A System Dynamics Model for Predicting Supply and Demand of Medical Education Talents in China
Bing Xiao 1,2
,
 
 
 
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1
School of Computer Science, Guangdong Polytechnic Normal University, 510665, China
 
2
Case School of Engineering, Case Western Reserve University, 44106, USA
 
 
Online publication date: 2017-08-11
 
 
Publication date: 2017-08-11
 
 
Corresponding author
Bing Xiao   

School of Computer Science, Guangdong Polytechnic Normal University, 510665, China; Case School of Engineering, Case Western Reserve University, 44106, USA
 
 
EURASIA J. Math., Sci Tech. Ed 2017;13(8):5033-5047
 
KEYWORDS
ABSTRACT
The purpose of this paper is to study the relationship between the demand for medical talents in China and the supply of talents in medical colleges and universities. Predicting the future development of medical talents is of great significance to the establishment of medical education and the cultivation of medical talents. Based on data from China Statistical Yearbook and OECD 34 countries from 2010 to 2014, as well as some research results on overseas and domestic scholars, a system dynamics prediction model relating the supply and demand of medical education talents is developed in this paper. Actual data for the Jiangsu Province is used to demonstrate the correctness, validity and applicability of the model. Simulation results show that, under the current conditions and political environment and to maintain the current average levels of OECD 34 countries, the projected number of demand for doctors in Jiangsu in 2024 is a little more than 319,800. Based on current data, there is still quite a large gap between these indexes in China and the desired target levels. Consequently, the system and structure of medical education need to be adjusted, with the corresponding policies and management system simultaneously reformed and medical environment improved.
 
REFERENCES (21)
1.
Agnes, R. S., & Sarah, N. T. A. (2016). Applying a system dynamics modelling approach to explore policy options for improving neonatal health in Uganda. Health Research Policy and Systems, 1-17.
 
2.
Anand, S, Fan, V. Y., Zhang, J., et al. (2008). China’s human resources for health: quantity, quality, and distribution. The Lancet, 372, 1774-1781.
 
3.
Behdad, K. (2009). System Dynamics approach to analysing the cost factors effects on cost of quality. International Journal of Quality and Reliability Management, 26, 685-698.
 
4.
Editorial Committee of Jiangsu Health Yearbook (2010-2014). Year book of public health in Jiangsu. Scientific and technical Documentation Press: Beijing.
 
5.
Eric, W., & Jason, B. (2016). Improving forecast accuracy through talent management. Journal of Business Forecasting, 4-9.
 
6.
Forrester, J. W. (1961). Industrial Dynamics. MIT Press: Cambridge, MA.
 
7.
Gao, W., Farahani, M. R., Aslam, A., & Hosamani, S. (2017). Distance learning techniques for ontology similarity measuring and ontology mapping. Cluster Computing-The Journal of Networks Software Tools and Applications, 20(2SI), 959-968.
 
8.
Ge, K., Dou, Y. Y. (2014). Application of system dynamics in the training of high skilled talents. China Mining Magazine, 23, 28-30.
 
9.
Jiangsu Provincial Statistics Bureau (2010-2014). Jiangsu Statistical Yearbook. China Statistics Press: Beijing.
 
10.
Mi, C., Shen, Y., Mi, W., & Huang, Y. (2015). Ship Identification Algorithm Based on 3D Point Cloud for Automated Ship Loaders. Journal of Coastal Research, (73), 28-34.
 
11.
Mi, C., Zhang, Z. W., Huang, Y. F., & Shen, Y. (2016). A fast automated vision system for container corner casting recognition. Journal of Marine Science and Technology, 24(1), 54-60.
 
12.
National Bureau of Statistics of China (2015). China Statistical Yearbook. China Statistics Press: Beijing.
 
13.
OECD data (2010-2014). Health resources. Retrieved from https://data.oecd.org/healthre....
 
14.
Wang, Ch. H., (2010). A Study of the Relationship Between Schooling and Enterprises’ Demand for talents. Journal of Social Science, 6, 393-398.
 
15.
Wang, P.T., & Gao, X. Zh. (2000). The talent requirements and quality prediction model. System Engineering Theory and Practice, 12, 123-128.
 
16.
Wang, J. L., Liu, S. F., Qiu, G. H., & Mi, Ch. M. (2010). Research on the current situation of Suzhou science and technology innovation talents and supply forecast. Science & Technology Progress and Policy, 27, 141-144.
 
17.
Wu, L. H., & Zheng, Ch. Y. (2004). Neural network model to predict the total amount of human resources in cities. Science and Technology Progress and Countermeasures, 8, 100-102.
 
18.
Xiao, B. (2013). System Dynamic Model of Enterprise Cluster Complex Network Resources Integratability. Information Technology Journal, 12, 1812-5646.
 
19.
Yu, Ch. Y., & Wang. P. T. (2004). The forecast model of system reconstructability analysis. Cybernetics, 33, 1016-1019.
 
20.
Zhang, Y. F., & Yan, Q. Sh. (2011). Application of SVM model modified by Markov method in the prediction of science and technology talent resources. Statistics and Decision, 11, 171-173.
 
21.
Zhang, Sh. (2013). Study on human resource situation and development strategy of medical education in China. Fudan Education Forum, 11, 86-92.
 
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