Job burnout and turnover intention among Chinese primary healthcare staff: the mediating effect of satisfaction

Abstract

Objectives: Although China has done a lot in strengthening the primary healthcare system, the high turnover intention is still a social problem to be reckoned with. The objective of this study is to explore the mediating effect of satisfaction between job burnout and turnover intention.

Design: Cross-sectional study.

Methods: A cross-sectional study was conducted to make sense of the job burnout, satisfaction and turnover intention among primary healthcare workers in central China. Structural equation modelling (SEM) was performed to study the mediating effect of satisfaction between job burnout and turnover intention with maximum likelihood estimation. The mediation effect test was carried out by using the bootstrap method.

Results: SEM showed that job burnout was positively related to the turnover intention with the standard path coefficient of 0.845 (C.R.=34.055, p<0.001). The partial mediating effect of satisfaction was 0.047, making up 5.32% of the total effect. The goodness-of-fit was acceptable (Goodness of Fit Index=0.947, Comparative Fit Index=0.975, root mean square error of approximation=0.067, Non-Normed Fit Index=0.971, Incremental Fit Index=0.975). Age, education level, monthly income, hire form and night shift were also found significantly correlated with turnover intention, and no difference was found between physicians and nurses.

Conclusions: The turnover intention is significantly affected by job burnout, satisfaction and demographical characteristics including age, education level, monthly income, hire form and night shift. Satisfaction can be regarded as a mediator between job burnout and turnover intention. Relative measures can be taken to promote enthusiasm and satisfaction thus decreasing the turnover rate.

Keywords: burnout; healthcare; job satisfaction; mediating effect; turnover intention.

Figure 1

Hypothesised model of burnout, satisfaction and turnover intention.

Figure 2

The structural equation modelling for the hypothetical model. AGFI, adjusted goodness of fit index; CFI, Comparative Fit Index; GFI, Goodness of Fit Index; IFI, Incremental Fit Index; NNFI, Non-Normed Fit Index; RMSEA, root mean square error of approximation.

Figure 3

The structural equation modelling after introducing demographic characteristic. AGFI, adjusted goodness of fit index; CFI, Comparative Fit Index; GFI, Goodness of Fit Index; IFI, Incremental Fit Index; NNFI, Non-Normed Fit Index; RMSEA, root mean square error of approximation.