ABSTRACT
Among the more fraught election years in recent history, 2020 transpired amid four interlaced crises: the COVID-19 pandemic, an economic recession and uneven recovery, a racial reckoning, and a crisis of democratic legitimacy that culminated in the riot at the Capitol on January 6, 2021, and widespread belief among Republicans that the election had been stolen from Donald Trump. Democracy amid Crises explains how these forces and the media messaging through which they were filtered shaped the election and post-election dialogue, as well as voter perceptions of both, with worrisome potential consequences for democracy. The book spotlights not one but several electorates, each embedded in a distinctive informational environment. The four crises affected these electorates differently, partly because the unique constellations of media in which they were advertently and inadvertently enmeshed contained dissimilar messages from the campaigns and other sources of influence. Awash in distinctive message streams, the various electorates adopted divergent perspectives on the crises, candidates, and state of the country. As a result, understanding voting behavior and attitudes about the events that followed requires an analysis of both the distinctive electorates and the informational environments that enveloped them. Importantly, our findings raise fundamental questions about the nation's future, occasioned by the contest over whether the 2020 presidential election was fairly and freely decided and by worrisome responses to the reality that the country's citizenry is becoming more multiracial, multiethnic, and, on matters religious, agnostic. © Oxford University Press 2023.
ABSTRACT
The Coronavirus disease (COVID-19) pandemic has caused social and economic crisis to the globe. Contact tracing is a proven effective way of containing the spread of COVID-19. In this paper, we propose CAPER, a Cellular-Assisted deeP lEaRning based COVID-19 contact tracing system based on cellular network channel state information (CSI) measurements. CAPER leverages a deep neural network based feature extractor to map cellular CSI to a neural network feature space, within which the Euclidean distance between points strongly correlates with the proximity of devices. By doing so, we maintain user privacy by ensuring that CAPER never propagates one client's CSI data to its server or to other clients. We implement a CAPER prototype using a software defined radio platform, and evaluate its performance in a variety of real-world situations including indoor and outdoor scenarios, crowded and sparse environments, and with differing data traffic patterns and cellular configurations in common use. Microbenchmarks show that our neural network model runs in 12.1 microseconds on the OnePlus 8 smartphone. End-to-end results demonstrate that CAPER achieves an overall accuracy of 93.39%, outperforming the accuracy of BLE based approach by 14.96%, in determining whether two devices are within six feet or not, and only misses 1.21% of close contacts. CAPER is also robust to environment dynamics, maintaining an accuracy of 92.35% after running for ten days. © 2022 Owner/Author.
ABSTRACT
Methods: Purposive snowballing sampling recruitment strategy was used to recruit eight leaders who worked in a clinical healthcare setting, and who have trained in and currently maintained a mindfulness practice. Data collection included remote, semi-structured interviews, a demographic questionnaire, and a follow-up conversation. Thematic analysis was conducted deductively using Hougaard and Carter's (2018) mindfulness-selflessness-compassion (MSC) framework and then inductively. Results: Themes were identified that supported each MSC category. Listening was an overarching theme. Three progressive patterns were associated with the MSC categories: (a) mindfulness as an entry point, (b) compassion as an entry point, and (c) progression toward collaborative leadership. Three outlier themes emerged: (a) spirituality as a foundation, (b) empathy as catalyst for emotional resonance, and (c) mindfulness as a way of life. Background: This is a time of rapid change and uncertainty in healthcare in the United States. The emergence of the COVID-19 pandemic has intensified ambiguity in healthcare and amplified the conditions, needs, and potential value in integrative health approaches. Mindfulness meditation is one integrative health approach that has been linked to more resilient leadership. Mindful healthcare leaders may more easily navigate the volatile and complex environment of the pandemic and facilitate resilience and innovation during the crisis. This descriptive qualitative analysis investigated how healthcare leaders trained in mindfulness describe their experience implementing their training in the workplace during the COVID-19 pandemic. Conclusion: The MSC categories were a sound structure for future research and practice. However, an approach that includes the interrelationships among the categories, including the outlier themes identified here, would better represent the phenomenon of mindfulness and create a more authentic system-based framework. Contributions of this study included a) an evidence-based definition of being present, b) a two-part process for developing emotional selfregulation, and c) an assessment of dynamics among mindfulness elements describing a system-based, holistic phenomenon of mindfulness.
ABSTRACT
Our understanding of COVID-19 pandemic epidemiology has many gaps, with many challenges arising on a global scale. This paper looks at the problem at a smaller geo-graphical scale, the extent of the campus of a large organization. Equipped with an asymptomatic testing program and rough location data from the campus wireless network, we make the case that epidemiological models may be informed from this new source of data, which offers fidelity at the temporal resolution of seconds and spatial resolution of a Wi-Fi cell size, in particular for the tasks of pinpointing clusters of cases and contexts of infection transmission. We sketch the design of a system that fuses the two foregoing information streams and explain how the result can be incorporated into standard epidemiological models of communicable disease, both for better parameter estimation in elementary models, as well as for providing spatial inputs into more sophisticated models. We conclude with logistical and privacy considerations we have encountered in an associated ongoing study, to inform similar efforts at other organizations.