Advertisement

BMC Public Health

, 19:1526 | Cite as

Moderating role of job satisfaction on turnover intention and burnout among workers in primary care institutions: a cross-sectional study

  • Xuyu Chen
  • Li Ran
  • Yuting Zhang
  • Jinru Yang
  • Hui Yao
  • Sirong Zhu
  • Xiaodong TanEmail author
Open Access
Research article
  • 396 Downloads
Part of the following topical collections:
  1. Occupational health

Abstract

Background

Global countries are suffering from a shortage of health professionals. Turnover intention is closely related to job satisfaction and burnout, making good use of these relationships could alleviate the crisis. Our research aims to examine the mediating role of job satisfaction in the relationship between burnout and turnover intention.

Methods

This research was conducted in Huangpi, China. The convenience sampling method and self-administereded questionnaires were used. 1370 of valid samples were collected with 97.72% effective rate. Descriptive analyses were conducted to describe social demographic factors. The structural equation model (SEM) was performed to adjust model fitting, and the mediation effect test was carried out by using the bootstrap method. Sobel-Z test was used to verify the significance of mediation effect.

Results

The mean age was 36.98 (SD = 9.84). The fitting indices of hypothetical model are not good. After the adjustments, χ2/df = 5.590, GFI = 0.932, AGFI = 0.901, CFI = 0.977, NFI = 0.973, IFI = 0.977, TLI = 0.970, RESEA = 0.058. The revised model fitted well, and the SEM was put up by using the bootstrap method. The mediating effect is partial, and Soble-Z test indicates that the mediation effect is significant. Burnout is negatively correlated with job satisfaction (p < 0.01) and the standardized path coefficient is − 0.41. Job satisfaction is also negatively correlated with turnover intention (p < 0.01) and the standardized path coefficient is − 0.18. Burnout is positively correlated with turnover intention (p < 0.01) and the standardized path coefficient is 0.83.

Conclusions

Job satisfaction is a mediating variable that affects the relationship between burnout and turnover intention. The mediating effect was a partial mediating effect and has a low impact of 7.4%. Improving treatment and giving more promotion opportunities for workers to improve job satisfaction, conducting career planning course and paying attention to employee psychological health to reduce job burnout. The above measures may be helpful to reduce employee turnover rate and alleviating the current situation of a shortage of health personnel in China.

Abbreviations

AGFI

Adjusted goodness of fit index

AIC

Akaike information criterion

ANOVA

Analysis of Variance

CFI

Comparative fit index

GFI

Goodness of fit index

IFI

Incremental fit index

JSS

Job Satisfaction Survey

MBI

Maslach Burnout Inventory

MSQ

Minnesota Satisfaction Questionnaire

NFI

Normed fit index

RMSEA

Root mean square error for approximation

Sa

The standard error of standardized path coefficient of path a

Sb

The standard error of standardized path coefficient of path b

Sc

The standard error of standardized path coefficient of path c

Sc′

The standard error of standardized path coefficient of path c’

SD

Standard Deviation

SE

Standard Error

SE-Bias

The standard error of bias

SEM

Structural Equation Model

SE-SE

The standard error caused by using bootstrap to estimate standard errors

TLI

Tucker-Lewis index

χ2

Chi-square

χ2/df

Chi-square divided by degree of freedom

Background

Turnover is generally viewed as the movement of staff out of an organization. It was regarded as a two-dimensional concept, distinguishing between the act of leaving as voluntary or involuntary, and between the leaving and joining of an individual to an organization [1]. A previous study [2] defined turnover intention as the next withdrawal behavior when employees encounter dissatisfaction. Mobley et al. [3] pointed out that turnover intention was the intention of a worker to leave an organization deliberately after a period of time working in a particular organization, after careful consideration, which belonged to voluntary turnover. It is considered as an outcome of affective variables (such as burnout and job satisfaction) rather than actual turnover [4]. That is, turnover intention can predict actual turnover behavior.

An American clinical psychologist first proposed the term “job burnout [5]”. At present, job burnout was defined as the symptoms of practitioners in the service industry who were unable to cope effectively with the continuing pressure at work, included emotional exhaustion, depersonalization and reduced personal accomplishment [6]. Initially, job satisfaction was described as the physical and psychological satisfaction of staff in their work [7]. In later studies, it was defined as the realization of a person’s work values in the work situation, resulting in a pleasurable emotional state [8, 9]. The definition of “the attitude towards one’s work and related emotions, beliefs and behaviors” was used in our study, which not only depended on the nature of the work, but also the personality, attitude and expectation of medical staff [10, 11]. Low job satisfaction is the most common cause of staffs’ turnover and low quality of health care service.

There is a plethora of researches on job satisfaction, burnout and turnover intention. A study found that higher work pressure and lower job satisfaction could easily lead to burnout, that is, job satisfaction was one of the important predictors of burnout [12]. However, another study of American surgeons had the opposite result: burnout is the biggest predictor of job satisfaction [13]. In fact, satisfaction and burnout are in no particular order and they need to be determined on a case-by-case basis. Yin et al. [14] found that satisfaction with work itself, occupational risks and off-duty arrangements were the main factors affecting doctors’ job burnout through a survey on doctors in public hospitals. A study showed that job satisfaction had a direct predictive effect on burnout of middle school teacher, and it also existed as a mediating variable between job stress and job burnout [15]. Arie R and Yoram N had nosed out that negative correlation was existed in job satisfaction and job stress, burnout [16].

Most of researches supported that burnout had a positively strong predictive effect on turnover intention [17, 18, 19, 20, 21, 22]. For example, emotional exhaustion, negative stagnation and other factors related to burnout have a significant positive correlation with turnover intention [23]. Beyond that, a study revealed that job satisfaction was negative correlated with turnover intention [24]. In the study of Guo et al. [25], turnover intention decreased with the increase of job satisfaction, this opinion was verified from a study of medical workers in Macao. And Wu et al. [26] also confirmed this result from a research of clinical nurses in Changsha. Therefore, the impact of job satisfaction on turnover intention is important, direct and negative. Satisfaction has a significant predictive effect on turnover intention.

At present, the medical working environment in China is tense, more and more medical personnel have left their jobs and changed their posts. As a result, there is a shortage of medical staff, which destroys the medical environment and disrupts the medical order and affects the health of the people. A study based on the National Health Service Survey in 2013 on China [27] found that the percentage of employees with low, medium and high turnover intentions was 44.5, 43.7 and 11.8% respectively in primary care institutions. And a foreign study [28] has investigated the turnover intention of nurses, and also found that 54% of them have turnover intention and 35% have turnover behavior. In view of the current shortage of medical personnel in various countries [29], job satisfaction and turnover intention may therefore be a crucial and topic concern.

Most previous research focused on the relationship between job satisfaction, turnover intention and burnout, and exploring the factors affecting the three. However, few studies focus on the mediating effect of a certain factor. Such research in China is still in its infancy. The purpose of the research is to examine the mediating role of job satisfaction in the relationship between burnout and turnover intention.

Methods

Participants and setting

A cross-sectional survey design was used. This quantitative research was conducted in Huangpi District, Wuhan, China. The investigation involved a total of 20 public primary care institutions in Huangpi, including 18 township health centers and 2 community health centers. It conducted from March 2019 to June 2019. The Convenience sampling method was used. Taking into account the scale and level of institutions in rural area, 25 copies of each institutions were determined. Sample size was amplified according to the inefficiency of 10%, and the final expected sample size was 550. Specialized investigators were trained to reduce information bias, inclusion and exclusion criteria was made to reduce selection bias. The inclusion criteria of participants were as follows, (1) Eligible participants had at least 6 months’ work experiences in their own workplace. (2) An employee who had not suffered from mental illness and had not been stimulated by major adverse life events in the near future. (3) Participants were voluntary. And we excluded staffs with a working time of less than 6 months, and employees who were not in the post during the investigation. A flow diagram of participants was shown in Fig. 1 in supplementary materials. There were 1402 questionnaires distributed. Questionnaires with uncompleted answers or suspected unreal answers were excluded. Finally, a total of 1370 of valid samples was collected with 97.72% effective rate. All responses were anonymous to protect the privacy of participants.
Fig. 1

The hypothetical model of the relationship between job satisfaction, burnout and turnover intention

Measures

We used self-administered questionnaires, which were classified into three parts, job satisfaction, burnout and turnover intention. Then we adjusted the questionnaire according to the pilot survey, actual situation and local culture. All the measures were followed the translation and back-translation process from English to Chinese [30]. The three scales all use the 5-point Likert scale. Content of each scale was shown in Table 1 in supplementary materials. The KMO measure and Bartlett’s spherical test were used to test construct validity, it was acceptable if values of KMO measure were greater than 0.50 and p value of Bartlett’s spherical test less than 0.05. Cronbach’s alpha coefficient was calculated to examine internal consistency reliability, values higher than 0.70 were considered satisfactory.
Table 1

Demographic characteristics of the respondents (n = 1370)

Variables

Group

n

%

Means (SD a)

Gender

Male

426

31.09

Female

944

68.91

Age(year) b

< 30

438

31.97

36.98 (9.84)

30–39

305

22.26

40–49

472

34.45

> 49

155

11.31

Occupation

Physician

586

42.77

Nurse

562

41.02

Medical technician

92

6.72

Public health personnel

66

4.82

Pharmacist

34

2.48

Others

30

2.19

Educational background

University and above

423

30.88

College

636

46.42

High school/Technical school

286

20.88

Junior high school and below

25

1.82

Marital status

Married

1059

77.30

Unmarried

270

19.71

Divorced/widowed

41

2.99

Professional title

Senior title

44

3.21

Middle title

207

15.11

Junior title

724

52.85

No title

395

28.83

Monthly income (RMB)

> 5000

55

4.01

4001~5000

274

20.00

3001~4000

519

37.88

2001~3000

413

30.15

< 2000

109

7.96

Hire form

Personnel agent staff

205

14.96

permanent staff

221

16.13

Contract staff

185

13.50

Temporary staff

759

55.40

Working time (hours a week) b

< 30

18

1.31

42.92 (8.48)

31–40

973

71.02

41–50

235

17.15

> 50

144

10.51

Working years b

1–5

404

29.49

14.65 (10.87)

6–10

230

16.79

11–15

134

9.78

15–20

169

12.34

21–25

149

10.88

> 25

287

20.95

Frequency of night shift (times a week)

0

868

63.36

1–3

450

32.85

> 3

52

3.80

Note: a SD, standard deviation, this indicator was calculated only for quantitative data. b represented quantitative data

Job satisfaction scale

Job satisfaction was measured with 18 items selected from the Minnesota Satisfaction Questionnaire (MSQ) [31] and the Job Satisfaction Survey (JSS) [32]. The content of job satisfaction included satisfaction with environment, remuneration, management, the work itself [22, 33]. Sample item includes “The comfort level of the working environment (office environment, greening, lighting) will satisfy you.” (KMO measure =0.957, p < 0.01, Cronbach’s α = 0.970).

Burnout scale

We used 5 items from the Maslach Burnout Inventory (MBI) [34] to measure individual burnout, and aggregated it to measure a positive effect on the burnout. Participants respond to the following items: “I feel that my daily [35] work is meaningless”, “I can’t find a sense of accomplishment at work”, “I feel exhausted when I get off work every day”, “this job has made me indifferent” and “this job makes me feel restless”. (KMO measure =0.857, p < 0.01, Cronbach’s α = 0.925).

Turnover intention scale

The turnover intention questionnaire was designed with reference to turnover intention scale explored by Griffeth [36]. We measured turnover intention using 5 items. Participants respond to the following items: “I had the idea of leaving this organization”, “within a year, I will go to find a new job”, “If there is an opportunity, I will definitely accept a better job”, “I think the employment situation in this organization is very good” and “Currently, I agree to find a good job in the market”. (KMO measure =0.800, p < 0.01, Cronbach’s α = 0.721).

Statistical analysis

Data entry and conversion was completed with EpiData 3.0. Double machine imputing method was used to enter the collected data into the computer. Descriptive analyses were conducted to describe social demographic factors. The structural equation model (SEM) was performed to adjust model fitting, the mediation effect test was carried out by using the bootstrap method. Sobel-Z test was used to verify the significance of mediation effect. Using SPSS 20.0 (IBM Corp, Armonk, NY, USA) and AMOS 24.0 (IBM Corp, Armonk, NY, USA) to analyze data, and p < 0.05 was determined to significant in statistics.

Results

Descriptive statistics

Table 1 showed the sociodemographic characteristics of the respondents. The mean age was 36.98 ± 9.84 years (minimum: 18 years, maximum: 73 years). 426 (31.09%) medical staffs were male and 944 (68.91%) were female. 42.77% of participants were physicians and 41.02% were nurses. The largest number of participants in the 40–49 age group, accounting for 34.45%, while the group over the age of 49 accounts for 11.31%. Most participants (77.30%) were married, 724 (52.58%) had a junior title and 63 % had no night shift in their work.

Structural equation model constructing and fitting

The Hypothetical model was established, as shown in Fig. 1. We have constructed four paths: (1) Path a: Path from independent variable to potential mediator variable, the path coefficient of path a represents the indirect effect of burnout to job satisfaction (Job satisfaction ← Burnout). (2) Path b: The path from potential mediator variable to dependent variable, the path coefficient of path b represents the indirect effect of job satisfaction to turnover intention (Turnover intention ← Job satisfaction). (3) Path c: the path from independent variable to dependent variable, the path coefficient of path c represents the total effect of burnout to turnover intention (Turnover intention ← Burnout). (4) Path c’: Under the influence of potential mediator variables, the path from the independent variable to the dependent variable, the path coefficient of path c’ represents the direct effect of burnout to turnover intention (Turnover intention’ ← Burnout’).

As shown in Table 2. From the results of the hypothetical model operation, we found that all fitting indices did not meet the fitting criteria, indicated that the hypothetical model was not ideal, so we revised the model. The model path was modified according to the amendment advice given by AMOS. We removed some items (A18, B1 and C4) and added lots of bidirectional arrows to make the model fitting better. The final fitting indices results were also shown in Table 2, and the revised standardized path coefficient map was displayed on Fig. 2. After the adjustments, validity and reliability of the three scales remained acceptable. KMO measure, p value for batrtlett’s spherical test and Cronbach’s α for job satisfaction is 0.957, < 0.01 and 0.976, respectively; For turnover intention is 0.857, < 0.01 and 0.910, respectively; For burnout is 0.798, < 0.01 and 0.879, respectively.
Table 2

Comparison of different fitting indices on hypothetical model and adjusted model

Fit index

χ2 a

χ2/df b

GFI c

AGF d

CFI e

NFI f

IFI g

TLI h

RMSEA i

AIC j

Optimum model

2–5

> 0.90

> 0.90

> 0.90

> 0.90

> 0.90

> 0.90

< 0.05

Hypothetical model

9695.143

27.940

0.600

0.533

0.801

0.796

0.802

0.784

0.140

9813.143

Results

unfit

unfit

unfit

unfit

unfit

unfit

unfit

unfit

Adjusted model

1157.059

5.590

0.932

0.901

0.977

0.973

0.977

0.970

0.058

1343.059

Results

acceptable

fit

fit

fit

fit

fit

fit

acceptable

Note: a χ2, Chi-square. b χ2/df, Chi-square divided by degree of freedom. c GFI Goodness of fit index, d AGFI Adjusted goodness of fit index, e CFI Comparative fit index, f NFI Normed fit index, g IFI Incremental fit index. h TLI Tucker-Lewis index. I RMSEA Root mean square error for approximation. j AIC Akaike information criterion

Fig. 2

The revised structural equation modeling of the relationship between job satisfaction, burnout and turnover intention

As shown in Table 3, we estimated the significance of total effect, direct effect and indirect effect by bias-corrected approach. The results showed that the total effect was significant of independent variable (burnout) to dependent variable (turnover intention) (p < 0.01), that was, the total effect of path c was statistically significant. The direct effects of path a (Job satisfaction Burnout) (p < 0.01), path b (Turnover intention ← Job satisfaction) (p < 0.01) and path c’ (Turnover intention’ ← Burnout’) (p < 0.01) were also significant. The results of indirect effect test again proved that path c’ (Turnover intention’ ← Burnout’) was statistically significant. We concluded that this mediating effect was a partial mediating effect.
Table 3

The p-value of significance test of total effect, direct effect and indirect effect by bias-corrected approach

 

Burnout

Job satisfaction

Turnover intention

Total effect

Job satisfaction

0.001

 

Turnover intention

0.001

0.001

Direct effect

Job satisfaction

0.001

Turnover intention

0.001

0.001

Indirect effect

Job satisfaction

Turnover intention

0.001

As shown in Table 4, standardized estimates and its standard errors were calculated by using the bootstrap method. Standardized path coefficient of path a (Job Satisfaction ← Burnout) is − 0.410 and its standard error (Sa) is 0.038. Standardized path coefficient of path b (Turnover intention ← Job satisfaction) is − 0.180 and its standard error (Sb) is 0.028. Standardized path coefficient of path c’ (Turnover intention’ ← Burnout’) is 0.824 and its standard error (Sc′) is 0.029. Looking up the related tables, the standardized path coefficient of path c is 0.899 and its standard error (Sc) is 0.020. Soble-Z test is carried out according to the formula \( \overline{z}=\frac{\mathrm{a}b}{\sqrt{{s_a}^2{b}^2+{s_b}^2{a}^2}} \). Finally, \( \overline{\mathrm{z}} \) = 4.506. According to MacKinnon’s critical value table, the result is p < 0.05, indicating that the mediation effect is significant.
Table 4

Standardized path coefficient and standard error of three main paths by bootstrap

Path

SE

SE-SE

Mean

Bias

SE-Bias

Job Satisfaction

Burnout

0.038

0.001

−0.410

0.000

0.001

Turnover

Job Satisfaction

0.028

0.000

−0.180

−0.002

0.001

Turnover’

Burnout’

0.029

0.000

0.824

−0.002

0.001

Note: SE standard error. SE-SE standard error caused by using bootstrap to estimate standard errors. SE-Bias the standard error of bias

Interpretation of the revised model

Model fit is acceptable if χ2/df ≤ 4.0 [37], GFI > 0.90, AGFI > 0.90, CFI > 0.90, NFI > 0.90, IFI > 0.90 [38], TLI > 0.90 and RMSEA < 0.05. As shown in Table 2, all fit indexes are up to standard, except for χ2/df and RMSEA. Our sample size is larger than 1000, the value of χ2/df is acceptable. In another study, the author points out that 0.05 < RMSEA < 0.08 is also acceptable [39]. Overall, the model fits well and the model is established.

As shown in Fig. 2, the standardized path coefficient of burnout to job satisfaction is − 0.41, indicates that burnout is negatively correlated with job satisfaction (p < 0.01). It shows that when the other conditions are unchanged, the turnover intention decreases by 0.41 units for each additional unit of burnout. The standardized path coefficient of job satisfaction to turnover intention is − 0.18, demonstrates that job satisfaction is also negatively correlated with turnover intention (p < 0.01). Under the same other conditions, the turnover intention decreases by 0.18 units for each additional unit of job satisfaction. The standardized path coefficient of burnout to turnover intention is 0.83, reveals that burnout is positively correlated with turnover intention (p < 0.01). That is, under the influence of job satisfaction, the turnover intention increases by 0.83 units for each additional unit of burnout.

The mediation effect is statistically significant (p < 0.01), and the impact of burnout on turnover intention through the intermediary effect of job satisfaction is 0.074 (a*b = (− 0.180)*-(0.410)). It manifests that when other conditions remain unchanged, the turnover intention will be indirectly increased by 0.074 units for each unit of burnout.

Discussion

Our results demonstrated that for medical workers in primary care institutions, a mediator variable was existed in burnout and turnover intention: job satisfaction. Job satisfaction was usually regarded as a dependent variable [40, 41, 42] or an independent variable [43, 44] in most of the current studies. And work to family conflict [42], work engagement [40], burnout and workload [45] were viewed as mediator variables. However, an American study suggested that burnout was the biggest predictor of job satisfaction [13]. There were few researches focus on job satisfaction as a mediator variable, which provides new ideas for future research. That’s why we try to study how job satisfaction as a mediating variable affects the correlation between burnout and turnover intention.

The results of the study provided further support for the importance of job satisfaction in engaging the workforce and retaining staff to settle the demands and challenges facing health care setting in primary care institutions. Turnover intention was negatively related to job satisfaction and positively related to burnout. Negative correlation was found between job satisfaction and burnout. Some studies conducted in China have also obtained similar results [14, 15, 22, 23, 24, 26, 27, 46], but the correlation coefficient of their results is greater than ours. In our study, the correlation coefficient of path b (Turnover intention ← Job satisfaction) is relatively small, only − 0.18. We speculated that traditional studies which usually used methods such as multivariate linear regression, logistic regression, and ANOVA may neglect the measurement error, so its negative correlation is stronger. However, the error is taken into account in the SEM.

Our finding showed that the mediating effect was a partial mediating effect. First of all, we need to realize what the difference between partial mediation and complete mediation is. In the process of partial mediation, any variable in the causal chain, when it controls the variable before it (including the independent variable), it will affect its subsequent variables significantly. And in the process of complete mediation, after controlling the mediation variables, the influence of the independent variables on the dependent variables is not significant. Job burnout is closely related to turnover intention, and job burnout affects turnover intention directly [17, 18, 19, 20, 21, 22], thus causing a partial mediating effect.

Although the mediation effect test confirms that the existence of job satisfaction as a mediating variable of job burnout affects turnover intention, the mediation effect has a low impact of 7.4%. That is to say, the mediation effect accounts for 7.4% of the variation of dependent variables. Despite the low mediation effect, we still believe that the research is valuable and meaningful. We speculated that the low mediation effect was caused by the following reasons. Firstly, it is related to the choice of independent variables closely. Job burnout includes three dimensions: emotional exhaustion, depersonalization and reduced personal accomplishment [6]. For example, family conflict and doctor-patient relationship are more intuitive than job burnout as important factors that directly affect emotions. If we take them as independent variables directly, the respondent would understand the meaning of the question more clearly, therefore, the path coefficients of path a (mediator variable ← independent variables) would be larger than those of the study (job satisfaction ← burnout), which will lead to a higher mediation effect. Secondly, turnover intention, burnout and job satisfaction are difficult to measure directly. The items and measuring methods may be somewhat various in different studies [47]. Therefore, the difference of instrument selection is also one of the important reasons for the inconsistency of results. Thirdly, the influence of job satisfaction on turnover intention is limited. There are many factors affecting turnover intention. Turnover intention is influenced not only by job satisfaction, but also by social demographic factors such as education [43], years in work [43], family’s relationship [43], monthly income, social support [48], mentoring [49], etc. and other unobserved factors. Therefore, these demographic factors and unobserved factors should be taken into account in the future study.

Our study also has some limitations. Firstly, Because of the diversity of the participants (including doctors, nurses, technicians.), we don’t use the international scale completely. According to the research purpose, the appropriate items have been chosen from these international scales, and the reliability and validity of the questionnaire are still guaranteed. Secondly, sample representativeness needs to be improved. This research can not be generalized to all china as our study place limited. In the future, we will continue to cooperate with other local governments in central China and to conduct similar surveys to solve the problem.

Conclusions

Our study provides a clear understanding of how job satisfaction can mediate the relationship between burnout and turnover intention. In the process of burnout affecting turnover intention, job satisfaction can be regarded as a mediating variable to influence its effect, and the mediating effect was a partial mediating effect. And the mediation effect has a low impact of 7.4%. Turnover intention was negatively related to job satisfaction and positively related to burnout, job satisfaction was negative related to burnout. We can make full use of this relationship to adjust the impact of job burnout on turnover intention by improving job satisfaction. Some operable and useful measures were taken to reduce employee turnover rate and alleviating the current situation of shortage of health personnel in China, such as improving treatment and giving more promotion opportunities for workers to improve job satisfaction, conducting career planning courses and paying attention to employee psychological health to reduce job burnout.

Notes

Acknowledgements

We would like to thank Wuhan University and Huangpi District Center for Disease Control and Prevention for their support of this project, as well as the efforts of team partners in the project.

Authors’ contributions

XyC participated in the survey, the data analysis and the writing of the article. LR took part in the design of the study and the writing of the article. YtZ and JrY contributed to the data collection and screening. HY and SrZ were involved in the data analysis and participated in the literature research. XdT made a second revision of the manuscript. All authors have read and approved the final version.

Funding

Not applicable.

Ethics approval and consent to participate

The ethics committee of Wuhan University School of Medicine (WUSM) reviewed it, and verified it to comply with the Declaration of Helsinki and its revised version, as well as the relevant regulations of biomedical journals, and approved the research (No.2018YF0080). All participants were provided with information about the investigation and gave written informed consent to participate.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Supplementary material

12889_2019_7894_MOESM1_ESM.docx (34 kb)
Additional file 1: Figure S1. A flow diagram of participants.
12889_2019_7894_MOESM2_ESM.docx (17 kb)
Additional file 2: Table S1. Contents of three scales in the questionnaire.

References

  1. 1.
    Bluedorn AC. A taxonomy of turnover. Acad Manag Rev. 1978;3(3):647–51.CrossRefGoogle Scholar
  2. 2.
    Porter LW, Steers RM. Organizational, work, and personal factors in employee turnover and absenteeism. Psychol Bull. 1973;80(2):151–76.CrossRefGoogle Scholar
  3. 3.
    Mobley WH. Intermediate linkages in the relationship between job satisfaction and employee turnover. J Appl Psychol. 1977;62(2):237–40.CrossRefGoogle Scholar
  4. 4.
    Hellman CM. Job satisfaction and intent to leave. J Soc Psychol. 1997;137(6):677–89.CrossRefGoogle Scholar
  5. 5.
    Freudenberger HJ. Staff Burn-Out. J Soc Issues. 2010;30(1):159–65.CrossRefGoogle Scholar
  6. 6.
    Maslach C, Jackson SE. The measurement of experienced burnout. J Organ Behav. 1981;2(2):99–113.CrossRefGoogle Scholar
  7. 7.
    Hoppock R. Age and job satisfaction. Psychol Monographs. 1936;47(2):115–8.CrossRefGoogle Scholar
  8. 8.
    Bussing A, Bissels T, Fuchs V, K-M P. A dynamic model of work satisfaction: qualitative approaches. Hum Relat. 1999;52(8):999–1028.Google Scholar
  9. 9.
    Locke E, Latham G. Work motivation and satisfaction: light at the end of the tunnel. Psychol Sci. 1990;1(4):240–6.CrossRefGoogle Scholar
  10. 10.
    Peters DH, Chakraborty S, Mahapatra P, Steinhardt L. Job satisfaction and motivation of health workers in public and private sectors: cross-sectional analysis from two Indian states. Hum Resour Health. 2010;8(1):27.PubMedPubMedCentralCrossRefGoogle Scholar
  11. 11.
    Wang H, Tang C, Zhao S, Meng Q, Liu X. Job satisfaction among health-care staff in township health centers in rural China: results from a latent class analysis. Int J Environ Res Public Health. 2017;14(10):1101.PubMedCentralCrossRefGoogle Scholar
  12. 12.
    Visser MR, Smets EM, Oort FJ, De Haes HC. Stress, satisfaction and burnout among Dutch medical specialists. Cmaj. 2003;168(3):271–5.PubMedPubMedCentralGoogle Scholar
  13. 13.
    Shanafelt TD, Balch CM, Bechamps GJ, Russell T, Dyrbye L, Satele D, Collicott P, Novotny PJ, Sloan J, Freischlag JA. Burnout and career satisfaction among American surgeons. Ann Surg. 2009;250(3):463–71.PubMedPubMedCentralGoogle Scholar
  14. 14.
    Yin W, Wang Z, Fan Y, Tian J, Qin S, Sun K, Li Y. Analysis of current situation and influencing factors of job burnout of doctors. Chin J Hosp Admin. 2008;24(3):184–7.Google Scholar
  15. 15.
    Xiang G. A study on middle school teachers' job burnout, job stress, job satisfaction and their relationship. Wuhan: Central China Normal University; 2005.Google Scholar
  16. 16.
    Reichel A, Neumann Y. Work stress, job burnout, and work outcomes in a turbulent environment. Int Stud Manag Org. 1993;23(3):75–96.Google Scholar
  17. 17.
    Estryn-Behar M, Van-Der-Heijden BH, Camerino D, Le-Nezet O, Conway P, Fry C, Hasselhorn H. The impact of social work environment, teamwork characteristics, burnout, and personal factors upon intent to leave among European nurses. Med Care. 2007;45(10):939–50.PubMedCrossRefPubMedCentralGoogle Scholar
  18. 18.
    Hongryun W, Hyunhee K, Sangmin P. Burnout and turnover intentions among junior counseling faculty: moderating role of mentoring. J Employ Couns. 2019;56(2):85–94.CrossRefGoogle Scholar
  19. 19.
    Rachel W, Margae K, Beatrice H, Hali H, Coleen K, Kevin G. Burnout and health care workforce turnover. Ann Fam Med. 2019;17(1):36–41.CrossRefGoogle Scholar
  20. 20.
    Shimizu T, Feng Q, Nagata S. Relationship between turnover and burnout among Japanese hospital nurses. J Occup Health. 2005;47(4):334–6.PubMedCrossRefPubMedCentralGoogle Scholar
  21. 21.
    Lee RT, Ashforth BE. A meta-analytic examination of the correlates of the three dimensions of job burnout. J Appl Psychol. 1996;81(2):123–33.PubMedCrossRefPubMedCentralGoogle Scholar
  22. 22.
    Zhang Y. The model study on the relationship between job satisfaction, career burnout and turnover intention among physicians from urban state-owned medical institutions. Shanghai: Fudan University; 2011.Google Scholar
  23. 23.
    Bing Z, Lei Z, Na Z. Research on the relationship between job burnout and turnover intention among clinical nurses born in the 80s. J Nurs Sci. 2013;28(8):16–18.Google Scholar
  24. 24.
    Chang X. Study on the relationship among job satisfaction, Career Burnout and Intent to Stay in General Practitioners. Jinan: Shangdong University; 2015.Google Scholar
  25. 25.
    Guo B. Relevant study on job characteristics, Job Satisfaction and Turnover Intention of Medical Nurses in Macao. Guangzhou: South China Normal University; 2007.Google Scholar
  26. 26.
    Wu L. Study of work satisfaction and turnover intention among clinic nurses in Changsha. Changsha: Central South University; 2007.Google Scholar
  27. 27.
    WangShuai CM. XuLing, MengQun: Analysis of turnover intention of grass-roots medical staff in China. Chin J Health Inform Manag. 2016;13(2):206–13.Google Scholar
  28. 28.
    Gardulf A, Söderström IL, Orton ML, Eriksson LE, Arnetz B, Nordström G. Why do nurses at a university hospital want to quit their jobs? J Nurs Manage. 2010;13(4):329–37.CrossRefGoogle Scholar
  29. 29.
    Guilbert JJ. The world health report 2006: working together for health. Educ Health (Abingdon). 2006;19(3):385–7.CrossRefGoogle Scholar
  30. 30.
    Brislin RW. Back-translation for cross-cultural research. J Cross-Cult Psychol. 1970;1(3):185–216.CrossRefGoogle Scholar
  31. 31.
    Weiss DJ, Dawis RV, England GW. Manual for the Minnesota satisfaction questionnaire. In: Minnesota Studies in Vocational Rehabilitation, vol. 22; 1967.Google Scholar
  32. 32.
    P ES. Job satisfaction. Thousand Oaks: Sage publication; 1997.Google Scholar
  33. 33.
    Meng R, Li J, Zhang Y, Yu Y, Luo Y, Liu X, Zhao Y, Hao Y, Hu Y, Yu C. Evaluation of Patient and Medical Staff Satisfaction regarding Healthcare Services in Wuhan Public Hospitals. Int J Environ Res Public Health. 2018;15(4):769.PubMedCentralCrossRefGoogle Scholar
  34. 34.
    Sueoka N, Nisigaki H, Yonezawa M, Tsukui T, Sakamoto C, Tabuchi M. The factorial validity of the Maslach burnout inventory-general survey (MBI-GS) across occupational groups and nations. J Occup Organ Psychol. 2011;73(1):53–66.Google Scholar
  35. 35.
    Maissiat GS, Lautert L, Pai DD, Tavares JP. Work context, job satisfaction and suffering in primary health care. Revista Gaúcha De Enfermagem. 2015;36(2):42–9.CrossRefGoogle Scholar
  36. 36.
    Griffeth RW, Hom PW. A comparison of different conceptualizations of perceived alternatives in turnover research. J Organ Behav. 1988;9(2):103–11.CrossRefGoogle Scholar
  37. 37.
    Bollen KA, Long JS. TESTING STRUCTURAL EQUATION MODELS. Bms Bulletin Sociol Methodol. 1993;23(39):66–7.Google Scholar
  38. 38.
    Hair JF, Black WC, Babin B, Anderson RE, Tatham RL. Multivariate data analysis. 7th ed. New York: Prentice Hall; 2009.Google Scholar
  39. 39.
    Byrne BM. Structural equation modeling with AMOS. Basic concepts, applications, and programming; 2009.Google Scholar
  40. 40.
    Van Bogaert P, Peremans L, Van Heusden D, Verspuy M, Kureckova V, Van de Cruys Z, Franck E. Predictors of burnout, work engagement and nurse reported job outcomes and quality of care: a mixed method study. BMC Nurs. 2017;16(1):5.PubMedPubMedCentralCrossRefGoogle Scholar
  41. 41.
    Marie-Josée F, Guy G, Jean-Marie B, François C. Associated and mediating variables related to job satisfaction among professionals from mental health teams. Psychiat Quart. 2018;2(89):415.Google Scholar
  42. 42.
    D X, Y Y, S D, Z T, L L, L H. Can Job Control Ameliorate Work-family Conflict and Enhance Job Satisfaction among Chinese Registered Nurses? A Mediation Model. Int J Occup Environ Med. 2018;2(9):97–105.Google Scholar
  43. 43.
    Chen I, Brown R, Bowers BJ, Chang W. Work-to-family conflict as a mediator of the relationship between job satisfaction and turnover intention. J Adv Nurs. 2015;71(10):2350.PubMedCrossRefPubMedCentralGoogle Scholar
  44. 44.
    Billie C, B KL. Impact of job satisfaction components on intent to leave and turnover for hospital-based nurses: a review of the research literature. Int J Nurs Stud. 2007;44(2):297–314.CrossRefGoogle Scholar
  45. 45.
    Van Bogaert P, Clarke S, Willems R, Mondelaers M. Nurse practice environment, workload, burnout, job outcomes, and quality of care in psychiatric hospitals: a structural equation model approach. J Adv Nurs. 2013;69(7):1515–24.PubMedCrossRefPubMedCentralGoogle Scholar
  46. 46.
    Meng J, Wang J. Reasons and countermeasures of clinical nurses'turnover intention. Fam Nur. 2007;5(5):61–2.Google Scholar
  47. 47.
    Pinder CC. Work motivation in organizational behavior. Upper Saddle River: Prentice-Hall; 1998.Google Scholar
  48. 48.
    Duan XJ, Ni X, Shi L, Zhang LJ, Ye Y, Mu HT, Li Z, Liu X, Fan LH, Wang YC. The impact of workplace violence on job satisfaction, job burnout, and turnover intention: the mediating role of social support. Health Qual Life Out. 2019;17(93).  https://doi.org/10.1186/s12955-019-1164-3.
  49. 49.
    Woo H, Kim H, Park S. Burnout and turnover intentions among junior counseling faculty: moderating role of mentoring. J Employment Couns. 2019;56(2):85–94.CrossRefGoogle Scholar

Copyright information

© The Author(s). 2019

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors and Affiliations

  • Xuyu Chen
    • 1
  • Li Ran
    • 1
  • Yuting Zhang
    • 1
  • Jinru Yang
    • 2
  • Hui Yao
    • 1
  • Sirong Zhu
    • 1
  • Xiaodong Tan
    • 1
    Email author
  1. 1.School of Health SciencesWuhan UniversityWuhanChina
  2. 2.School of Clinical MedicineWuhan University of Science and TechnologyWuhanChina

Personalised recommendations