Background:In the context of China's aging population,meeting consumer demand is an essential way for nursing homes to fulfill social responsibilities and improve competitive advantages.However,since little is known about the elderly's service level and price choices for nursing home care,this study aims to explore the non-disabled elderly's nursing home admission intention,service level,and price choices.Methods:A cross-sectional survey of 402 non-disabled respondents was conducted in three different income level cities of Zhejiang Province,in July and August 2018.Multinomial logistic regression and multiple linear regression were used to identify the determinants of admission intention,service level choice,and price choice.Results:Education,residence,and number of children were significantly associated with nursing home admission intention.Compared to those with no intention,the elderly with higher income and household wealth were less likely to have conditional intentions,and those living with the family were less likely to have unconditional intentions.Compared to medium-level services,the elderly with higher monthly income(relative risk ratio[RRR]3.07,95％confidence interval[CI]:1.801 to 5.233),household wealth(RRR 5.451,95％CI:2.249 to 13.216),and age(RRR 1.528,95％CI:1.004 to 2.326)were more likely to prefer high-level services,while older adults with higher monthly income(RRR 0.516,95％CI:0.344 to 0.774),and those with pensions(RRR 0.267,95％CI:0.076 to 0.931)were less likely to prefer low-level services.The elderly's price preference increased by 398 CNY as monthly income increased by 1 000 CNY,and by 270 CNY as the housing number increased by one.Having pensions increased price preference(468 CNY),whereas having health insurance decreased price preference(-690 CNY).Conclusion:The elderly's intention of nursing home admission was primarily affected by sociodemographic fac-tors,while price and service level choices were primarily affected by financial factors.Nursing homes should use the market segmentation method to provide precision nursing home care for different groups of non-disabled elderly.

Since the outbreak of coronavirus disease 2019 (COVID-19) , in order to relieve the pressure of outpatient services, especially fever clinic service in hospital, and to avoid the cross-infection among patients, the general practice team of Sir Run Run Shaw Hospital has opened free online counseling service. This article introduces the workflow and related contents of the online counseling mode of general practice under the COVID-19 epidemic situation, so as to provide reference for medical institutions to implement "internet+general practice" mode of counseling service.

Aiming at the problem of low recognition accuracy of motor imagery electroencephalogram signal due to individual differences of subjects, an individual adaptive feature representation method of motor imagery electroencephalogram signal is proposed in this paper. Firstly, based on the individual differences and signal characteristics in different frequency bands, an adaptive channel selection method based on expansive relevant features with label F (ReliefF) was proposed. By extracting five time-frequency domain observation features of each frequency band signal, ReliefF algorithm was employed to evaluate the effectiveness of the frequency band signal in each channel, and then the corresponding signal channel was selected for each frequency band. Secondly, a feature representation method of common space pattern (CSP) based on fast correlation-based filter (FCBF) was proposed (CSP-FCBF). The features of electroencephalogram signal were extracted by CSP, and the best feature sets were obtained by using FCBF to optimize the features, so as to realize the effective state representation of motor imagery electroencephalogram signal. Finally, support vector machine (SVM) was adopted as a classifier to realize identification. Experimental results show that the proposed method in this research can effectively represent the states of motor imagery electroencephalogram signal, with an average identification accuracy of (83.0±5.5)% for four types of states, which is 6.6% higher than the traditional CSP feature representation method. The research results obtained in the feature representation of motor imagery electroencephalogram signal lay the foundation for the realization of adaptive electroencephalogram signal decoding and its application.
Humans ; Imagination ; Signal Processing, Computer-Assisted ; Brain-Computer Interfaces ; Electroencephalography/methods* ; Imagery, Psychotherapy ; Algorithms

In the context of the "Belt and Road" initiative, We systematically analyzed the general education and training systems of 16 Western Pacific countries and regions, including general practitioner college education, post-graduation education, and faculty status. Developed countries and regions have a long-term medical education system, strong faculty, and a comprehensive training model for general practitioners. Underdeveloped countries and regions are relatively weak in educational institutions, faculty, and general practitioner training models. The underdeveloped countries and regions should develop a general medical education and training system in terms of strengthening the construction of general medical disciplines, strengthening the supervision and certification of general practitioners, improving the general medical training model, and strengthening the construction of teachers.

Due to the high complexity and subject variability of motor imagery electroencephalogram, its decoding is limited by the inadequate accuracy of traditional recognition models. To resolve this problem, a recognition model for motor imagery electroencephalogram based on flicker noise spectrum (FNS) and weighted filter bank common spatial pattern ( wFBCSP) was proposed. First, the FNS method was used to analyze the motor imagery electroencephalogram. Using the second derivative moment as structure function, the ensued precursor time series were generated by using a sliding window strategy, so that hidden dynamic information of transition phase could be captured. Then, based on the characteristic of signal frequency band, the feature of the transition phase precursor time series and reaction phase series were extracted by wFBCSP, generating features representing relevant transition and reaction phase. To make the selected features adapt to subject variability and realize better generalization, algorithm of minimum redundancy maximum relevance was further used to select features. Finally, support vector machine as the classifier was used for the classification. In the motor imagery electroencephalogram recognition, the method proposed in this study yielded an average accuracy of 86.34%, which is higher than the comparison methods. Thus, our proposed method provides a new idea for decoding motor imagery electroencephalogram.
Brain-Computer Interfaces ; Imagination ; Signal Processing, Computer-Assisted ; Electroencephalography/methods* ; Algorithms ; Spectrum Analysis