Professionalism: the major factor influencing job satisfaction among Korean and Chinese nurses

Int Nurs Rev. 2009 Sep;56(3):313-8. doi: 10.1111/j.1466-7657.2009.00710.x.

Abstract

Background: The nursing shortage has become an internationally important issue. Nurses' professionalism and job satisfaction have been recognized as strong factors influencing their turnover. As international interchanges in nursing education are growing between Korea and China, understanding the commonalities and differences in factors associated with job satisfaction is critical to improving nurses' job retention.

Aim: To compare the factors influencing job satisfaction among Korean and Chinese nurses.

Method: A cross-sectional survey was conducted. The participants were comprised of 693 nurses at three general hospitals in Jinan, People's Republic of China and 593 nurses at two general hospitals in Seoul, Korea. A questionnaire was designed to measure the nurses' professionalism and job satisfaction. Stepwise multiple regression analysis was performed to identify factors related to job satisfaction.

Results: Professionalism was the common factor influencing job satisfaction in Korean and Chinese nurses. Professionalism was positively related to job satisfaction in both groups. Additional factors associated with job satisfaction were demographics and job characteristics such as age, job position and department of work, which were significant only in Korean nurses.

Conclusions: Professionalism was the most important factor influencing job satisfaction in both Korean and Chinese nurses. Enhancing nursing professionalism is recommended as a common strategy to improve nurses' job retention across different healthcare systems.

Publication types

  • Comparative Study

MeSH terms

  • Adult
  • Age Distribution
  • Attitude of Health Personnel*
  • Career Mobility
  • China
  • Cross-Cultural Comparison
  • Cross-Sectional Studies
  • Educational Status
  • Humans
  • Job Satisfaction*
  • Marital Status / statistics & numerical data
  • Nurses / statistics & numerical data*
  • Nursing Research
  • Personnel Staffing and Scheduling / statistics & numerical data
  • Principal Component Analysis
  • Regression Analysis
  • Republic of Korea