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AbstractThe validity of network observations is sometimes of concern in empirical studies, since observed networks are prone to error and may not represent the population of interest. This lack of validity is not just a result of random measurement error, but often due to systematic bias that can lead to the misinterpretation of actors' preferences of network selections. These issues in network observations could bias the estimation of common network models (such as those pertaining to influence and selection) and lead to erroneous statistical inferences. In this study, we proposed a simulation-based sensitivity analysis method that can evaluate the robustness of inferences made in social network analysis to six forms of selection mechanisms that can cause biases in network observations—random, homophily, anti-homophily, transitivity, reciprocity, and preferential attachment. We then applied this sensitivity analysis to test the robustness of inferences for social influence effects, and we derived two sets of analytical solutions that can account for biases in network observations due to random, homophily, and anti-homophily selection.

AbstractMuch of the impact of a policy depends on how it is implemented, especially as mediated by organizations such as schools or hospitals. Here, we focus on how implementation of evidence‐based practices in human service organizations (e.g., schools, hospitals) is affected by intraorganizational network dynamics. In particular, we hypothesize intraorganizational behavioral divergence and network polarization are likely to occur when actors strongly identify with their organizations. Using agent‐based models, we find that when organizational identification is high, external change agents who attempt to direct organizations by introducing policy aligned messages (e.g., professional development emphasizing specific teaching practices) may unintentionally contribute to divergence in practice and polarization in networks, inhibiting full implementation of the desired practices as well as reducing organizational capacity to absorb new practices. Thus, the external change agent should consider the interaction between the type of message and the intraorganizational network dynamics driven by organizational identification.

With the wide application of sea-based reactors, the impact of ocean conditions on the safety performance of reactors has gradually attracted attention. In this paper, by establishing the thermal hydraulic transient analysis model and the critical heat flux (CHF) model of natural circulation system, the CHF characteristics in the rectangular channel of natural self-feedback conditions under ocean conditions are studied. The results show that the additional acceleration field generated by ocean conditions will affect the thermal hydraulic parameters of the natural circulation system, that is, the external macroscopic thermal hydraulic field. On the other hand, the boiling crisis mechanism will be affected, that is, the force on the bubble and the thickness of the liquid film. Within the parameters of the study, ocean conditions have a great impact on CHF of natural circulation, and the maximum degradation of CHF is about 45%. The obtained analysis results are significant to the improvement of design and safety operation of the reactor system.

AbstractWe developed a decision support system to model, analyze, and improve market adoption of Dell's SupportAssist program. SupportAssist is a proactive and preventive support service capability that can monitor system operations data from all connected Dell devices around the world and predict impending failures in those devices. Performance of such data‐intensive services is highly interconnected with market adoption: service performance depends on the richness of the customer database, which is influenced by customer adoption that in turn depends on customer satisfaction and service performance—a reinforcing feedback loop. We developed the SupportAssist adoption model (SAAM). SAAM utilizes various data sources and modeling techniques, particularly system dynamics, to analyze market response under different strategies. Dell anticipates improving market adoption of SupportAssist and revenue from support services, as results of using this analytical tool.Copyright © 2018 The Authors System Dynamics Review published by John Wiley & Sons Ltd on behalf of System Dynamics Society

Jun Zhang,1,* Feng Zhou,2,* Xiaoxiang Ge,3 Xu Ran,4 Yeping Li,4 Si Chen,5 Xiaotong Dai,5 Dafang Chen,5 Baoguo Jiang6 1Department of Neurology, Peking University People's Hospital, Beijing, China; 2Department of Data Science, Tsinghua University, Beijing, China; 3Office of Academic Affairs, Army Military Medical University, Chongqing, China; 4Department of Research and Development, Chongqing Hospital Management Limited Company (Doctor-Patient Experience Research Base, National Health and Family Planning Commission of the People's Republic of China), Chongqing, China; 5Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China; 6Department of Orthopedics, Peking University People's Hospital, Beijing, China *These authors contributed equally to this work Background and aims: Patient satisfaction is one of the important ways to measure the results of treatment and the quality of medical services. The purpose of the present study was to verify the reliability and validity of the indicator system for evaluating satisfaction of outpatients and inpatients in Chinese hospitals.Methods: The study was based on the satisfaction questionnaire program of the national doctor–patient experience research center with data from 99,802 outpatients and 229,215 inpatients, collected in China between 2016 and 2017. We adopted exploratory factor analysis for validity analysis and the method of split-half reliability and Cronbach's α coefficient for reliability analysis.Results: In the validity analysis of the indicator system for outpatients, the factor loading was between 0.438 and 0.919, and the reliability was excellent. In the validity analysis of the indicator system for nonsurgical inpatients, the factor loading of the secondary indicators was between 0.417 and 0.75, and the reliability was excellent. In the validity analysis of the indicator system for surgical inpatients satisfaction, the factor loading of the secondary indicators range was 0.391–0.751, and the reliability was excellent.Conclusion: The indicator systems for evaluating satisfaction of outpatients, surgical inpatients and nonsurgical inpatients all have excellent reliability and good validity. They can be widely used for an outpatient and inpatient satisfaction questionnaire in Chinese hospitals. Keywords: patient satisfaction questionnaire, reliability, validity, evaluation