Psychometric evaluation of the Chinese version of the burnout syndrome assessment scale in nurses

ObjectiveThis study aimed to translate the Burnout Syndrome Assessment Scale (BOSAS) into Chinese and validate its reliability and validity among Chinese emergency department and ICU nurses.MethodsThe scale was translated into Chinese using Brislin’s translation principle. A total of 626 nurses from Jiangxi, Zhejiang, and Fujian provinces in China participated in an online questionnaire survey. The survey included the general information questionnaire for nurses developed by the research team and the Chinese version of the Burnout Syndrome Assessment Scale. Reliability and validity of the Chinese version of the scale were analyzed using SPSS.25 and AMOS.24 software.ResultsThe Chinese version of the Burnout Syndrome Assessment Scale consists of a total of 20 items, encompassing two dimensions: personal burnout and job burnout. This structure is consistent with the original English version of the scale. The Chinese version of BOSAS demonstrated high internal consistency, with a Cronbach’s α coefficient of 0.941. Additionally, the scale exhibited good split-half reliability (0.765) and test-retest reliability (0.871). The content validity index (S-CVI) was 0.971, indicating strong content validity. Exploratory factor analysis confirmed the same 2-factor structure as the original scale, and confirmatory factor analysis further validated this structure, with all fit indices indicating appropriateness.ConclusionThe Burnout Syndrome Assessment Scale has been successfully introduced and its reliability and validity have been verified in Chinese emergency department and ICU nurses

Quality assessment of randomized controlled trial abstracts on drug therapy of periodontal disease from the abstracts published in dental Science Citation Indexed journals in the last ten years

Randomized controlled trials (RCTs) provide the highest level of evidence and are likely to influence clinical decision-making. This study evaluated the reporting quality of RCT abstracts on drug therapy of periodontal disease and assessed the associated factors. The Pubmed database was searched for periodontal RCTs published in Science Citation Indexed (SCI) dental journals from 2010/01/01 to 2019/07/17. Information was extracted from the abstracts according to a modified Consolidated Standards of Reporting Trials (CONSORT) guideline checklist. The data was analyzed using descriptive statistical analysis and the statistical associations were examined using the linear regression analysis (P <0.05). This study retrieved 1715 articles and 249 of them were finally included. The average overall CONSORT score was 15.6 ± 3.4, which represented 40.9% (±0.6) of CONSORT criteria filling. The reporting rate of some items (trial design, numbers analyzed, confidence intervals, intention-to-treat analysis or per-protocol analysis, harms, registration) was less than 30%. The adequate reporting rate of some items (participants, randomization, numbers analyzed, confidence intervals, intention-to-treat analysis or per protocol analysis) was no more than 4%. None of the abstracts reported funding. According to the multivariable linear regression results, number of authors (P=0.030), word count (P <0.001), continent (P=0.003), structured format (P <0.001), type of periodontal disease (P <0.001) and international collaboration (P=0.023) have a significant association with reporting quality. The quality of RCT abstracts on drug therapy of periodontal disease in SCI dental journals remained suboptimal. More efforts should be made to improve RCT abstracts reporting quality

A Comprehensive Survey on Distributed Training of Graph Neural Networks

Graph neural networks (GNNs) have been demonstrated to be a powerful algorithmic model in broad application fields for their effectiveness in learning over graphs. To scale GNN training up for large-scale and ever-growing graphs, the most promising solution is distributed training which distributes the workload of training across multiple computing nodes. At present, the volume of related research on distributed GNN training is exceptionally vast, accompanied by an extraordinarily rapid pace of publication. Moreover, the approaches reported in these studies exhibit significant divergence. This situation poses a considerable challenge for newcomers, hindering their ability to grasp a comprehensive understanding of the workflows, computational patterns, communication strategies, and optimization techniques employed in distributed GNN training. As a result, there is a pressing need for a survey to provide correct recognition, analysis, and comparisons in this field. In this paper, we provide a comprehensive survey of distributed GNN training by investigating various optimization techniques used in distributed GNN training. First, distributed GNN training is classified into several categories according to their workflows. In addition, their computational patterns and communication patterns, as well as the optimization techniques proposed by recent work are introduced. Second, the software frameworks and hardware platforms of distributed GNN training are also introduced for a deeper understanding. Third, distributed GNN training is compared with distributed training of deep neural networks, emphasizing the uniqueness of distributed GNN training. Finally, interesting issues and opportunities in this field are discussed.Comment: To Appear in Proceedings of the IEE

Finite-Time Consensus of Networked Multiagent Systems with Time-Varying Linear Control Protocols

Finite-time consensus problems for networked multiagent systems with first-order/second-order dynamics are investigated in this paper. The goal of this paper is to design local information based control protocols such that the systems achieve consensus at any preset time. In order to realize this objective, a class of linear feedback control protocols with time-varying gains is introduced. We prove that the multiagent systems under such kinds of time-varying control protocols can achieve consensus at the preset time if the undirected communication graph is connected. Numerical simulations are presented to illustrate the effectiveness of the obtained theoretic results

Metagenomic Insights Into the Contribution of Phages to Antibiotic Resistance in Water Samples Related to Swine Feedlot Wastewater Treatment

In this study, we examined the types of antibiotic resistance genes (ARGs) possessed by bacteria and bacteriophages in swine feedlot wastewater before and after treatment using a metagenomics approach. We found that the relative abundance of ARGs in bacterial DNA in all water samples was significantly higher than that in phages DNA (&gt;10.6-fold), and wastewater treatment did not significantly change the relative abundance of bacterial- or phage-associated ARGs. We further detected the distribution and diversity of the different types of ARGs according to the class of antibiotics to which they confer resistance, the tetracycline resistance genes were the most abundant resistance genes and phages were more likely to harbor ATP-binding cassette transporter family and ribosomal protection genes. Moreover, the colistin resistance gene mcr-1 was also detected in the phage population. When assessing the contribution of phages in spreading different groups of ARGs, β-lactamase resistance genes had a relatively high spreading ability even though the abundance was low. These findings possibly indicated that phages not only could serve as important reservoir of ARG but also carry particular ARGs in swine feedlot wastewater, and this phenomenon is independent of the environment