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1. chinaXiv:202111.00024 [pdf]

默认选项设置的助推效果:来自元分析的证据

赵宁; 刘鑫; 李纾; 郑蕊
Subjects: Psychology >> Applied Psychology

默认选项设置指通过设置默认选项以增加人们选择该选项可能性的助推方法,近年来被越来越多地运用于促进公众积极行为上,然而这种方法在有效性上却受到了来自公众和学者的质疑。据此,本研究运用元分析法整合了近几年有关默认选项设置的已有实证研究,分析该助推手段的有效性,并进一步考察可能影响其有效性的相关变量。研究共纳入符合要求的原始文献56篇, 含92个研究,结果发现:(1) 默认选项设置的确能有效助推人们的行为;(2) 默认选项设置的助推有效性在东、西方文化下存在显著差异,其在西方文化背景下的助推效果要显著好于其在东方文化背景下的使用;(3) 默认选项设置的助推有效性在不同情境应用领域中存在显著差异,相较于健康和环保领域,默认选项设置在金钱相关的情境领域下助推效果更好。

submitted time 2021-11-22 Hits1708Downloads75 Comment 0

2. chinaXiv:202105.00003 [pdf]

SEPRES: Sepsis prediction via a clinical data integration system and real-world studies in the intensive care unit

Chen, Qiyu; Li, Ranran; Lin, Chihche; Lai, Chiming; Chen, Dechang; Qu, Hongping; Huang, Yaling; Lu, Wenlian; Tang, Yaoqing; Li, Lei
Subjects: Medicine, Pharmacy >> Clinical Medicine

Background: Sepsis is vital in critical care medicine, and early detection and intervention are key to survival. We aimed to establish an early warning system for sepsis based on a data integration system that can be implemented in the intensive care unit (ICU). Methods: We trained the LightGBM and multilayer perceptron on the open-source database Medical Information Mart for Intensive Care for sepsis prediction. An ensemble sepsis prediction model was established based on the transfer learning and ensemble learning technique on the private dataset of Ruijin Hospital. The Shapley Additive Explanations analysis was applied to present feature importance on the prediction inference. With the development of data-integrating hub to collect and transmit data from different brands of ICU medical devices, the data integration system was established to receive, integrate, standardize, and store the real-time clinical data. In this way, the sepsis prediction model developed in the ICU of the Ruijin Hospital for the real-world study of sepsis early warning on ICU management. The trial was registered with ClinicalTrials.gov (NCT05088850). Findings: Our best early warning model achieved an area under the receiver operating characteristic curve (AUC) of 0·9833 in the task of detecting sepsis in 4-h preceding on the open-source database, while our ensemble model achieved an AUC of 0·9065?0·9436 in the retrospective research from 1?5-h preceding on the private database, and 0·8636?0·8992 in real-time real-world studies using the data integration system in the ICU of the Ruijin Hospital. In the continuous early warning process of patients admitted to the ICU, 22 patients who met the diagnostic criteria for sepsis during hospitalization were predicted as positive cases; 29 patients without sepsis were predicted as negative cases. Additionally, 17 patients were predicted as false-positive cases; in six patients with sepsis during ICU stay, the predicted probabilities at different time nodes were all less than the warning threshold 0·7 and predicted as false-negative cases. Interpretation: Machine learning models could allow accurate and real-time inference to detect sepsis onset within 5-h preceding at most with the help of the data integration system. We identified the features such as age, antibiotics, ventilation, and net balance to be important for the sepsis prediction inference. We argue that this system has promising potential to improve ICU management by helping medical practitioners identify at-sepsis-risk patients and prepare for timely diagnosis and intervention. Funding: Shanghai Municipal Science and Technology Major Project, the ZHANGJIANG LAB, and the Science and Technology Commission of Shanghai Municipality.

submitted time 2021-11-22 Hits1620Downloads674 Comment 0

3. chinaXiv:202111.00023 [pdf]

孤芳自赏还是乐于助人?员工自恋对亲社会行为的影响研究

刘文兴; 祝养浩; 柏 阳; 王海江; 韩 翼
Subjects: Psychology >> Management Psychology

本研究通过整合自恋双元理论和特质激活理论,探讨欣赏型自恋、敌对型自恋对员工亲社会行为的影响机制,深入分析了员工关系趋近型动机和关系回避型动机的中介作用以及任务相互依赖性的调节作用。通过对来自员工-同事二阶段配对的235份数据进行分析,结果发现:欣赏型自恋对员工亲社会行为具有显著的正向影响,敌对型自恋对员工亲社会行为具有显著负向影响;关系趋近型动机在欣赏型自恋与员工亲社会行为之间发挥着中介作用,关系回避型动机在敌对型自恋与员工亲社会行为之间没有发挥中介作用;任务相互依赖性不仅正向调节欣赏型自恋与员工关系趋近型动机的直接效应,而且还正向调节着欣赏型自恋通过关系趋近型动机对亲社会行为的间接效应。

submitted time 2021-11-22 Hits1710Downloads61 Comment 0

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