Application of Structural Equation Model to Evaluate the Perception of Service Quality of Medical Staffs of infectious Disease Department in Chinese Hospitals

Objective To enhance the quality of medical service for Chinese patients through research of service quality from Chinese medical personnel. Methods Serv Qual scale was used for infection medical staffs randomly by sampling questionnaire in Beijing, Shanghai, Chengdu, Chongqing, Guangzhou and Nanning. The data collected were entered and analyzed using SPSS 20.0. Statistical methods included frequency, factor analysis, reliability analysis, correlation analysis, independent samples t test, one-way analyses of variance, simultaneous regression analysis and structural equation model analysis. Results The Kaiser-Meyer-Olkin value for the factor analysis of the scale was 0.970. The Cronbach's α for the reliability analysis was 0.975. The Pearson correlation coefficients were 0.624-0.874 and statistically significant. Undergraduates felt good, Ph D students felt bad; the doctors felt bad; managers felt good. Standard 5 dimensions of the regression coefficients were positive, including empathy(β = 0.288), reliability(β = 0.241) impacting on perceived service quality mostly. The control ability and stability of the standard error of perceived service quality directly effected value were 0.646 and 0.382, respectively. Conclusions Medical staffs of infectious disease department have poor perception of service quality. Hospitals should improve awareness and of clinicians and deepen the reform of the medical care system.

Adaptive Optimal Capacity Perception and Control for Wireless Multi-Hop Networks

In wireless multimedia communications, it is extremely difficult to derive general end-to-end capacity results because of decentralized packet scheduling and the interference between communicating nodes. In this paper, we present a state-based channel capacity perception scheme to provide statistical Quality-of-Service (QoS) guarantees under a medium or high traffic load for IEEE 802.11 wireless multi-hop networks. The proposed scheme first perceives the state of the wireless link from the MAC retransmission information and extends this information to calculate the wireless channel capacity, particularly under a saturated traffic load, on the basis of the interference among flows and the link state in the wireless multi-hop networks. Finally, the adaptive optimal control algorithm allocates a network resource and forwards the data packet by taking into consideration the channel capacity deployments in multi-terminal or multi-hop mesh networks. Extensive computer simulations demonstrate that the proposed scheme can achieve better performance in terms of packet delivery ratio and network throughput compared to the existing capacity prediction schemes.