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COVID-19 vaccination hesitancy has become a major concern around the world. Recent reports have also highlighted COVID-19 vaccination hesitancy in healthcare workers. Despite media reports and scientific publications, little is known about the extent and predictors of COVID-19 vaccination refusal among nurses. Thus, the purpose of this study was to assess COVID-19 vaccine refusal rates among nurses globally and to explore the reasons for refusal and factors associated with the uptake of the vaccines. A scoping review of the published literature was conducted, and a final pool of 51 studies (n = 41,098 nurses) from 36 countries was included in this review. The overall pooled prevalence rate of COVID-19 vaccine refusal among 41,098 nurses worldwide was 20.7% (95% CI = 16.5–27%). The rates of vaccination refusal were higher from March 2020–December 2020 compared to the rates from January 2021–May 2021. The major reasons for COVID-19 vaccine refusal were concerns about vaccine safety, side effects, and efficacy; misinformation and lack of knowledge; and mistrust in experts, authorities, or pharmaceutical companies. The major factors associated with acceptance of the vaccines were: male sex, older age, and flu vaccination history. Evidence-based strategies should be implemented in healthcare systems worldwide to increase the uptake of COVID-19 vaccines among nurses to ensure their safety and the safety of their patients and community members.

The combination of increasing component power consumption, a desire for denser systems, and the required performance growth in the face of technology-scaling issues are posing enormous challenges for powering and cooling of server systems. The challenges are directly linked to the peak power consumption of servers.Our solution, Power Shifting, reduces the peak power consumption of servers minimizing the impact on performance. We reduce peak power consumption by using workload-guided dynamic allocation of power among components incorporating real-time performance feedback, activity-related power estimation techniques, and performance-sensitive activity-regulation mechanisms to enforce power budgets.We apply our techniques to a computer system with a single processor and memory. Power shifting adds a system power manager with a dynamic, global view of the system's power consumption to continuously re-budget the available power amongst the two components. Our contributions include: !Demonstration of the greater effectiveness of dynamic power allocation over static budgeting, ! Evaluation of different power shifting policies, ! Analysis of system and workload factors critical to successful power shifting, and ! Proposal of performance-sensitive power budget enforcement mechanisms that ensure system reliability.

This paper examines the problem of data placement in Bubba, a highly-parallel system for data-intensive applications being developed at MCC. “Highly-parallel” implies that load balancing is a critical performance issue. “Data-intensive” means data is so large that operations should be executed where the data resides. As a result, data placement becomes a critical performance issue. In general, determining the optimal placement of data across processing nodes for performance is a difficult problem. We describe our heuristic approach to solving the data placement problem in Bubba. We then present experimental results using a specific workload to provide insight into the problem. Several researchers have argued the benefits of declustering (i e, spreading each base relation over many nodes). We show that as declustering is increased, load balancing continues to improve. However, for transactions involving complex joins, further declustering reduces throughput because of communications, startup and termination overhead. We argue that data placement, especially declustering, in a highly-parallel system must be considered early in the design, so that mechanisms can be included for supporting variable declustering, for minimizing the most significant overheads associated with large-scale declustering, and for gathering the required statistics.