Objective To investigate the the status of Health manpower resources of rural hospitals, Health service, health personnel ’s reflection for the hospital’ current issues in Pingshan Xibaipo town in Hebei province, so as to provide baseline data for the establishment of a rural hospital’s comprehensive model. Method We conducted the survey which using questionnaires to collect the information of the rural hospital staff’s self-evaluation of health services, the reflecting of the problems of the current hospitals ,the views of the future building, village health humen resources for health, etc. SPSS 17.0 sofewarewas used for data analysis. Result The following problems are existing in Xibaipo town hospitals. (1) The rural hospital infrastructure is poor: 14 beds, housing a total area of 832 square meters is far below the national average. (2) The quality of the health personnel in the rural hospital is low.The proportion of doctors and nurses (1׃0.75) is higher than the Ministry of Health’s standard (1׃1), less educated (only 2 person’s first degree is college), low-level professional titled (the proportion of health officer of no professional title is 42.86%), etc. (3) Health resources in village are scarce: the quality of rural doctors is low (90.91% are non-formal school graduation), village clinics is lack of facilities (16 villages have only a total of 10 village clinics, 5 beds). Conclusion The infrastructure of the rural hospital was poor,while the situation of the personnel allocation in the rural hospital was irrational ,and the integrated management of the hospital in town and village is not good . The health personnel proposed recommendation about construction of health personnel, departments, information technology , etc .The rural hospital should introduce equipment and personnel, improve health care services and strengthen the integrated management of the hospital in town and village .
Objective To investigate the residents’ needs, demand and utilizations of health service in Pingshan Xibaipo town in Hebei province, so as to provide baseline data for the establishment of a rural hospital’s comprehensive model. Method We conducted the survey which using questionnaires to collect the information of all residents in 16 villages of Xibaipo town. The contents of the survey was residents’ spectrum of disease, health service needs, demand and utilization, the cost and Evaluation of medical services, etc. SPSS 17.0 software was used for descriptive analysis. Result The local health services situation was ‘high need, low-use’. The participation rate of the new rural cooperative medical care (NCMS) was 98.06%. Most residents choose rural hospital as the hospital to visits and hospitalization. The evaluation of the equipment and the reasonableness of the charges was lowest. There was a great demand for periodic physical examination and health education. Conclusion The proportion of Health service needs Converse to health services use is low. The health services of major diseases such as hypertension, cerebrovascular diseases need to focus on strengthening. The government should increase financial investment and strengthen basic health resources, strive to do a good work of the residents’ health education and physical examination.
Objective To investigate the basic condition and psychological health status of children in rural areas of Xibaipo town in Hebei province, learn the psychological problems of the leftover children and their influencing factors, and provide scientific basis for the school and family education for the leftover children. Methods A questionnaire survey was performed on 446 middle and primary school students in Xibaipo town, and the self-designed questionnaire was applied to collect the basic condition, family condition and psychological health status of the children. EpiData 3.0 and SPSS 17.0 were used to perform data entry and data analyses, respectively. Results The survey displayed that the leftover children accounted for 43.5% of the total number of students. The negative emotion of leftover children was significantly ber than that of non-leftover children (χ2=12.484, Plt;0.001), especially for children with both parents living far away. The main factors affecting children’s emotion were their academic performance and whether they were left behind by their parents. Conclusion The psychological health status of the leftover children is poorer than that of non-leftover children, especially for children with both parents working outside and mothers working outside. Being left over and the academic performance are the factors affecting children’s mental condition. More attention should be paid to the physical and mental development of leftover children and vigorous efforts should be exerted to formulate corresponding policies and take countermeasures so as to promote healthy growth of children.
The effective classification of multi-task motor imagery electroencephalogram (EEG) is helpful to achieve accurate multi-dimensional human-computer interaction, and the high frequency domain specificity between subjects can improve the classification accuracy and robustness. Therefore, this paper proposed a multi-task EEG signal classification method based on adaptive time-frequency common spatial pattern (CSP) combined with convolutional neural network (CNN). The characteristics of subjects' personalized rhythm were extracted by adaptive spectrum awareness, and the spatial characteristics were calculated by using the one-versus-rest CSP, and then the composite time-domain characteristics were characterized to construct the spatial-temporal frequency multi-level fusion features. Finally, the CNN was used to perform high-precision and high-robust four-task classification. The algorithm in this paper was verified by the self-test dataset containing 10 subjects (33 ± 3 years old, inexperienced) and the dataset of the 4th 2018 Brain-Computer Interface Competition (BCI competition Ⅳ-2a). The average accuracy of the proposed algorithm for the four-task classification reached 93.96% and 84.04%, respectively. Compared with other advanced algorithms, the average classification accuracy of the proposed algorithm was significantly improved, and the accuracy range error between subjects was significantly reduced in the public dataset. The results show that the proposed algorithm has good performance in multi-task classification, and can effectively improve the classification accuracy and robustness.