Principal component regression analysis of research performance and personnel composition in clinic departments
10.3760/cma.j.issn.1006-1924.2020.02.002
- VernacularTitle:临床科室科研绩效与人员构成的主成分回归分析
- Author:
Danlu ZHANG
1
;
Qi WANG
Author Information
1. 大连医科大学附属第二医院 116023
- From:
Chinese Journal of Medical Science Research Management
2020;33(2):86-90
- CountryChina
- Language:Chinese
-
Abstract:
Objective:The personnel composition factors and impact which influenced research performance of clinic departments were explored, to optimize personnel structure for further references on enhance the hospital scientific achievements.Methods:The research performance scores and personnel overall composition of 75 clinic departments in the year of 2017 were retrospectively analyzed. Principal component regression was used to analyze the correlation between research performance and personnel composition.Results:KMO was 0.635, P<0.001, the model had statistical significance. Four common factors identified were: middle-age females, young masters, senior master supervisors and doctors & doctoral supervisors. Accumulative variance contribution rate was 87.19%. After reducing equations, doctor degrees ( β=124.164), intermediate professional title ( β=123.573), Age≥40 ( β=102.149) and doctoral supervisors ( β=95.309) were four most influential factors on research performance of clinic departments. Conclusions:The selection of common factors should be comprehensively considered, combining with multiple criterions and professional significance. A team structure based on middle-aged and young people led by doctoral supervisors, and with backbones of doctoral degree personnel will help to improve the scientific research performance of clinical departments.
