Objective To understand the cognition and acceptance of community hemodialysis centers among hemodialysis patients in Yangzhou, and to provide theoretical basis for the development of community hemodialysis centers. Methods A cluster random sampling method was used to select 400 maintenance hemodialysis patients treated in various areas of Yangzhou in April 2021 for a questionnaire survey to analyze the influencing factors of patients’ medical treatment behavior. Results A total of 390 valid questionnaires were recovered, with an effective recovery rate of 97.50%. Among the patients, 40.51% were very concerned about the construction of hemodialysis centers in the community, 56.67% understood the relevant policies, and 56.92% of the patients were willing to choose the community for dialysis treatment. The results of logistic regression analysis showed that the main factors affecting whether patients choose community for hemodialysis treatment include the patients’ residence [Jiangdu vs. Guangling: odds ratio (OR)=7.183, 95% confidence interval (CI) (2.010, 25.674), P=0.002; Gaoyou vs. Guangling: OR=22.512, 95%CI (7.201, 70.373), P<0.001; Yizheng vs. Guangling: OR=25.137, 95%CI (7.636, 82.744), P<0.001; Baoying vs. Guangling: OR=23.784, 95%CI (7.795, 72.569), P<0.001], degree of concern [some concern vs. very concerned: OR=0.267, 95 %CI (0.137, 0.521), P<0.001; not very concerned vs. very concerned: OR=0.062, 95%CI (0.023, 0.168), P<0.001; not concerned vs. very concerned: OR=0.101, 95% CI (0.023, 0.439), P=0.002], awareness [somewhat know vs. know very well: OR=0.025, 95%CI (0.002, 0.318), P=0.004; don’t know very well vs. know very well: OR=0.035, 95%CI (0.003, 0.439), P=0.009; don’t know vs. know very well: OR=0.006, 95%CI (0.000, 0.084), P<0.001]. Conclusions Hemodialysis patients in Yangzhou have a low level of awareness and acceptance of community-based hemodialysis centers. The patients’ residence, degree of attention and awareness of community-based hemodialysis center directly affect whether they choose the community for treatment. The relevant departments and medical institutions can start from the factors that affect patients’ choice of medical treatment, further strengthen the publicity of community dialysis, optimize the allocation of medical resources, and improve the capacity of community health services.
Emotion can reflect the psychological and physiological health of human beings, and the main expression of human emotion is voice and facial expression. How to extract and effectively integrate the two modes of emotion information is one of the main challenges faced by emotion recognition. In this paper, a multi-branch bidirectional multi-scale time perception model is proposed, which can detect the forward and reverse speech Mel-frequency spectrum coefficients in the time dimension. At the same time, the model uses causal convolution to obtain temporal correlation information between different scale features, and assigns attention maps to them according to the information, so as to obtain multi-scale fusion of speech emotion features. Secondly, this paper proposes a two-modal feature dynamic fusion algorithm, which combines the advantages of AlexNet and uses overlapping maximum pooling layers to obtain richer fusion features from different modal feature mosaic matrices. Experimental results show that the accuracy of the multi-branch bidirectional multi-scale time sensing dual-modal emotion recognition model proposed in this paper reaches 97.67% and 90.14% respectively on the two public audio and video emotion data sets, which is superior to other common methods, indicating that the proposed emotion recognition model can effectively capture emotion feature information and improve the accuracy of emotion recognition.