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1.
Chinese Journal of Information on Traditional Chinese Medicine ; (12): 1-5, 2018.
Article in Chinese | WPRIM | ID: wpr-707077

ABSTRACT

Objective To understand the current situation of TCM human resources; To forecast the number of TCM human resources in 2020; To provide data for national scientific management policy. Methods Through Statistical Yearbook of China's Health and Family Planning and Yearbook of Chinese Traditional Medicine, the national human resources data of TCM were collected and compiled from 2010 to 2015, and descriptive analysis and gray model method were used for data analysis. Results The TCM human resources continued to grow, but the growth rate of Chinese pharmacists was slow; the lack of TCM human resources in TCM health institutions; the uneven distribution of human resources in inter-provincial TCM; the number of TCM human resources in 2020 could meet the requirements of TCM 13th Five-year Plan. Conclusion The development of Chinese pharmacists should be paid attention to; the proportion of TCM pharmacists should be balanced; the proportion of TCM human resources in TCM institutions should be improved; scientific planning and the coordinated development of TCM human resources in various regions should be promoted.

2.
Chinese Journal of Medical Library and Information Science ; (12): 68-71, 2018.
Article in Chinese | WPRIM | ID: wpr-712446

ABSTRACT

Evaluation of paper academic influence is an important component part for the scientific and technological evaluation of traditional Chinese medicine,and plays an important role in promoting the scientific and technological innovative development of traditional Chinese medicine. The Traditional Chinese Medicine-Paper Influence Evaluation System (TCM-PIES), which was thus designed according to the existing problems in scientific and technological evaluation of traditional Chinese medicine and in evaluation of papers on traditional Chinese medicine,and the characteristics of academic papers on traditional Chinese medicine by analyzing their data with information technology as its support, is applied in selection of excellent institutions and professional academic interests.

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