examined for modernization. The Canadian professional regulatory framework for health care providers is a legacy of the Constitution of 1867. It can be characterized as a Federation of 13 different jurisdictional systems with each province or territory having exclusive jurisdiction over regulation of its health professionals. This results in differing entry to practice requirements, standards of practice, classes or categories of registration and transfer criteria for eligibility from other provinces. The United States nursing state board regulators, the Australian Commonwealth, and their state governments have moved from their original regulatory frameworks to modern ones. Their models are more supportive of mobility, cross-border virtual care, education, and health provider professional development and well-being. Aside from recent discussions in the 4 Canadian Atlantic provinces, there has been little will, effort, or advancement to modernize the regulatory framework in Canada to support multi-jurisdictional licensure. This paper aims to briefly describe 6 existing models that support multijurisdictional licensure. In the fall of 2022, the 4 Atlantic Premiers (akin to state Governors in the US) asked each of their medical regulatory authority (akin to State Medical Boards) to develop a licensing system such that physicians could practice in all 4 Atlantic provinces without the need to acquire multiple licenses. Two models will be discussed that meet this recently stated objective of the Atlantic Premiers while the others do not. [ABSTRACT FROM AUTHOR]

In this paper, we test the validity of the employment hysteresis hypothesis. For this purpose, we use daily employment data at the national and state levels in the United States from January 8, 2020, to May 30, 2020. We apply the modified version of the Kapetanios-Shin unit root test, along with finite-sample critical values. We find that the employment hysteresis hypothesis is valid in the United States during the COVID-19 era. The validity of the findings does not change when data at the national and state levels are used. The evidence is also valid when the employment levels for all firms and small firms are considered. The results are also robust to employment levels for workers at different income levels and employment in five different sectors. [ABSTRACT FROM AUTHOR]

This paper examines whether school COVID-19 policies influenced enrollment differently by student age and race/ethnicity. Unlike much prior research, we i) analyze enrollments for virtually the entire U.S. public school population for both the 2020- 2021 and 2021-2022 school years, ii) compare enrollment trends within districts in order to isolate subgroup heterogeneity from district characteristics, and iii) account for district selection into preferred learning modes. Analyzing data on over 9,000 districts that serve more than 90% of public school students in the United States, we find enrollment responses to COVID policies differed notably. We find that White enrollments declined more than Black, Hispanic, and Asian enrollments in districts that started the 2020-2021 school year virtually, but in districts that started in-person the reverse was true: Non-White enrollments declined more than White enrollments. Moreover, Black, Hispanic, and Asian families responded more than White families to higher COVID-19 death rates in the months preceding the start of the 2021 school year. In 2021-2022, enrollment differences by the previous year's learning mode persisted. Racial/ethnic differences did not vary by whether the district required masking in classrooms. These findings are consistent with the greater risk faced by communities of color during the pandemic and demonstrate an additional source of disparate impact from COVID policies. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has accelerated acceptance of learning from other countries, especially for high-income countries to learn from low- and middle-income countries, a practice known as global learning. COVID-19’s rapid disease transmission underscored how connected the globe is as well as revealed stark health inequities which facilitated looking outside of one’s borders for solutions. The Global Learning for Health Equity (GL4HE) Network, supported by Robert Wood Johnson Foundation, held a 3-part webinar series in December 2021 to understand the current state of global learning and explore how global learning can advance health equity in the post-COVID-19 era. This paper reflects on these cutting-edge discussions about the current state of global learning, drawing upon the highlights, perspectives, and conclusions that emerged from these webinars. The paper also comments on best practices for global learning, including adapting for context, addressing biases, funding considerations, ensuring bidirectional partnerships, community engagement, and adopting a multidisciplinary approach. [ABSTRACT FROM AUTHOR]

The 2021 American Rescue Plan included the temporary expansion of the Child Tax Credit (CTC)--the largest individual income tax credit program in the United States--for most families with children. In the context of the COVID-19 pandemic, how did the public perceive this social policy benefit for families, especially in relation to other traditional social programs? By focusing on the CTC, an understudied policy area, and presenting original survey data, this paper first shows that, while the majority of respondents favored the CTC, levels of support for these benefits were lower than support for other social programs. Second, the paper suggests that, compared to older people and people with disabilities, Americans view families as part of the "undeserving" population. Third, by presenting panel data, we show that there is no change in levels of CTC support even among recipients of these benefits. Overall, these findings shed light on important challenges to the development and implementation of family policy in the USA, as well as the possibility of recalibrating the US liberal welfare state. [ABSTRACT FROM AUTHOR]

When the COVID-19 pandemic forced P-12 schools to temporarily transition to online learning, it exacerbated the already deep-seated educational inequities in communities across the United States. Employing a grounded theory approach, this paper explores how educators and community stakeholders created a free full-time volunteer-led K-2 learning pod for historically underserved students. The authors discuss the opportunities and challenges experienced in the pod's implementation, as well as lessons for these types of university-community partnerships. The findings of the study reveal the potential of equity-focused collaborations that accept risk, occupy uncomfortable liminal spaces, and leverage informal networks and relationships to build trusting and authentic community partnerships. The paper concludes with a call to reimagine the parameters of university-community engagement in times of crisis. [ABSTRACT FROM AUTHOR]

China has been contributing to new approaches to global governance. The Health Silk Road (HSR), a significant component of the Belt and Road Initiative (BRI), was proposed by China in 2016. This paper claims that HSR is a new institution introduced alongside the existing WHO-led multilateral health system, and its relationship with the existing system can be described as layering. Having explored the new development of HSR during COVID-19, this paper further argues that while HSR has its unique strength in making contributions to global health governance and economic recovery, it faces a prominent issue of securitisation in the context of China-U.S. strategic competition, suspicion of the quality of medical products and sectoral fragmentation. [ABSTRACT FROM AUTHOR]

This paper utilizes narrative inquiry to examine the effect of COVID-19 on political resistance, focusing on education as a key site. Based on survey and interview data the paper considers parents' perspectives about the impacts of COVID-19 and racial inequalities in their children's schooling. Two narrative types are constructed and analyzed: consensus narratives and parenting narratives that refute an overarching, manufactured political narrative in the United States of "divisiveness" about race and education, while also identifying the layers and complexities of individual parents' everyday lives raising and educating children. [ABSTRACT FROM AUTHOR]

The pathogenic bacteria Neisseria meningitidis, which causes invasive meningococcal disease (IMD), predominantly colonizes humans asymptomatically; however, invasive disease occurs in a small proportion of the population. Here, we explore the seasonality of IMD and develop and validate a suite of models for simulating and forecasting disease outcomes in the United States. We combine the models into multi-model ensembles (MME) based on the past performance of the individual models, as well as a naive equally weighted aggregation, and compare the retrospective forecast performance over a six-month forecast horizon. Deployment of the complete vaccination regimen, introduced in 2011, coincided with a change in the periodicity of IMD, suggesting altered transmission dynamics. We found that a model forced with the period obtained by local power wavelet decomposition best fit and forecast observations. In addition, the MME performed the best across the entire study period. Finally, our study included US-level data until 2022, allowing study of a possible IMD rebound after relaxation of non-pharmaceutical interventions imposed in response to the COVID-19 pandemic; however, no evidence of a rebound was found. Our findings demonstrate the ability of process-based models to retrospectively forecast IMD and provide a first analysis of the seasonality of IMD before and after the complete vaccination regimen. Author summary: This paper presents a time series analysis of Invasive Meningococcal Disease caused by the pathogenic bacteria Neisseria meningitidis as well as the development and validation of a suite of models for simulating and forecasting disease outcomes in the United States. We present the models and their performance and construct a multi-model ensemble (MME) forecast that combines individual models based on past forecast performance, as well as an equally weighted aggregation. Deployment of a complete vaccination regimen, introduced for adolescents in 2011, coincided with a change in the periodicity of IMD, suggesting altered transmission dynamics. We found that a model forced with the period obtained by local power wavelet decomposition best fits and forecasts observations. In addition, the MME forecasts performed the best across the entire study period. Finally, our study included US-level data until 2022, allowing study of a possible IMD rebound after the relaxation of non-pharmaceutical interventions imposed in response to the COVID-19 pandemic; however, no evidence of a rebound was found. Our findings demonstrate the ability of process-based models to retrospectively forecast IMD and provide a first analysis of the seasonality of IMD before and after the complete vaccination regimen. [ABSTRACT FROM AUTHOR]

This paper proposes a dynamic infectious disease model for COVID-19 daily counts data and estimate the model using the Langevinized EnKF algorithm, which is scalable for large-scale spatio-temporal data, converges to the right filtering distribution, and is thus suitable for performing statistical inference and quantifying uncertainty for the underlying dynamic system. Under the framework of the proposed dynamic infectious disease model, we tested the impact of temperature, precipitation, state emergency order and stay home order on the spread of COVID-19 based on the United States county-wise daily counts data. Our numerical results show that warm and humid weather can significantly slow the spread of COVID-19, and the state emergency and stay home orders also help to slow it. This finding provides guidance and support to future policies or acts for mitigating the community transmission and lowering the mortality rate of COVID-19. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has been particularly deadly for residents of nursing homes and other long-term care facilities. This paper analyzes COVID-19 deaths at nursing homes during the first wave of the pandemic in the United States during the spring and early summer 2020. By combining data on facility-level COVID-19 deaths during this period with data on the neighborhoods where nursing home staff reside for a sample of eighteen states, this paper finds that staff neighborhood characteristics were a large and significant predictor of COVID-19 nursing home deaths. Even after controlling for the county where a facility is located, one standard deviation increases in average staff neighborhood (Census tract) population density, public transportation use, and non-white share were associated with 1.3 (p <.001), 1.4 (p <.001), and 0.9 (p <.001) additional deaths per 100 beds, respectively. These effects are larger than all facility management or quality variables, and larger than the effect of the nursing home's own neighborhood characteristics. These results suggest COVID-19 outbreaks in staff communities can have large consequences for the facilities where they work, even in highly-rated facilities, and that disparities in nursing home outbreaks may be related to differences in the types of neighborhoods nursing home staff live in. [ABSTRACT FROM AUTHOR]

The differential impact of the COVID‐19 pandemic on communities of color in the United States along with the civil unrest taking place in 2020 in response to the killing of unarmed Black men and women by the police have increased awareness of the structural racism pervading US society. These developments have reraised the issue of reparations for Black Americans, usually proposed in the context of providing financial compensation for the injustices of slavery to the descendants of those who were enslaved. This paper will discuss the systematic racial inequality and structural racism in US society that have significantly disadvantaged racial and ethnic minorities while giving advantages to white Americans, which most recently have resulted in significantly higher mortality and morbidity among Black, Hispanic, and Native Americans during the pandemic. The paper will conceptualize reparations within the context of theories of reparative justice. It will also consider whether reparations are owed, and if so, by whom, to whom, and in what form. The final section will offer a proposal for collective reparations to the Black community and other people of color. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to address assumptions about the prevalence of change in human resource management (HRM) and organizational change literature, providing evidence from employee perceptions across three countries. The results indicate change was commonplace even before the pandemic disruptions of 2020. Design/methodology/approach: Given this study's exploratory, employee perspective, a cross-sectional self-report survey was used. Three survey panel samples were collected in 2017: US (n = 718), Australia (n = 501) and New Zealand (n = 516). Analysis of variance was used to test whether the prevalence of change differed significantly between countries or specific groups of employees. An analysis of comments on change types and emotional response provides further insights. Findings: The paper provides evidence of the ubiquity of change: 73% of employees are experiencing change at work and 42% perceived it as moderate to massive, with little variation between countries. Employees commonly experience more than one change, with those experiencing large amounts of change reporting predominantly negative emotional impacts. Research limitations/implications: The research provides a snapshot across three countries during a prosperous and relatively stable period, providing a point of comparison for the turbulent times we have faced in 2020" as the publication date will be 2021 the current text may not work as well. Since change can be arduous, the authors recommend that HRM researchers consider change prevalence as a contextual factor, and practitioners heed employee reactions to change, particularly during periods of significant change. Originality/value: In providing foundational evidence of change ubiquity in contemporary workplaces, this paper enables more accurate discussions regarding change. [ABSTRACT FROM AUTHOR]

A substantial fraction of k-12 schools and childcare facilities in the United States closed their in-person operations during the COVID-19 pandemic. These closures may have altered the labor supply decisions of parents of affected children due to a need to be at home and take care of their children during the school day. In this paper, we examine the impact of school and childcare facility closures on parental labor market outcomes. We test whether COVID-19 facilities closures have a disproportionate impact on parents of children under 18 years old. Our results show that both women's and men's work lives were affected by school closures, with both groups seeing a reduction in the likelihood of working, work hours and the likelihood of working full-time. We also find that closures had a corresponding negative effect on the earnings of fathers of children under 18 years old, but not on mothers. These effects are concentrated among parents without a college degree, parents working in occupations that do not lend themselves to telework, and parents without other family members living at home, suggesting that such individuals had a more difficult time adjusting their work lives to school and childcare facility closures. [ABSTRACT FROM AUTHOR]

We examine the impact of government interventions on the spread of COVID-19 and consumer spending. We do this by first estimating models of COVID-19 spread, consumer spending, and social distancing in the United States during the early stages of the COVID-19 pandemic. Social distancing has a large effect on reducing COVID-19 spread and is responsive to national and local case numbers. Nonmask government interventions reduce COVID-19 spread, whereas the effectiveness of mask mandates is much smaller and statistically insignificant. Mask mandates tend to increase social distancing, as do nonmask governmental restrictions as a whole. Social distancing hurts spending in the absence of a mask mandate but has a negligible effect on spending if there is a mask mandate. Mask mandates have a direct effect of increasing spending in counties with high levels of social distancing while reducing spending in counties with low levels of social distancing. We use these three estimated models to calculate the effect of mask mandates and other governmental interventions on COVID-19 cases, deaths, and consumer spending. Implemented mask mandates decreased COVID-19 cases by a statistically insignificant 774,000 cases, saving 28,000 lives, over a four-month period, but led to $76B–$155B of additional consumer spending. Other nonmask governmental interventions that were implemented reduced the number of COVID-19 cases by 34M, saving 1,230,000 lives, while reducing consumer spending by approximately $470B–$703B over our 4-month period of the study. Thus, these restrictions were cost effective as long as one values each saved life at $387,000–$608,000 or more. This paper was accepted by Matt Shum, marketing. Supplemental Material: The data is available at https://doi.org/10.1287/mnsc.2023.4853. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has had a marked impact on healthcare businesses since 2020. Healthcare organizations suffered greatly from financial stress coupled with disruptions in national and global supply chains. Healthcare workers and patients alike experienced significant downturns in their physical and mental health. Large government and legislative reforms were enacted to combat the progression of the pandemic. This paper explores these areas in detail in order to provide a clearer understanding of the successes and inadequacies that exist within the United States healthcare system as illuminated by the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

After three years of global rampage, the COVID-19 epidemic, the most serious infectious disease to occur worldwide since the 1918 influenza pandemic, is nearing its end. From the global experience, medical control and social control are the two main dimensions in the prevention and control of COVID-19. From the perspective of "two types of control", namely medical control and social control, this paper finds that the political system, economic structure, and cultural values of the United States greatly limit the government's ability to impose social control, forcing it to adopt medical control to fight the virus in a single dimension. In contrast, China's political system, economic structure, and cultural values allow its government to adopt stringent, extensive, and frequent social control, as well as medical control to fight the virus. This approach departs from the traditional pathway of fighting the epidemic, i.e., "infection–treatment–immunization", thereby outpacing the evolution of the virus and controlling its spread more rapidly. This finding helps explain why the Chinese government adopted a strict "zeroing" and "dynamic zeroing" policy during the first three years, at the cost of enormous economic, social, and even political legitimacy. It was not until late 2022, when the Omicron variant with the waning virulence became prevalent, that China chose to "coexist" with the virus, thus avoiding a massive epidemic-related death. While the United States adopted a pulsed-style strategy at the beginning of the epidemic, i.e., "relaxation–suppression–relaxation–suppression", and began to "coexist" with the virus in just one year, resulting in a large number of excess deaths associated with the epidemic. The study contributes to explaining the difference in the interplay between public health priorities and COVID-19 response strategies in China and the United States, based on the specific public health context and the perspective of "medical control" and "social control". [ABSTRACT FROM AUTHOR]

The worldwide outbreak of the coronavirus was first identified in 2019 in Wuhan, China. Since then, the disease has spread worldwide. As it is currently spreading in the United States, policy makers, public health officials and citizens are racing to understand the impact of this virus on the United States healthcare system. They fear a rapid influx of patients overwhelming the healthcare system and leading to unnecessary fatalities. Most countries and states in America have introduced mitigation strategies, such as using social distancing to decrease the rate of newly infected people. This is what is usually meant by flattening the curve. In this paper, we use queueing theoretic methods to analyze the time evolution of the number of people hospitalized due to the coronavirus. Given that the rate of new infections varies over time as the pandemic evolves, we model the number of coronavirus patients as a dynamical system based on the theory of infinite server queues with time inhomogeneous Poisson arrival rates. With this model we are able to quantify how flattening the curve affects the peak demand for hospital resources. This allows us to characterize how aggressive societal policy needs to be to avoid overwhelming the capacity of healthcare system. We also demonstrate how curve flattening impacts the elapsed lag between the times of the peak rate of hospitalizations and the peak demand for the hospital resources. Finally, we present empirical evidence from Italy and the United States that supports the insights from our model analysis. [ABSTRACT FROM AUTHOR]

In response to the rapidly evolving coronavirus disease 2019 (COVID-19) pandemic, the All of Us Research Program longitudinal cohort study developed the COVID-19 Participant Experience (COPE) survey to better understand the pandemic experiences and health impacts of COVID-19 on diverse populations within the United States. Six survey versions were deployed between May 2020 and March 2021, covering mental health, loneliness, activity, substance use, and discrimination, as well as COVID-19 symptoms, testing, treatment, and vaccination. A total of 104,910 All of Us Research Program participants, of whom over 73% were from communities traditionally underrepresented in biomedical research, completed 275,201 surveys; 9,693 completed all 6 surveys. Response rates varied widely among demographic groups and were lower among participants from certain racial and ethnic minority populations, participants with low income or educational attainment, and participants with a Spanish language preference. Survey modifications improved participant response rates between the first and last surveys (13.9% to 16.1%, P  < 0.001). This paper describes a data set with longitudinal COVID-19 survey data in a large, diverse population that will enable researchers to address important questions related to the pandemic, a data set that is of additional scientific value when combined with the program's other data sources. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has had a devastating impact on the world at large with over 750 million cases and almost 7 million deaths reported thus far. Of those, over 100 million cases and 1 million deaths have occurred in the United States of America (USA). The mental health of the general population has been impacted by several aspects of the pandemic including lockdowns, media sensationalism, social isolation, and spread of the disease. In this paper, we examine the associations that social isolation and COVID-19 infection and related death had with the prevalence of anxiety and depression in the general population of the USA in a state-by-state multiple time-series analysis. Vector Error Correction Models are estimated and we subsequently evaluated the coefficients of the estimated models and calculated their impulse response functions for further interpretation. We found that COVID-19 incidence was positively associated with anxiety across the studied period for a majority of states. Variables related to social isolation had a varied effect depending on the state being considered. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic presented enormous data challenges in the United States. Policy makers, epidemiological modelers, and health researchers all require up-to-date data on the pandemic and relevant public behavior, ideally at fine spatial and temporal resolution. The COVIDcast API is our attempt to fill this need: Operational since April 2020, it provides open access to both traditional public health surveillance signals (cases, deaths, and hospitalizations) and many auxiliary indicators of COVID-19 activity, such as signals extracted from deidentified medical claims data, massive online surveys, cell phone mobility data, and internet search trends. These are available at a fine geographic resolution (mostly at the county level) and are updated daily. The COVIDcast API also tracks all revisions to historical data, allowing modelers to account for the frequent revisions and backfill that are common for many public health data sources. All of the data are available in a common format through the API and accompanying R and Python software packages. This paper describes the data sources and signals, and provides examples demonstrating that the auxiliary signals in the COVIDcast API present information relevant to tracking COVID activity, augmenting traditional public health reporting and empowering research and decision-making. [ABSTRACT FROM AUTHOR]

The COVID-19 global pandemic has significantly impacted every aspect of life all over the world. The United States is reported to have suffered more than 20% of the global casualties from this pandemic. It is imperative to investigate the growth dynamics of the disease in the US based on varying geographical and governmental factors that best manifest itself in each State of the Country. This paper utilizes a hybrid machine learning and continuum deformation-based approach for analyzing the stability and growth rate of the pandemic. To this end, principal stress values of the pandemic continuum body are obtained using Mohr's Circle method and overlapping, moving windows of data are analysed successively. This helps in finding the correlations between the growth rate and Governments' action/Public's reaction. Government actions include "state of emergency", "shelter at place", and "phase declarations". We also consider the vaccination rate milestones, which shows us the coordinated Governments' action/Public's reaction. Finally, a number of recommendations are made to the Governments and people for better management of future pandemics. [ABSTRACT FROM AUTHOR]

This paper examines the spatial and temporal distribution of all COVID-19 cases from January to June 2020 against the underlying distribution of population in the United States. It is found that, as time passes, COVID-19 cases become a power law with cutoff, resembling the underlying spatial distribution of populations. The power law implies that many states and counties have a low number of cases, while only a few highly populated states and counties have a high number of cases. To further differentiate patterns between the underlying populations and COVID-19 cases, we derived their inherent hierarchy or spatial heterogeneity characterized by the ht-index. We found that the ht-index of COVID-19 cases persistently approaches that of the populations; that is, 5 and 7 at the state and county levels, respectively. Mapping the ht-index of COVID-19 cases against that of populations shows that the pandemic is largely shaped by the underlying population with the R-square value between infection and population up to 0.82. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic is a global health concern that has spread around the globe. Machine Learning is promising in the fight against the COVID-19 pandemic. Machine learning and artificial intelligence have been employed by various healthcare providers, scientists, and clinicians in medical industries in the fight against COVID-19 disease. In this paper, we discuss the impact of the Covid-19 pandemic on alcohol consumption habit changes among healthcare workers in the United States during the first wave of the Covid-19 pandemic. We utilize multiple supervised and unsupervised machine learning methods and models such as decision trees, logistic regression, support vector machines, multilayer perceptron, XGBoost, CatBoost, LightGBM, AdaBoost, Chi-Squared Test, mutual information, KModes clustering and the synthetic minority oversampling technique on a mental health survey data obtained from the University of Michigan Inter-University Consortium for Political and Social Research to investigate the links between COVID-19-related deleterious effects and changes in alcohol consumption habits among healthcare workers. Through the interpretation of the supervised and unsupervised methods, we have concluded that healthcare workers whose children stayed home during the first wave in the US consumed more alcohol. We also found that the work schedule changes due to the Covid-19 pandemic led to a change in alcohol use habits. Changes in food consumption, age, gender, geographical characteristics, changes in sleep habits, the amount of news consumption, and screen time are also important predictors of an increase in alcohol use among healthcare workers in the United States. [ABSTRACT FROM AUTHOR]

Scant research exists on COVID-19 vaccine hesitancy among law enforcement officers, hindering health messaging development for officers and, by extension, the communities they serve. This paper's goal was to address this gap by providing the necessary data to better under hesitancy to guide training and policy interventions for officers. The objective was to conduct the first nationally representative survey of officers on COVID-19 vaccine hesitancy and its correlates. We collected data from February 2021 to March 2022 on officer COVID-19 vaccine hesitancy and examined their responses in terms of sociodemographic factors, health status, and job characteristics. We found that 40% of officers were COVID-19 vaccine hesitant. We found that officers with higher education, older officers, officers with more law enforcement experience, officers who received recent health checkups, and commanders (compared to line officers) were less likely to be COVID-19 vaccine hesitant. Critically, officers working in law enforcement agencies that provided masks for COVID-19 protection were less likely to be COVID-19 vaccine hesitant (compared to agencies not providing masks). Ongoing research is needed to understand how evolving attitudes and barriers toward vaccination change over time for officers and to test messaging to better align officers with health guidelines. [ABSTRACT FROM AUTHOR]