Research On Competency Evaluation Of Rural Doctors Based On Artificial Neural Network

Objective: In view of the characteristics of rural doctors to explore an intuitive and efficient way to measure competence,on the basis of analyzing the composition of evaluation index system of rural doctors' competency,establishing a rural doctor competency evaluation model based on BP neural network,using artificial neural network evaluation of the overall competence of rural doctors,analyze the current situation of country doctors competency,in order to improve the competency of rural doctors to provide strategies and basis.Method: This study combined with the previous research on the competence theory of rural doctors,based on the pool of evaluation indicators established in the previous research,through exploratory factor analysis and screening of dimensions and entries to establish rural doctors competency evaluation index system,using confirmatory factor analysis to verify the evaluation system,ultimately determine the competency of rural doctors evaluation index system.In order to research the status quo,using questionnaires and cross-sectional survey,using BP neural network to evaluate the competency of rural doctors,using chi-square test,analysis of variance and other analysis methods to identify key populations,we put forward strategies to adapt to the current development of rural doctors and establish the path of sustainable development of rural doctors.Results: On the basis of exploratory factor analysis,the evaluation index system of rural doctors' competency is extracted and constructed.The evaluation system includes 24 evaluation indexes including thinking ability,communication ability,personal characteristics,professional skills,and occupational cognition.Using confirmatory factor analysis,the factors structure system and structure of theevaluation index system are verified.The confirmatory factor shows that the main fitting index CMIN /DF=1.86,RMR=0.038,GFI=0.924,TLI=0.926,IFI=0.942,CFI=0.941,all above the fitting requirement,and the model structure validity test is adopted,indicating that the overall fitting effect of the evaluation index system is ideal.The BP neural network in artificial neural network was selected to evaluate the competency of rural doctors.Using the Matlab establish a 3-layer BP neural network model,through training and testing competency evaluation model of the BP neural network model acceptance criteria,the evaluation results showed that,among the rural doctors surveyed,the good grades accounted for 43.84%,followed by the general ranking,accounting for 28.08%,and the excellent grade accounting for23.89%,with the lowest proportion at 4.19%.Among the factors affecting the competency of rural doctors,the total effect from large to small is income level,training,equipment conditions and endowment insurance.Through social demographic characteristics under the village doctor competency level difference inspection,found that the different age,record of formal schooling,qualification,different roles,different economic development level regional differences of rural doctors,should be listed as a focus on the crowd.Conclusions: This study combined with the actual situation of the rural areas in shandong province,through exploratory factor analysis,confirmatory factor analysis to validate,constructs a total of 24 contains five dimensions competency evaluation index system of evaluation index of competence,and combining the evaluation index system by artificial neural network to evaluate the competence level of rural doctors surveyed area,the evaluation results show that the current rural doctors in shandong province competency better overall health,but there are still a large room to improve,through the competency level difference test,identify should focus on rural doctors group,and to establish a sustainable development path of rural doctors,to further enhance the level of competence of rural doctors to provide a new Angle of view,is to improve the contingent of rural doctors working status,development of rural doctors and human resource management to provide important reference.