BMJ Open · Complete List of Authors: Luo, Hao; Guangdong Medical University School of Public Health, Department of Environmental and Occupational Health Yang, Hui ; Guangdong Medical

For peer review only

The Relationship between Occupational Stress and Job

Burnout among Rural-to-urban Migrant Workers in

Dongguan, China: A Cross-Sectional Study

Journal: BMJ Open

Manuscript ID bmjopen-2016-012597

Article Type: Research

Date Submitted by the Author: 13-May-2016

Complete List of Authors: Luo, Hao; Guangdong Medical University School of Public Health, Department of Environmental and Occupational Health Yang, Hui ; Guangdong Medical University School of Public Health,

Department of Environmental and Occupational Health Xu, Xiu; Guangdong Medical University School of Public Health, Department of Environmental and Occupational Health Yun, Lin ; Guangdong Medical University School of Public Health, Department of Environmental and Occupational Health Chen, Yu; Guangdong Medical University School of Public Health, Department of Environmental and Occupational Health Xu, Long; Guangdong Medical University School of Public Health, Department of Environmental and Occupational Health Liu, Jia; Guangdong Medical University School of Public Health, Guangdong Medical University School of Public Health Liu, Lin; Guangdong Medical University School of Public Health,

Department of Environmental and Occupational Health Liang, Hai; Guangdong Medical University School of Public Health, Department of Environmental and Occupational Health Zhuang, Ya; Guangdong Medical University School of Public Health, Department of Environmental and Occupational Health Hong, Lie; Guangdong Medical University School of Public Health, Department of Environmental and Occupational Health Chen, Ling ; Guangdong Medical University School of Public Health, Department of Environmental and Occupational Health Yang, Jin; BaoanCenter for Disease Control and Preventionof Shenzhen Tang, Huan; Guangdong Medical University School of Public Health,

Department of Environmental and Occupational Health

<b>Primary Subject Heading</b>:

Epidemiology

Secondary Subject Heading: Public health

Keywords: occupational stress, job burnout, Rural-to-urban Migrant Workers

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The Relationship between Occupational Stress and Job

Burnout among Rural-to-urban Migrant Workers in

Dongguan, China: A Cross-Sectional Study

Hao Luo1,a

, Hui Yang1,a

, Xiujuan Xu1, Lin Yun

1, Yuting Chen

1, Longmei

Xu1, Jiaxian Liu

1, Linhua Liu

1, Hairong Liang

1, Yali Zhuang

1, Liecheng

Hong1, Ling Chen

1, Jinping Yang

2, Huanwen Tang

1*

1Department of Environmental and Occupational Health, Dongguan Key Laboratory

of Environmental Medicine, School of Public Health, Guangdong Medical University,

Dongguan, 523808, PR China

2BaoanCenter for Disease Control and Prevention of Shenzhen, Shenzhen, China

ABSTRACT

Objectives: In China’s labor force, migrant workers have the highest incidences of

occupational diseases. Few studies have addressed the effects of occupational stress

on job burnout among migrant workers in developing countries. This study aimed to

investigate the factors associated with this issue and to analyze the relationship

between occupational stress and job burnout among migrant workers.

Design: Cross-sectional survey.

Setting: Dongguan, Guangdong Province, China.

Participants: The participants were 4464 migrant workers from Dongguan in

Guangdong Province.

Primary and secondary outcome measures: Multistage sampling procedures were

used to demographic characteristics, behavior customs and job-related data.

Hierarchical linear regression and logistic regression models were performed to

explore the relationship between occupational stress and burnout.

Results: Demographics, behavior customs and job-related characteristics had

significant effects on burnout. After adjusting for the control variable, a high level of

emotional exhaustion was significantly associated with high role overload, high role

insufficiency, high role boundary, high physical environment, high psychological

a. These authors contributed equally to this study

*Correspondence to Huan-wen Tang; [email protected]

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strain, high physical strain, low role ambiguity, low responsibility and low vocational

strain. A high level of depersonalization was associated with high role overload, high

role ambiguity, high role boundary, high interpersonal strain, high recreation, low

physical environment and low social support. A low level of personal accomplishment

was associated with high role boundary, high role insufficiency, low responsibility,

low social support, low physical environment, low self-care and low interpersonal

strain.

Conclusions: Occupational stress was associated with job burnout among migrant

workers in China. Relieving occupational stress and maintaining an appropriate

quantity and quality of work could be an important measure to prevent migrant

workers’ job burnout.

Strengths and limitations of this study

■This study is one of the first to examine the relationship between occupational stress

and job burnout among Rural-to-urban Migrant Workers in China.

■However, this study was a cross-sectional design, there may be an inherent bias in

self-report measures.

INTRODUCTION

Stress is an important element of life, and appropriate levels of stress can help an

individual overcome challenging situations. However, if not managed well, high

levels of stress will result in emotional problems and ill health1. Stress-related

symptoms range from mild medical unfitness to general unhappiness and anxiety to

more serious conditions, including drug dependency, excessive drinking, increased

smoking, divorce, psychiatric problems and suicide2. Stress has been found to be

strongly associated with job satisfaction; increased levels of stress can lead to reduced

job satisfaction. Occupational stress is defined as harmful physical and emotional

responses incurred in the work environment. As jobs have shifted from manufacturing

industries to service industries, the psychological and emotional demands of work

have increased, which has led to an increased awareness of work-related burnout3.

Burnout is described as a prolonged response to chronic physical, emotional and

mental exhaustion at work, and it is characterized by emotional exhaustion

(EE),depersonalization (DEP) and reduced personal accomplishment (PA) 45

. Burnout

has been recognized as an occupational hazard and is associated with physical illness

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and mental problems, including cardiovascular disease, musculoskeletal pain,

depression and anxiety67

. Additionally, burnout is associated with absenteeism, both

intention to leave the job and actual turnover, and lower productivity, job performance

and involvement7-9

.

Rural-to-urban migrant workers, those who migrate from the rural areas of their

original residence to urban areas, are a unique phenomenon occurring as China

experiences an economic transformation. Over the past three decades, China’s

economic development has produced the largest human migration in history. By 2012,

the number of migrant workers had reached 263 million10

. Most migrant workers

gathered in economically developed areas such as the Pearl River Delta in Guangdong

Province. Dongguan lies in the Pearl River Delta, and many of its small and

medium-sized enterprises were assembled by migrant workers. Although migrant

workers have become a vital labor supply to Dongguan’s economy, this population

lacks protection, particularly with regard to health. Several studies showed that

because of their exposure to poor working conditions, occupational hazards and long

working hours1112

, migrant workers have become the group in China’s labor force

with the highest incidences of occupational diseases13

. Although working conditions

have improved with modernization, higher levels of knowledge and productivity are

now being required for migrant workers. Under these circumstances, the degree of

acceptance felt in a new home, relationships with co-workers, job-related and

behavior customs, interpersonal tensions and conflicts may lead to occupational stress

for migrant workers. Meanwhile, evidence from earlier studies suggests a positive

association between the employees’ health and burnout 1415

, whereas demographics

and job-related characteristics may also be important factors influencing the level of

burnout1516

. Additionally, occupational stress has been strongly associated with

burnout 1718

. However, there have not been any studies on the relationship between

occupational stress and burnout among rural-to-urban migrant workers. Because this

population is unique and its numbers are increasing, it is necessary to investigate the

relationships between occupational stress and burnout. Here, we performed a

cross-sectional study among rural-to-urban migrant workers in Dongguan to (1) assess

the relationship between migration characteristics and burnout among rural-to-urban

migrant workers; and (2) assess the association between occupational stress and

burnout. The results could help deepen our understanding of the factors associated

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with burnout and underscore the need to establish policies to protect rural-to-urban

migrant workers’ health.

METHODS

Study Design and Subjects

This cross-sectional survey was performed among migrant workers in Dongguan,

China. The participants in this study were selected using a multi-stage, stratified

sampling method. In the first stage, two towns were randomly selected based on both

economics and the proportion of migrants. In the second stage, we randomly selected

factories in electronics, shoe making, the chemical industry and furniture by applying

computer-generated random numbers to the town’s list of factories. In the third stage,

workers were randomly selected for participation from the sampled factories. All of

the participants were informed about the study and were invited to fill out an

anonymous self-administered questionnaire between Marchand May of 2013. The

inclusion criteria for participation were as follows: (1) between 18-60 years old; (2)

without a local household registration in Dongguan; (3) having left their original

home and resided in Dongguan for at least six months; and (4) willingness to provide

verbal informed consent. The exclusion criteria were individuals who might have

difficulty understanding and answering the questionnaire, even with the help of

facilitators. Initially, there were 4,500rural-to-urbanmigrant workers who were

available and who met the criteria. Formal consent was given by 4,463 respondents

(99.17%) who completed the questionnaires. Six hundred sixty-three cases with

missing data were excluded from the 4,463 acquired data sets, resulting in a total of

3,806 questionnaire data sets used in the final analysis. The overall complete response

rate was 84.58%.This study was obtained under a protocol approved by the

Guangdong Medical University Ethics and Human Subjects Committee. Written

informed consent was obtained from all of the study participants.

Measurement of Occupational Stress

Occupational stress was assessed using the Occupational Stress Inventory—revised

edition (OSI-R) written by Osipow and adapted by Li19

. The scale consists of the

following three dimensions: the Occupational Role Questionnaire (ORQ) (60 items),

the Personal Strain Questionnaire (PSQ) (40 items) and the Personal Resources

Questionnaire (PRQ) (40 items).The respondent scores the frequency of a particular

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behavior on a Likert scale from 1 (never) to 5 (often)20

. For the ORQ and PSQ, a

higher score indicates more nervousness. A higher score for the PRQ indicates that

the respondent has a stronger ability to cope with tension. In each dimension, we

divided the subjects into tertilesoflow, moderate and high groups. The following were

the cutoff points for the OSI-R subscales: ORQ: low <120, moderate 120-160,

high >160; PSQ and PRQ: low <92, moderate 92-100, high >100. In our sample,

Cronbach’s alpha coefficients for the ORQ, PSQ and PRQ were 0.88, 0.86 and 0.91,

respectively.

Measurement of Job Burnout

Job burnout was measured by the Chinese version of the Maslach Burnout Inventory

(MBI-HSS), which consisted of 19 test items divided into three dimensions5721

: 7

items of emotional exhaustion (EE), 5 items of depersonalization (DEP) and 7 items

of reduced sense of personal accomplishment (PA). The first dimension (EE)

describes feelings in a general sense, DEP is associated with behavior and PA

involves cognition and feelings affecting self-efficacy22

. Some item statements are

reversed. Items were scored on a seven-point Likert scale ranging from 1 (strongly

disagree) to 7 (strongly agree). The scores for each dimension were computed

separately. Scores of EE and DEP higher than 66.7%, together with PA scores lower

than 33.3%, were used to code low and high scores. A higher burnout level is

predicted by higher scores for the EE and DEP subscales and by lower scores for the

PA scale. The MBI-HSS has previously demonstrated that it has high validity and

reliability among Chinese medical professionals23

. In our study, the Cronbach’s alpha

for EE, DEP and PA was 0.70, 0.77 and 0.74, respectively.

Additional Questions

A questionnaire composed of demographic variables such age, gender, marital status,

education level, behavior customs including smoking, drinking and physical exercise,

and job-related data such as type of workplace and years of practice was developed

for the purpose of the study.

Statistical Analysis

The data were analyzed using SPSS for Windows Version 19.0(SPSS Inc., USA). All

of the statistical tests were two-sided, and a P value<0.05 was considered statistically

significant. The independent-sample t-test or one-way analysis of variance (ANOVA)

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was used to compare the means of the MBI-HSS scores in the demographics, behavior

customs and job-related data. Pearson’s correlation coefficients were used to examine

the correlations among the study variables. Multiple regression analysis using the

hierarchical stepwise method was performed to examine the association between

occupational stress and MBI-HSS scores. In the first step of the hierarchical linear

regression analyses, the control variables were added into the model. According to the

independent-sample T-test and one-way ANOVA analysis, variations such as gender,

age, marital status, educational level, smoking, drinking, physical training, hobbies,

type of workplace, years of practice, working hours per day and dull or repetitive

work were included in the model as potential confounders. In the second step,

dimensions of OIS-R were added. Variances of MBI-HSS scores explained by

occupational stress were examined byΔR2. A non-conditional logistic regression

model was applied to estimate the degree of association between each dimension of

occupational stress and the MBI-HSS.

RESULTS

Participant Characteristics

Of the 3,806 respondents in the study, the average age was 31.35±7.60 years, and the

majority (77.96%) of the study participants were over the age of 25. Over half

(52.60%) of the participants were male. In total, 66.55% of the respondents were

married, and only 13.56% of rural-to-urban migrant workers had a junior college

degree or greater. Most of the participants had good behavior customs: approximately

three-quarters (75.60%) avoided liquor and smoking, 72.75% had never smoked

cigarettes, 46.24% engaged in physical exercise weekly and 72.77% had an avocation.

Among all of the respondents, 77.04% worked during the day, 41.51% had worked for

5 years or less, 62.61% worked between 8 and 10 h per day, 61.56% worked

monotonously and 62.09% vented their troubles when they faced work pressure.

Factors associated with Job Burnout

Table 1 provides the mean differences in burnout subscales according to the

respondent’s demographics, behavior customs and job-related characteristics. Marital

status, education level, physical exercise, avocation, workplace type, working hours

per day and monotonous work were all associated with an emotional exhaustion score.

Rural-to-urban migrant workers who were widowed/divorced had higher emotional

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exhaustion scores than those who were single or married (P<0.05). Rural-to-urban

migrant workers with a junior school degree or below had higher emotional

exhaustion scores than the other subjects (P<0.05). Rural-to-urban migrant workers

who engaged in physical exercise every week and had an avocation had lower

emotional exhaustion scores than the other respondents (P<0.05). Rural-to-urban

migrant workers who worked during the day had lower emotional exhaustion scores

compared to shift workers (P<0.05). Rural-to-urban migrant workers with

monotonous work and who worked≥10 h per day had higher emotional exhaustion

scores than the other participants (P<0.05).

In the dimension of depersonalization, the mean differences regarding venting

one’s troubles when faced with work pressure were not statistically significant.

Female migrant workers and those over 40 years of age had lower depersonalization

scores compared to the other subjects (P<0.05). Rural-to-urban migrant workers with

a junior school degree or below and who were widowed/divorced had higher

depersonalization scores than the other respondents (P<0.05). Rural-to-urban migrant

workers who had good behavior customs, worked during the day, had been working

≤5 years, worked <8 h per day and whose work was not repetitive had lower

depersonalization scores compared to the other respondents (P<0.05).

In the case of personal accomplishment, the mean differences in gender, smoking,

physical exercise, avocation, workplace type, working hours per day and monotonous

work were statistically significant. Male migrant laborers had higher personal

accomplishment scores than females (P<0.05). Rural-to-urban migrant workers who

smoked, engaged in physical exercise weekly and had an avocation had higher

personal accomplishment scores than the other respondents (P<0.05). Rural-to-urban

migrant workers who worked during the day had higher personal accomplishment

scores compared to shift workers (P<0.05). Rural-to-urban migrant workers who

worked≥10 h per day and whose work was not repetitive had higher personal

accomplishment scores than the other participants (P<0.05).

Correlation between Study Variables

Table 2 lists the correlations among the MBI-HSS, OR, PS and PR variables. Two

dimensions of job burnout (EE and DEP) were positively correlated with every item

of OR and PS and negatively correlated with PR. PA was positively correlated with

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two items of OR (responsibility and physical environment) and two items of PS

(psychological strain and interpersonal strain) and PR, but was negatively correlated

with the other items of OR and one item of PS (vocational strain). There was no

significant correlation between PA and physical strain.

The Relationship between Occupational Stress and Job Burnout

Table 3 presents the results of the hierarchical linear regression analyses. After

adjusting for the control variable, a positive association between emotional exhaustion

and role overload, role insufficiency, role boundary, physical environment,

psychological strain and physical strain was observed. However, emotional

exhaustion was significantly negatively associated with role ambiguity, responsibility

and vocational strain. Role overload, role ambiguity, role boundary, interpersonal

strain and recreation positively predicted depersonalization, where as physical

environment and social support were negatively associated with depersonalization. A

low level of personal accomplishment was associated with high role boundary, high

role insufficiency, low responsibility, low social support, low physical environment,

low self-care and low interpersonal strain (in descending order of standardized

estimates).The results demonstrate that demographics, behavior customs and

job-related variables explained 17.6, 13.4and 14.6% of the variance in emotional

exhaustion, depersonalization and personal accomplishment, respectively.

Occupational stress was responsible for 14.1, 15.7 and 11.6% of the variance in

emotional exhaustion, depersonalization and sense of personal accomplishment,

respectively.

The results of the logistic regression are presented in Table 4. Exposure to low job

strain decreased the risk of high emotional exhaustion, high depersonalization and low

sense of personal accomplishment. Migrant workers with low job strain and moderate

job strain had 0.242 and 0.787timesthe risk of presenting emotional exhaustion,

respectively, compared to rural-to-urban migrant workers with high job strain.

Respondents with low job strain and moderate job strain had 0.333 times and 0.254

times the risk of depersonalization, respectively, compared to respondents with high

job strain. Additionally, subjects with low job strain and moderate job strain had

0.664 and 0.431 times the risk of presenting personal accomplishment, respectively,

compared to migrant workers with high job strain, whereas exposure to low personal

strain decreased the risk of high emotional exhaustion, high depersonalization and low

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levels of personal accomplishment. Rural-to-urban migrant workers with low personal

strain and moderate personal strain had 0.512 and 0.619 times the risk of emotional

exhaustion, respectively, compared to respondents with high personal strain.

Respondents with low and moderate job strain had 0.609 and 0.499 times the risk of

depersonalization, respectively, compared to respondents with high personal strain;

those with moderate personal strain had 0.791 times the risk of personal

accomplishment compared to migrant workers with high personal strain. Meanwhile,

exposure to high personal resources decreased the risk of high emotional exhaustion,

high depersonalization and low sense of personal accomplishment.

DISCUSSION

In this study, we examined the relationship between occupational stress and job

burnout among rural-to-urban migrant workers in China. The results indicated that

burnout was associated with occupational stress in this population. Reducing

occupational stress could be an important measure to prevent migrant workers’ job

burnout.

Demographic characteristics including gender, age, marital status and education

level might be factors influencing the three dimensions of burnout. In our study, males

had more depersonalization than females, which was consistent with previous

studies24

. Male migrant workers had higher levels of personal accomplishment than

female migrant workers, which is likely because men take on few household

responsibilities and tasks in dual-career family life14

. Migrant workers between 25 and

40 years of age had a higher level of depersonalization. People in this age group make

up the core task force, which can be assigned more tasks, and the greater burden can

leave people feeling apathetic. Several studies support our findings that

widowed/divorced migrant workers had more emotional exhaustion and

depersonalization than single and married workers, which might be related to the

social support provided by a partner1425

. Education level had significant effects on the

emotional exhaustion and depersonalization of migrant workers. Rural-to-urban

migrant workers with a higher education level had low levels of burnout, which is

likely because migrant workers with lower education accept unskilled jobs.

Behavior customs such as smoking, drinking, physical exercise and avocation had

significant effects on burnout. Rural-to-urban migrant workers with good behavior

customs had a lower level of burnout, which is likely because respondents with good

behavior customs were able to cope more easily with the strain faced at work.

Considering the burnout associated with job-related characteristics, burnout varied

significantly based on whether or not the respondents worked overnight, whether or

not they worked monotonously, the number of years of practice and the number of

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hours worked per day. A similar result was observed in previous studies152627

, in

which high burnout was related to heavy workload and job-related stress.

Occupational stress plays an important role in the burnout of migrant workers. Our

study showed that the three dimensions of occupational stress were correlated with the

three dimensions of job burnout. Rural-to-urban migrant workers who experienced

high role overload, high role boundary, low responsibility, high psychological strain

and high physical strain had a higher risk of developing high emotional exhaustion,

depersonalization and a low sense of personal accomplishment. Migrant workers with

high role overload and role boundary spend too much time and energy completing

their performance goals; thus, they lack the time to relieve themselves of the

competitive pressures they face. Furthermore, rural-to-urban migrant workers with

high psychological and physical strain feel continuously nervous. These factors may

contribute to their high emotional exhaustion and depersonalization as well as their

low level of personal accomplishment. Social support improves the ability to manage

stress and is effective in reducing burnout2829

. The hierarchical linear regression

analysis shows that those with low social support had a higher risk of developing high

depersonalization and a low sense of personal accomplishment, which is likely

because social support may buffer the effects of stress3031

. The present study indicated

that job strain, personal strain and personal resources were another predictor of job

burnout. High job strain is significantly associated with the dimensions of burnout.

Rural-to-urban migrant workers work for a long time and are overloaded, which may

lead to physical illness and mental problems. Meanwhile, the pessimism with regard

to facing work pressure and the ability to vent work stress may also lead to anxiety

and depression. The effects on both physical and mental health can easily lead to

burnout. Our study indicated that low personal resources was a risk of burnout, which

is related to the fact that such individuals rarely engage in recreational activities and

lack social support.

Limitations of this study

Because our study had a cross-sectional design, there were inevitably some

limitations. First, the data did not provide any causal associations between

occupational stress and job burnout. Second, the data were obtained by self-reporting;

thus, there is a potential for inflated relationships because of common method bias.

Third, the study population was limited to staff from two towns, which can also

introduce bias. All of the findings obtained in the present study must be confirmed in

future prospective studies.

CONCLUSION

In conclusion, this study found that occupational stress was associated with job

burnout among migrant workers. Strategies to decrease occupational stress as well as

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maintain an appropriate quantity and quality of work could be crucial to preventing

job burnout of migrant workers in China.

Acknowledgments The authors wish to thank the staff at Guangdong Occupational

Health Association for their support. We also thank all of the participants in this study

for their voluntary participation.

Contributors Hao Luo and Hui Yang are the main authors and assisted with

questionnaire development and distribution; and data collation and analysis. Xiujuan

Xu performed the statistical analyses. Lin Yun, Yuting Chen, Longmei Xu, and

Jiaxian Liu contributed to questionnaire development and distribution. Linhua Liu,

Hairong Liang, Yali Zhuang, Liecheng Hong, Ling Chen, and Jinping Yang

contributed to acquisition of data. Huanwen Tang conceived the study and design and

questionnaire development.

Funding This study was supported by the National Natural Science of China

(81273116), the Guangdong Provincial Natural Science Foundation

(S2013010015153), the Key Project of the Science and Technology Program of

Dongguan Bureau of Science and Technology, China (2012108101011), the Science

and Technology Program of Zhanjiang Bureau of Science and Technology, China

(2013B01082) and the Science Foundation of Guangdong Medical University

(XK1414).

Competing interests The authors declare that they have no conflicts of interest.

Ethics approval This study was approved by the Medical Ethics Committee of

Guangdong Medical University.

Data sharing statement No additional data available.

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China 2012.


China 2011.

13 Zhang X, Wang Z, Li T. The current status of occupational health in China.

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14 Wang Y, Ramos A, Wu H, et al. Relationship between occupational stress and

burnout among Chinese teachers: a cross-sectional survey in Liaoning, China. Int

Arch Occup Environ Health 2015; 88: 589-597.

15 Jin MU, Jeong SH, Kim EK, et al. Burnout and its related factors in Korean

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16 Tijdink JK, Vergouwen AC, Smulders YM. Emotional exhaustion and burnout

among medical professors; a nationwide survey. BMC Med Educ 2014; 14: 183.

17 Escriba-Aguir V, Martin-Baena D, Perez-Hoyos S. Psychosocial work

environment and burnout among emergency medical and nursing staff. Int Arch

Occup Environ Health 2006; 80: 127-133.

18 Wu S, Zhu W, Wang Z, et al. Relationship between burnout and occupational

stress among nurses in China. J Adv Nurs 2007; 59: 233-239.

19 OSIPOW SH. Occupational Stress Inventory Revised Edition. Odessa:

Psychological Assessment Resources Inc 1998; 1-10.

20 Hayne AN, Gerhardt C, Davis J. Filipino nurses in the United States: recruitment,

retention, occupational stress, and job satisfaction. J Transcult Nurs 2009; 20:

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21 Boles JS DD, Rieks JM,eta. The dimensionality of the Maslach Burnout Inventory

across small business owners and Edueator. J Voc Beh 2000; 56: 12-34.

22 Nowakowska-Domagala K, Jablkowska-Gorecka K,

Kostrzanowska-Jarmakowska L, et al. The Interrelationships of Coping Styles and

Professional Burnout Among Physiotherapists: A Cross-Sectional Study.

Medicine (Baltimore) 2015; 94: e906.

23 Wu S, Li H, Zhu W, et al. Effect of work stressors, personal strain, and coping

resources on burnout in Chinese medical professionals: a structural equation

model. Ind Health 2012; 50: 279-287.

24 Lin QH, Jiang CQ, Lam TH. The relationship between occupational stress,

burnout, and turnover intention among managerial staff from a Sino-Japanese joint

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25 Bauer J, Stamm A, Virnich K, et al. Correlation between burnout syndrome and

psychological and psychosomatic symptoms among teachers. Int Arch Occup

Environ Health 2006; 79: 199-204.

26 Linzer M, Visser MR, Oort FJ, et al. Predicting and preventing physician burnout:

results from the United States and the Netherlands. Am J Med 2001; 111: 170-175.

27 Goehring C, Bouvier Gallacchi M, Kunzi B, et al. Psychosocial and professional

characteristics of burnout in Swiss primary care practitioners: a cross-sectional

survey. Swiss Med Wkly 2005; 135: 101-108.

28 Prag PW. Stress, burnout, and social support: a review and call for research. Air

Med J 2003; 22: 18-22.

29 Baruch-Feldman C, Brondolo E, Ben-Dayan D, et al. Sources of social support

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30 Ward L. Mental health nursing and stress: maintaining balance. Int J Ment Health

Nurs 2011; 20: 77-85.

31 Hughes H, Umeh K. Work stress differentials between psychiatric and general

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Table 1 Univariate analysis of the MBI-HSS score according to demographics, behavior

customs and job-related characteristics

Variable n Emotional exhaustion Depersonalization Personal accomplishment

Mean ± SE P-value Mean ± SE P-value Mean ± SE P-value

Gender

Male 2,017 28.05±5.41 0.87 17.34±3.96 0.049 32.84±6.42 0.000

Female 1,789 28.02±5.38 17.09±3.85 31.83±5.93 Age <25 839 27.97±5.33 0.502 17.22±3.81 0.000 32.33±6.08 0.157

25-40 2,462 28.11±5.43 17.42±3.91 32.27±6.37 >40 505 27.82±5.40 16.31±3.96 32.86±5.62 Marital status Single 1,216 28.00±5.39 0.034 17.44±3.74 0.012 32.30±6.19 0.061

Married 2,533 28.01±5.37 17.11±3.98 32.44±6.22 Widowed/divorced 57 29.88±6.17 18.11±4.05 30.51±6.26

Education level Junior school or below 1,466 28.38±5.57 0.009 17.10±4.01 0.002 32.21±6.15 0.205

High school 1,824 27.82±5.37 17.17±3.91 32.55±6.34 Junior college or above 516 27.83±4.92 17.78±3.55 32.14±5.96 Smoking Yes 1,037 28.06±5.34 0.893 17.47±3.97 0.020 32.75±6.65 0.024

No 2,769 28.03±5.41 17.14±3.89 32.22±6.04 Drinking Yes 929 28.29±5.38 0.101 17.74±3.97 0.000 32.56±6.54 0.282

No 2,877 27.96±5.40 17.06±3.88 32.30±6.10 Physical exercise Yes 1,760 27.32±5.33 0.000 17.05±4.21 0.011 32.92±6.98 0.000

No 2,046 28.66±5.37 17.38±3.64 31.89±5.43 Avocation Yes 2,770 27.67±5.43 0.000 17.00±3.95 0.000 32.70±6.45 0.000

No 1,036 29.03±5.15 17.83±3.75 31.48±5.46 Workplace type Fixed 2,932 26.94±4.98 0.000 16.53±3.72 0.000 33.68±5.72 0.000

Shift 874 31.72±5.12 19.57±3.63 28.29±6.08 Years of practice ≤5 years 1,580 27.80±5.34 0.059 16.91±3.92 0.000 32.57±5.95 0.175

6-10 years 1,043 28.14±5.20 17.51±3.73 32.13±6.24 >10 years 1,183 28.04±5.40 17.40±4.03 32.30±6.53 Working hours per day <8 hours 610 27.46±5.54 0.000 16.85±4.00 0.008 32.14±6.57 0.001

8-10 hours 2,383 27.95±5.34 17.37±3.92 32.16±6.21 ≥10 hours 813 28.74±5.40 17.10±3.79 33.08±5.91 Monotonous work Yes 2,343 28.62±5.54 0.000 17.46±3.85 0.000 32.01±6.10 0.000

No 1,463 27.10±5.02 16.85±3.98 32.93±6.36 Facing work pressure,

whether vent troubles

Yes 2,363 27.91±5.35 0.063 17.28±4.02 0.275 32.32±6.49 0.585

No 1,443 28.25±5.46 17.14±3.73 32.43±5.74

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Table 2 Correlation coefficients among dimensions of MBI-HSS scores and OSI-R

Variable Emotional exhaustion Depersonalization Personal accomplishment Occupational role (OR) Role overload 0.281** 0.284** -0.071** Role insufficiency 0.323** 0.227** -0.213** Role ambiguity 0.116** 0.363** -0.294** Role boundary 0.194** 0.386** -0.155** Responsibility 0.038* 0.116** 0.145** Physical environment 0.295** 0.130** 0.055**

Personal strain (PS) Vocational strain 0.118** 0.141** -0.038* Psychological strain 0.195** 0.155** 0.043** Interpersonal strain 0.109** 0.092** 0.111** Physical strain 0.205** 0.208** 0.012 Personal resources (PR) Recreation -0.104** -0.052** 0.196** Self-care -0.138** -0.088** 0.232** Social support -0.169** -0.280** 0.303** Rational coping -0.127** -0.203** 0.244**

* P<0.05; ** P<0.01

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Table 3 Hierarchical linear regression analyses of the factors associated with MBI-HSS

scores

* P<0.05; ** P<0.01

Variable Emotional exhaustion Depersonalization Personal accomplishment

Step 1

(β) Step 2 (β) Step 1 (β) Step 2 (β) Step 1 (β) Step 2 (β)

Gender -0.094 -0.193 -0.067 -0.073 -0.862** -0.957**

Age -0.182 -0.011 -0.474** -0.359** 0.334 0.220

Marital status 0.083 0.081 0.177 -0.095 -0.198 0.089

Education level -0.175 -0.322** 0.273** -0.012 0.009 0.092

Smoking 0.082 0.135 -0.031 0.155 -0.113 0.045

Drinking -0.399 0.245 -0.587** -0.121 0.297 0.064

Physical exercise 0.998** 0.579** 0.153 0.363** -0.639* -0.270

Avocation 0.574** 0.360* 0.628** 0.216 -0.582* -0.013

Workplace type 4.622** 3.910** 2.979** 2.428** -5.117** -4.324**

Years of practice 0.362** 0.292** 0.414** 0.310** -0.3576** -0.400**

Working hours per day 0.560** 0.237 0.096 0.185* 0.509** 0.111

Monotonous work -1.293** -0.818** -0.421** -0.257* 0.724** 0.551**

Role overload 0.167** 0.045** 0.009

Role insufficiency 0.197** 0.017 0.006

Role ambiguity -0.144** 0.070** -0.106**

Role boundary 0.082** 0.149** -0.151**

Responsibility -0.052** -0.001 0.104**

Physical environment 0.073** -0.025** 0.072**

Vocational strain -0.087** -0.013 -0.040

Psychological strain 0.096** 0.003 0.005

Interpersonal strain -0.011 0.023* 0.047*

Physical strain 0.055** 0.015 0.016

Recreation 0.003 0.028** 0.014

Self-care -0.026 0.018 0.061**

Social support -0.018 -0.072** 0.099**

Rational coping 0.009 0.001 0.13

R2 0.176** 0.317** 0.134** 0.291** 0.146** 0.262**

△R2 0.176** 0.141** 0.134** 0.157** 0.146** 0.116**

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Table 4 ORs of job burnout by job strain and personal strain

Variable Emotional exhaustion OR (95% CI)

Depersonalization OR (95% CI)

Personal accomplishment OR (95% CI)

Job Strain Low 0.242 (0.183, 0.320)** 0.333 (0.267, 0.416)** 0.664 (0.540, 0.816)** Moderate 0.787 (0.678, 0.913)** 0.254 (0.217, 0.297)** 0.431 (0.370, 0.502)** High 1.000 1.000 1.000

Personal Strain Low 0.512 (0.434, 0.604)** 0.609 (0.520, 0.714)** 1.085 (0.930, 1.265) Moderate 0.619 (0.573, 0.833)** 0.499 (0.410, 0.608)** 0.791 (0.654, 0.956) * High 1.000 1.000 1.000

Personal Resources Low 1.324 (0.992, 1.767) 2.807 (2.134, 3.693)** 4.349(3.280, 5.765)** Moderate 1.999 (1.478, 2.689)** 2.088 (1.553, 2.807)** 5.009 (3.681, 6.817)** High 1.000 1.000 1.000

* P<0.05; ** P<0.01

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STROBE 2007 (v4) Statement—Checklist of items that should be included in reports of cross-sectional studies

Section/Topic Item

# Recommendation Reported on page #

Title and abstract 1 (a) Indicate the study’s design with a commonly used term in the title or the abstract 1

(b) Provide in the abstract an informative and balanced summary of what was done and what was found 1

Introduction

Background/rationale 2 Explain the scientific background and rationale for the investigation being reported 2-3

Objectives 3 State specific objectives, including any prespecified hypotheses 3-4

Methods

Study design 4 Present key elements of study design early in the paper 4

Setting 5 Describe the setting, locations, and relevant dates, including periods of recruitment, exposure, follow-up, and data

collection

4

Participants

6

(a) Give the eligibility criteria, and the sources and methods of selection of participants 4

Variables 7 Clearly define all outcomes, exposures, predictors, potential confounders, and effect modifiers. Give diagnostic criteria, if

applicable

4-5

Data sources/

measurement

8* For each variable of interest, give sources of data and details of methods of assessment (measurement). Describe

comparability of assessment methods if there is more than one group

4-5

Bias 9 Describe any efforts to address potential sources of bias 5

Study size 10 Explain how the study size was arrived at 4

Quantitative variables 11 Explain how quantitative variables were handled in the analyses. If applicable, describe which groupings were chosen and

why

Statistical methods 12 (a) Describe all statistical methods, including those used to control for confounding 5-6

(b) Describe any methods used to examine subgroups and interactions 5-6

(c) Explain how missing data were addressed 5-6

(d) If applicable, describe analytical methods taking account of sampling strategy 5-6

(e) Describe any sensitivity analyses 5-6

Results

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Participants 13* (a) Report numbers of individuals at each stage of study—eg numbers potentially eligible, examined for eligibility,

confirmed eligible, included in the study, completing follow-up, and analysed

6

(b) Give reasons for non-participation at each stage 6

(c) Consider use of a flow diagram 6

Descriptive data 14* (a) Give characteristics of study participants (eg demographic, clinical, social) and information on exposures and potential

confounders

6

(b) Indicate number of participants with missing data for each variable of interest 6

Outcome data 15* Report numbers of outcome events or summary measures 6

Main results 16 (a) Give unadjusted estimates and, if applicable, confounder-adjusted estimates and their precision (eg, 95% confidence

interval). Make clear which confounders were adjusted for and why they were included

6-9

(b) Report category boundaries when continuous variables were categorized 6-9

(c) If relevant, consider translating estimates of relative risk into absolute risk for a meaningful time period 6-9

Other analyses 17 Report other analyses done—eg analyses of subgroups and interactions, and sensitivity analyses

Discussion

Key results 18 Summarise key results with reference to study objectives 9

Limitations 19 Discuss limitations of the study, taking into account sources of potential bias or imprecision. Discuss both direction and

magnitude of any potential bias

10

Interpretation 20 Give a cautious overall interpretation of results considering objectives, limitations, multiplicity of analyses, results from

similar studies, and other relevant evidence

9-10

Generalisability 21 Discuss the generalisability (external validity) of the study results 9-10

Other information

Funding 22 Give the source of funding and the role of the funders for the present study and, if applicable, for the original study on

which the present article is based

11

*Give information separately for cases and controls in case-control studies and, if applicable, for exposed and unexposed groups in cohort and cross-sectional studies.

Note: An Explanation and Elaboration article discusses each checklist item and gives methodological background and published examples of transparent reporting. The STROBE

checklist is best used in conjunction with this article (freely available on the Web sites of PLoS Medicine at http://www.plosmedicine.org/, Annals of Internal Medicine at

http://www.annals.org/, and Epidemiology at http://www.epidem.com/). Information on the STROBE Initiative is available at www.strobe-statement.org.

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Relationship between Occupational Stress and Job Burnout

among Rural-to-urban Migrant Workers in Dongguan, China:

A Cross-Sectional Study

Journal: BMJ Open

Manuscript ID bmjopen-2016-012597.R1


Date Submitted by the Author: 12-Jul-2016


Department of Environmental and Occupational Health Xu, Xiu; Guangdong Medical University School of Public Health, Department of Environmental and Occupational Health Yun, Lin ; Guangdong Medical University School of Public Health, Department of Environmental and Occupational Health Chen, Ruoling; King's College London, ; Anhui Medical University, School of Health Administrations Chen, Yu; Guangdong Medical University School of Public Health, Department of Environmental and Occupational Health Xu, Long; Guangdong Medical University School of Public Health, Department of Environmental and Occupational Health Liu, Jia; Guangdong Medical University School of Public Health, Guangdong

Medical University School of Public Health Liu, Lin; Guangdong Medical University School of Public Health, Department of Environmental and Occupational Health Liang, Hai; Guangdong Medical University School of Public Health, Department of Environmental and Occupational Health Zhuang, Ya; Guangdong Medical University School of Public Health, Department of Environmental and Occupational Health Hong, Lie; Guangdong Medical University School of Public Health, Department of Environmental and Occupational Health Chen, Ling ; Guangdong Medical University School of Public Health, Department of Environmental and Occupational Health

Yang, Jin; BaoanCenter for Disease Control and Preventionof Shenzhen Tang, Huan; Guangdong Medical University School of Public Health, Department of Environmental and Occupational Health


Epidemiology




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among Rural-to-urban Migrant Workers in Dongguan,

China: A Cross-Sectional Study

Hao Luo1,a

, Hui Yang1,a


1, Ruoling Chen

3, Yuting

Chen1, Longmei Xu

1, Jiaxian Liu

1, Linhua Liu

1, Hairong Liang

1, Yali

Zhuang1, Liecheng Hong

1, Ling Chen

1, Jinping Yang

2, Huanwen Tang

1*





3 Post Graduate Academic Institute of Medicine, and Faculty of Education, Health and

Wellbeing, University of Wolverhampton, UK.

ABSTRACT

Objectives: In China, there have been an increasing number of migrant workers from

rural to urban areas, and migrant workers have the highest incidence of occupational

diseases. However, few studies have examined the impact of occupational stress on

job burnout in these migrant workers. This study aimed to investigate the relationship


Design: This study utilized a cross-sectional survey.

Setting: This investigation was conducted in Dongguan city, Guangdong Province,

China.

Participants: 3,806 migrant workers, aged 18-60 years old, were randomly selected

using multistage sampling procedures.


used to examine demographic characteristics, behavior customs and job-related data.

Hierarchical linear regression and logistic regression models were constructed to



*Correspondence to Huanwen Tang; [email protected]

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Results: Demographics, behavior customs and job-related characteristics significantly

affected on burnout. After adjusting for the control variable, a high level of emotional

exhaustion was associated with high role overload, high role insufficiency, high role

boundary, high physical environment, high psychological strain, high physical strain,

low role ambiguity, low responsibility and low vocational strain. A high level of

depersonalization was associated with high role overload, high role ambiguity, high

role boundary, high interpersonal strain, high recreation, low physical environment

and low social support. A low level of personal accomplishment was associated with

high role boundary, high role insufficiency, low responsibility, low social support,

low physical environment, low self-care and low interpersonal strain. Compared to the

personal resources, the job strain and personal strain were more likely to explain the

burnout of rural-to-urban migrant workers in our study.

Conclusions: The migrant workers have increased job burnouts in relation to

occupational stress. Relieving occupational stress and maintaining an appropriate

quantity and quality of work could be important measures for preventing job burnout

among these workers.


■The current study is the first to examine the relationship between occupational stress

and job burnout among rural-to-urban migrant workers in China.

■However, this study is a cross-sectional design, being cautious for the causal

relationship between occupational stress and job burnout. Due to self-report measures,

there may be an inherent bias in the data collection.

INTRODUCTION



levels of stress will result in emotional problems and ill health.1 Stress-related

symptoms range from mild medical unfitness, general unhappiness and anxiety to


smoking, divorce, psychiatric problems and suicide.2 Stress has been found to be



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3



have increased. This has led to an increased awareness of work-related burnout.3


mental exhaustion at work, which is characterized by emotional exhaustion (EE),

depersonalization (DEP) and reduced personal accomplishment (PA).4, 5

It has been

recognized as an occupational hazard and is associated with physical illness and

mental problems, including cardiovascular disease, musculoskeletal pain, depression

and anxiety.6, 7

Additionally, burnout is associated with absenteeism, both intention to

leave the job and actual turnover, and lower productivity, job performance and

involvement.7-9

Rural-to-urban migrant workers, who migrate from the rural areas of their original

residence to urban areas, are a unique phenomenon occurring in low and middle

income countries (LMC) with experiencing economic transformations. China is the

largest population LMC, and over the past three decades has had a rapid economic

development, with having produced the largest human migration in history. By 2012,

the number of migrant workers had reached 263 million.10

Most migrant workers





lacks health and safety protection. Previous studies showed that because of their

exposure to poor working conditions, occupational hazards and long working hours,11,

12 migrant workers have suffered from the highest incidences of occupational diseases

in all labor force in China.13

Although working environments have improved with

modernization, higher levels of knowledge and productivity are now being demanded

from migrant workers. Under these circumstances, the degree of acceptance felt in a

new home, relationships with co-workers, job-related and behavior customs,

interpersonal tensions and conflicts may lead to occupational stress for migrant

workers. Meanwhile, evidence from earlier studies suggests a positive association

between the employees’ health and burnout,14, 15

whereas demographics and

job-related characteristics may also be important factors influencing the level of

burnout.15, 16

Additionally, occupational stress has been strongly associated with

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burnout in medical and nursing populations.17, 18

However, there are few studies on the

relationship between occupational stress and burnout among rural-to-urban migrant

workers. Because this population is unique and its numbers are increasing, it is

necessary to investigate the relationships between occupational stress and burnout.

We carried out a cross-sectional study among rural-to-urban migrant workers in

Dongguan to investigate (1) the relationship between migration characteristics and

burnout among rural-to-urban migrant workers, and (2) the association between

occupational stress and burnout. The results could help deepen our understanding of

the factors associated with burnout and underscore the need to establish policies to

protect rural-to-urban migrant workers’ health.

METHODS


This cross-sectional survey was performed among migrant workers from the

Guancheng District of Dongguan, China. The participants in this study were recruited

using a multi-stage, stratified sampling method. In the first stage, two towns were

randomly selected from eight towns in Guancheng based on both economic

considerations and the proportion of migrants in these towns. In the second stage, we

randomly selected factories in electronics, shoe making, the chemical industry and

furniture by applying computer-generated random numbers to the town’s list of

factories. In the third stage, workers were randomly selected for participation from the

sampled factories, which employed approximately 10,000 rural-to-urban migrant

workers. All of the participants were informed about the study and were invited to fill

out an anonymous self-administered questionnaire between March 2013 and May

2013. The inclusion criteria for participation were as follows: (1) aged at18-60 years

old; (2) without a local household registration in Dongguan; (3) having left their

original home and resided in Dongguan for at least six months; and (4) willingness to

provide verbal informed consent. The exclusion criteria were individuals who might

have difficulties in understanding and answering the questionnaire, even with the help

of facilitators. Initially, there were 4,500 rural-to-urban migrant workers who were




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5







edition (OSI-R) written by Osipow and adapted by Li.19

The scale consists of the




behavior on a Likert scale from 1 (never) to 5 (often).20

For the ORQ and PSQ, a



divided the participants into 3 parts according to tertiles of their scores as low,

moderate and high score groups. The following were the cutoff points for the OSI-R

subscales: ORQ: low <120, moderate 120-160, high >160; PSQ and PRQ: low <92,

moderate 92-100, high >100. In our sample, the Cronbach’s alpha coefficients for the

ORQ, PSQ and PRQ were 0.88, 0.86 and 0.91, respectively.



(MBI-HSS), which consisted of 19 test items divided into three dimensions:5, 7, 21

7




involves cognition and feelings affecting self-efficacy.22

Some item statements are






PA scale. To better describe burnout states and to more readily identify

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burnout-associated risk factors, a weighted burnout score was introduced. Structural

analysis indicates that exhaustion has the most consistent relationship with burnout;

therefore, the equation used for calculations was burnout=0.4×EE+0.3×DEP+0.3×PA.

Burnout score was classified into three categories: no burnout (total score from 1

to2.49), mild burnout (2.50-4.49) and severe burnout (4.50-7). Rural-to-urban migrant

workers with mild or severe burnout were defined as “burnout cases”.23

The

MBI-HSS has previously demonstrated that it has high validity and reliability among

Chinese medical professionals.24

In our current study, the Cronbach’s alpha for EE,

DEP and PA was 0.70, 0.77 and 0.74, respectively.







The data were analyzed using SPSS for Windows Version 19.0 (SPSS, Inc., USA).

All statistical tests were two-sided, and a P value<0.05 was considered statistically




the correlations among the study variables. Hierarchical linear regression analyses

were performed to examine associations between occupational stress and MBI-HSS

scores. In the first step of the hierarchical linear regression analyses, the control

variables were added into the model. According to the independent-sample T-test and

one-way ANOVA analysis, variations such as age, gender, marital status, educational

level, smoking, drinking, physical training, hobbies, type of workplace, years of

practice, working hours per day and dull or repetitive work were included in the

model as potential confounders. In the second step, dimensions of OIS-R were added.

Variances of MBI-HSS scores explained by occupational stress were examined

byΔR2. A non-conditional logistic regression model was applied to estimate the

degree of association between each dimension of occupational stress and the

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MBI-HSS. The univariate model was first used to identify the potential risk factors,

and then the multivariable model was applied to confirm the identified associations.

RESULTS


Of the 3,806 respondents in the study, the average age was 31.35±7.60 years, with

22.04% being under the age of 25 years, 64.69% in 25-40 years old, 13.27% over 40

years old. Over half of participants (52.60%) were male, and66.55% of the

respondents were married.13.56% of participants had a junior college degree or

greater. Most of participants had good behavior customs: approximately






Factors Associated with Job Burnout

Table 1 shows differences in burnout subscales according to the respondent’s

demographics, behavior customs and job-related characteristics. Marital status,

education level, physical exercise, avocation, workplace type, working hours per day

and monotonous work were all associated with an emotional exhaustion score.









monotonous work who worked ≥10 h per day had higher emotional exhaustion scores

than other participants (P<0.05).



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Female migrant workers and those aged over 40 years old had lower

depersonalization scores compared to other subjects (P<0.05). Rural-to-urban migrant

workers with a junior school degree or below and who were widowed/divorced had

higher depersonalization scores than the other respondents (P<0.05). Rural-to-urban

migrant workers who had good behavior customs, worked during the day, had been

working ≤5 years, worked <8 h per day and whose work was not repetitive had lower










worked ≥10 h per day and whose work was not repetitive had higher personal











Table 3 presents relationship between occupational stress and job burnout in the

hierarchical linear regression analyses. After adjusted for the control variable, a

positive association of emotional exhaustion with role overload, role insufficiency,

role boundary, physical environment, psychological strain and physical strain was

observed. However, emotional exhaustion was significantly reversely associated with

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role ambiguity, responsibility and vocational strain. Role overload, role ambiguity,

role boundary, interpersonal strain and recreation positively predicted

depersonalization, whereas physical environment and social support were negatively

associated with depersonalization. A low level of personal accomplishment was

associated with high role boundary, high role insufficiency, low responsibility, low

social support, low physical environment, low self-care and low interpersonal strain

(in descending order of standardized estimates). The analysis showed that

demographics, behavior customs and job-related variables explained 17.6%, 13.4%

and 14.6% of the variance in emotional exhaustion, depersonalization and personal

accomplishment, respectively. Occupational stress was responsible for 14.1%, 15.7%

and 11.6% of the variance in emotional exhaustion, depersonalization and sense of

personal accomplishment, respectively.

In the univariate logistic regression, the three dimensions of burnout are associated

with job strain /personal strain/ personal resources. After controlling the confounders,

the results of the multivariable logistic regression are presented in Table 4. Exposure

to low job strain was associated with prevented high emotional exhaustion, high

depersonalization, and low sense of personal accomplishment. Migrant workers with

low job strain (OR=0.339, 95%CI=0.251-0.458) had less emotional exhaustion than

rural-to-urban migrant workers with high job strain. Respondents with low job strain

(OR=0.418, 95%CI=0.325-0.538) and moderate job strain (OR=0.287,

95%CI=0.242-0.341) had lower depersonalization than respondents with high job

strain. Additionally, subjects with low job strain (OR=0.768, 95%CI=0.604-0.977)

and moderate job strain (OR=0.450, 95%CI=0.379-0.534) had greater senses of

personal accomplishment, than migrant workers with high job strain, whereas

exposure to low personal strain was associated with prevent high emotional

exhaustion, and high depersonalization, Rural-to-urban migrant workers with low

personal strain (OR=0.588, 95%CI=0.487-0.709) and moderate personal strain

(OR=0.782, 95%CI=0.639-0.956) had less emotional exhaustion than respondents

with high personal strain. Respondents with moderate personal strain (OR=0.759,

95%CI=0.610-0.944) had less depersonalization than respondents with high personal

strain. Moreover, exposure to low personal resources increased the risk of high

emotional exhaustion, high depersonalization and a low sense of personal

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accomplishment. Rural-to-urban migrant with low and moderate personal resources

had 1.353 (95%CI=1.002-1.828) times and 2.046 (95%CI=1.506-2.780) times risk of

presenting emotional exhaustion, respectively, compared to rural-to-urban migrant

with high personal resources. Respondents with low and moderate personal resources


presenting depersonalization, respectively, compared to rural-to-urban migrant with

high personal resources. Additionally, subjects with low and moderate personal

resources had 3.944 (95%CI=2.935-5.299) times and 4.739 times risk of presenting

low personal accomplishment, respectively, compared to migrant workers with high

job strain.

To further explain the relationship between occupational stress and burnout, we

also calculated total scores for burnout. The burnout cases included respondents with

mild and severe burnout. The results of the univariate logistic regression showed that

the three dimensions of burnout are associated with job strain /personal strain/

personal resources. After controlling the confounders, Table 4 indicates that no

association was observed between personal resources and burnout. Exposure to low

(OR=0.025, 95% CI=0.007-0.082)/moderate job strain (OR=0.079, 95%

CI=0.024-0.256) and low personal strain (OR=0.585, 95% CI=0.366-0.935) was

associated with decreased burnout.

DISCUSSION


burnout among rural-to-urban migrant workers in China. The findings indicated that

burnout was significantly associated with occupational stress in this working

population. Reducing occupational stress could be an important strategy to prevent

job burnout among migrant workers.

Demographic characteristics including age, gender, education level and marital

status might be factors influencing the three dimensions of burnout. In our study,

males had more depersonalization than females, which was consistent with previous

studies.25

Male migrant workers had higher levels of personal accomplishment than

female migrant workers, probably because men take on few household responsibilities

and tasks in dual-career family life.14

Migrant workers between 25 and 40 years of age

had a higher level of depersonalization. People in this age group make up the core

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task force, which can be assigned more tasks, and the greater burden can leave people

feeling apathetic. Our findings that widowed/divorced migrant workers had more

emotional exhaustion and depersonalization than single and married workers are

supported by other studies.14, 26

This might be related to the social support provided by

a partner. Education level had significant effects on the emotional exhaustion and

depersonalization of migrant workers. Rural-to-urban migrant workers with a higher

education level had low levels of burnout. This is probably because migrant workers

with lower education accept unskilled jobs.



customs had a lower level of burnout. This is probably because respondents with good





hours worked per day. A similar result was observed in previous studies,15, 27, 28

in








high role overload and role boundary spent too much time and energy completing

their performance goals; thus, they would lack the time to relieve themselves of the

competitive pressures they faced. Furthermore, rural-to-urban migrant workers with

high psychological and physical strain felt continuously nervous. These factors may


low level of personal accomplishment. Previous studies suggested that social support

improved the ability to manage stress and was effective in reducing burnout.29, 30

Our

hierarchical linear regression analysis showed that those with low social support had a

higher risk of developing high depersonalization and a low sense of personal

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accomplishment, probably because social support may buffer the effects of stress.31,

32Our current study indicated that job strain, personal strain and personal resources

were another predictor of job burnout. High job strain was significantly associated

with the three dimensions of burnout and the total job burnout score. Rural-to-urban

migrant workers work for long hours and are overloaded, which may lead to physical

illness and mental problems. Meanwhile, the pessimism with regard to facing work

pressure and the ability to vent work stress may lead to anxiety and depression. The

effects on both physical and mental health can easily lead to burnout. Our data

suggested that low personal resources was a risk factor for the three dimensions of

burnout. This is related to the fact that such individuals rarely engage in recreational

activities and lack social support.


Our study is a cross-sectional survey and could not provide a causal relationship for

the findings of the association between occupational stress and job burnout. We

collected the data through the participants’ self-reporting; thus there may be a

potential bias due to the self-report methods. The study population was selected from

the two towns only, and the generalization of the findings may be limited. All of the

findings obtained in the present study must be confirmed in future prospective studies.

CONCLUSION


burnout among migrant workers. Strategies to reduce occupational stress as well as

maintain an appropriate quantity and quality of work need to be developed and should

be crucial to preventing job burnout of migrant workers in China.





questionnaire development and distribution as well as data collation and analysis.

Xiujuan Xu performed the statistical analyses. Lin Yun, Yuting Chen, Longmei Xu,

and Jiaxian Liu contributed to questionnaire development and distribution. Ruoling

Chen is assisted with improvements to the grammar in this manuscript. Linhua Liu,

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contributed to data acquisition. Huanwen Tang conceived of the study and its design

and contributed to questionnaire development.







(XK1414).




Data sharing statement No additional data are available.

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Table 1 Univariate analysis of MBI-HSS score according to demographic, behavior custom

and job-related characteristics



Gender

Male 2017 28.05±5.41 0.87 17.34±3.96 0.049 32.84±6.42 0.000

Female 1789 28.02±5.38 17.09±3.85 31.83±5.93 Age

<25 839 27.97±5.33 0.502 17.22±3.81 0.000 32.33±6.08 0.157

25-40 2462 28.11±5.43 17.42±3.91 32.27±6.37 >40 505 27.82±5.40 16.31±3.96 32.86±5.62

Marital status Single 1216 28.00±5.39 0.034 17.44±3.74 0.012 32.30±6.19 0.061

Married 2533 28.01±5.37 17.11±3.98 32.44±6.22 Widowed/divorced 57 29.88±6.17 18.11±4.05 30.51±6.26

Education level Junior school or less 1466 28.38±5.57 0.009 17.10±4.01 0.002 32.21±6.15 0.205

High school 1824 27.82±5.37 17.17±3.91 32.55±6.34 Junior college or more 516 27.83±4.92 17.78±3.55 32.14±5.96

Smoking Yes 1037 28.06±5.34 0.893 17.47±3.97 0.020 32.75±6.65 0.024

No 2769 28.03±5.41 17.14±3.89 32.22±6.04 Drinking Yes 929 28.29±5.38 0.101 17.74±3.97 0.000 32.56±6.54 0.282

No 2877 27.96±5.40 17.06±3.88 32.30±6.10 Physical exercise Yes 1760 27.32±5.33 0.000 17.05±4.21 0.011 32.92±6.98 0.000

No 2046 28.66±5.37 17.38±3.64 31.89±5.43 Avocation Yes 2770 27.67±5.43 0.000 17.00±3.95 0.000 32.70±6.45 0.000

No 1036 29.03±5.15 17.83±3.75 31.48±5.46 Workplace type

Fixed 2932 26.94±4.98 0.000 16.53±3.72 0.000 33.68±5.72 0.000

Shift 874 31.72±5.12 19.57±3.63 28.29±6.08 Years of practice

≤5 year 1580 27.80±5.34 0.059 16.91±3.92 0.000 32.57±5.95 0.175

6-10 years 1043 28.14±5.20 17.51±3.73 32.13±6.24 >10 year 1183 28.04±5.40 17.40±4.03 32.30±6.53

Working hours per day < 8 hours 610 27.46±5.54 0.000 16.85±4.00 0.008 32.14±6.57 0.001

8-10 hours 2383 27.95±5.34 17.37±3.92 32.16±6.21 ≥10 hours 813 28.74±5.40 17.10±3.79 33.08±5.91

Monotonous work Yes 2343 28.62±5.54 0.000 17.46±3.85 0.000 32.01±6.10 0.000

No 1463 27.10±5.02 16.85±3.98 32.93±6.36 Whether troubles are

discussed when facing

work pressure

Yes 2363 27.91±5.35 0.063 17.28±4.02 0.275 32.32±6.49 0.585

No 1443 28.25±5.46 17.14±3.73 32.43±5.74

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* P<0.05; ** P<0.01

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Table 3 Hierarchical linear regression analyses of factors associated with MBI-HSS scores

* P<0.05; ** P<0.01


Step 1 (β) Step 2 (β) Step 1 (β) Step 2 (β) Step 1 (β) Step 2 (β)

Gender -0.094 -0.193 -0.067 -0.073 -0.862** -0.957**

Age -0.182 -0.011 -0.474** -0.359** 0.334 0.220

Marital status 0.083 0.081 0.177 -0.095 -0.198 0.089

Education level -0.175 -0.322** 0.273** -0.012 0.009 0.092

Smoking 0.082 0.135 -0.031 0.155 -0.113 0.045

Drinking -0.399 0.245 -0.587** -0.121 0.297 0.064


Avocation 0.574** 0.360* 0.628** 0.216 -0.582* -0.013

Workplace type 4.622** 3.910** 2.979** 2.428** -5.117** -4.324**



Monotonous work -1.293** -0.818** -0.421** -0.257* 0.724** 0.551**

Role overload 0.167** 0.045** 0.009


Role ambiguity -0.144** 0.070** -0.106**

Role boundary 0.082** 0.149** -0.151**

Responsibility -0.052** -0.001 0.104**






Recreation 0.003 0.028** 0.014

Self-care -0.026 0.018 0.061**

Social support -0.018 -0.072** 0.099**


R2 0.176** 0.317** 0.134** 0.291** 0.146** 0.262**

△R2 0.176** 0.141** 0.134** 0.157** 0.146** 0.116**

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Variable Emotional exhaustion

OR (95% CI)

Depersonalization

OR (95% CI)

Personal accomplishment

OR (95% CI)

Burnout

OR (95% CI)

Job Strain

Low 0.339 (0.251, 0.458)** 0.418 (0.325,0.538)** 0.768(0.604, 0.977)* 0.025 (0.007, 0.082) **

Moderate 0.950 (0.806, 1.120) 0.287 (0.242, 0.341)** 0.450 (0.379, 0.534)** 0.079 (0.024, 0.256)**

High 1.000 1.000 1.000 1.000

Personal Strain

Low 0.588 (0.487, 0.709)** 0.841 (0.694, 1.020) 1.128 (0.935, 1.361) 0.585 (0.366, 0.935)*

Moderate 0.782 (0.639, 0.956)** 0.759 (0.610, 0.944)* 0.970 (0.785, 1.199) 0.630 (0.377, 1.050)

High 1.000 1.000 1.000 1.000

Personal Resources

Low 1.353 (1.002, 1.828)* 2.480 (1.840, 3.343)** 3.944 (2.935,5.299)** 5.502 (0.757, 39.995)

Moderate 2.046 (1.506, 2.780)** 1.889 (1.376, 2.592)** 4.739 (3.448, 6.514)** 5.473 (0.747, 39.500)

High 1.000 1.000 1.000 1.000

* P<0.05; **P<0.01

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Section/Topic Item




Introduction



Methods



collection

4

Participants

6

(a) Give the eligibility criteria, and the sources and methods of selection of participants 4-5


applicable

4-5

Data sources/

measurement



4-5




why



(c) Explain how missing data were addressed 6--7



Results

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7




confounders

7





7-10




Discussion

Key results 18 Summarise key results with reference to study objectives 10-12



12



10-12


Other information



13





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among Rural-to-urban Migrant Workers in Dongguan, China:

A Cross-Sectional Study

Journal: BMJ Open

Manuscript ID bmjopen-2016-012597.R2


Date Submitted by the Author: 21-Jul-2016


Department of Environmental and Occupational Health Xu, Xiu; Guangdong Medical University School of Public Health, Department of Environmental and Occupational Health Yun, Lin ; Guangdong Medical University School of Public Health, Department of Environmental and Occupational Health Chen, Ruoling; King's College London, ; Anhui Medical University, School of Health Administrations Chen, Yu; Guangdong Medical University School of Public Health, Department of Environmental and Occupational Health Xu, Long; Guangdong Medical University School of Public Health, Department of Environmental and Occupational Health Liu, Jia; Guangdong Medical University School of Public Health, Guangdong

Medical University School of Public Health Liu, Lin; Guangdong Medical University School of Public Health, Department of Environmental and Occupational Health Liang, Hai; Guangdong Medical University School of Public Health, Department of Environmental and Occupational Health Zhuang, Ya; Guangdong Medical University School of Public Health, Department of Environmental and Occupational Health Hong, Lie; Guangdong Medical University School of Public Health, Department of Environmental and Occupational Health Chen, Ling ; Guangdong Medical University School of Public Health, Department of Environmental and Occupational Health

Yang, Jin; BaoanCenter for Disease Control and Preventionof Shenzhen Tang, Huan; Guangdong Medical University School of Public Health, Department of Environmental and Occupational Health


Epidemiology




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among Rural-to-urban Migrant Workers in Dongguan,

China: A Cross-Sectional Study

Hao Luo1,a

, Hui Yang1,a


1, Ruoling Chen

3, Yuting

Chen1, Longmei Xu

1, Jiaxian Liu

1, Linhua Liu

1, Hairong Liang

1, Yali

Zhuang1, Liecheng Hong

1, Ling Chen

1, Jinping Yang

2, Huanwen Tang

1*





3 Post Graduate Academic Institute of Medicine, and Faculty of Education, Health and

Wellbeing, University of Wolverhampton, UK.

ABSTRACT

Objectives: In China, there have been an increasing number of migrant workers from

rural to urban areas, and migrant workers have the highest incidence of occupational

diseases. However, few studies have examined the impact of occupational stress on

job burnout in these migrant workers. This study aimed to investigate the relationship


Design: This study utilized a cross-sectional survey.

Setting: This investigation was conducted in Dongguan city, Guangdong Province,

China.

Participants: 3,806 migrant workers, aged 18-60 years old, were randomly selected

using multistage sampling procedures.


used to examine demographic characteristics, behavior customs and job-related data.

Hierarchical linear regression and logistic regression models were constructed to



*Correspondence to Huanwen Tang; [email protected]

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Results: Demographics, behavior customs and job-related characteristics significantly

affected on burnout. After adjusting for the control variable, a high level of emotional

exhaustion was associated with high role overload, high role insufficiency, high role

boundary, high physical environment, high psychological strain, high physical strain,

low role ambiguity, low responsibility and low vocational strain. A high level of

depersonalization was associated with high role overload, high role ambiguity, high

role boundary, high interpersonal strain, high recreation, low physical environment

and low social support. A low level of personal accomplishment was associated with

high role boundary, high role insufficiency, low responsibility, low social support,

low physical environment, low self-care and low interpersonal strain. Compared to the

personal resources, the job strain and personal strain were more likely to explain the

burnout of rural-to-urban migrant workers in our study.

Conclusions: The migrant workers have increased job burnouts in relation to

occupational stress. Relieving occupational stress and maintaining an appropriate

quantity and quality of work could be important measures for preventing job burnout

among these workers.


■The current study is the first to examine the relationship between occupational stress

and job burnout among rural-to-urban migrant workers in China.

■However, this study is a cross-sectional design, being cautious for the causal

relationship between occupational stress and job burnout. Due to self-report measures,

there may be an inherent bias in the data collection.

INTRODUCTION



levels of stress will result in emotional problems and ill health.1 Stress-related

symptoms range from mild medical unfitness, general unhappiness and anxiety to


smoking, divorce, psychiatric problems and suicide.2 Stress has been found to be



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have increased. This has led to an increased awareness of work-related burnout.3


mental exhaustion at work, which is characterized by emotional exhaustion (EE),

depersonalization (DEP) and reduced personal accomplishment (PA).4, 5

It has been

recognized as an occupational hazard and is associated with physical illness and

mental problems, including cardiovascular disease, musculoskeletal pain, depression

and anxiety.6, 7

Additionally, burnout is associated with absenteeism, both intention to

leave the job and actual turnover, and lower productivity, job performance and

involvement.7-9

Rural-to-urban migrant workers, who migrate from the rural areas of their original

residence to urban areas, are a unique phenomenon occurring in low and middle

income countries (LMC) with experiencing economic transformations. China is the

largest population LMC, and over the past three decades has had a rapid economic

development, with having produced the largest human migration in history. By 2012,

the number of migrant workers had reached 263 million.10

Most migrant workers





lacks health and safety protection. Previous studies showed that because of their

exposure to poor working conditions, occupational hazards and long working hours,11,

12 migrant workers have suffered from the highest incidences of occupational diseases

in all labor force in China.13

Although working environments have improved with

modernization, higher levels of knowledge and productivity are now being demanded

from migrant workers. Under these circumstances, the degree of acceptance felt in a

new home, relationships with co-workers, job-related and behavior customs,

interpersonal tensions and conflicts may lead to occupational stress for migrant

workers. Meanwhile, evidence from earlier studies suggests a positive association

between the employees’ health and burnout,14, 15

whereas demographics and

job-related characteristics may also be important factors influencing the level of

burnout.15, 16

Additionally, occupational stress has been strongly associated with

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burnout in medical and nursing populations.17, 18

However, there are few studies on the

relationship between occupational stress and burnout among rural-to-urban migrant

workers. Because this population is unique and its numbers are increasing, it is

necessary to investigate the relationships between occupational stress and burnout.

We carried out a cross-sectional study among rural-to-urban migrant workers in

Dongguan to investigate (1) the relationship between migration characteristics and

burnout among rural-to-urban migrant workers, and (2) the association between

occupational stress and burnout. The results could help deepen our understanding of

the factors associated with burnout and underscore the need to establish policies to

protect rural-to-urban migrant workers’ health.

METHODS


This cross-sectional survey was performed among migrant workers from the

Guancheng District of Dongguan, China. The participants in this study were recruited

using a multi-stage, stratified sampling method. In the first stage, two towns were

randomly selected from eight towns in Guancheng based on both economic

considerations and the proportion of migrants in these towns. In the second stage, we

randomly selected factories in electronics, shoe making, the chemical industry and

furniture by applying computer-generated random numbers to the town’s list of

factories. In the third stage, workers were randomly selected for participation from the

sampled factories, which employed approximately 10,000 rural-to-urban migrant

workers. All of the participants were informed about the study and were invited to fill

out an anonymous self-administered questionnaire between March 2013 and May

2013. The inclusion criteria for participation were as follows: (1) aged at18-60 years

old; (2) without a local household registration in Dongguan; (3) having left their

original home and resided in Dongguan for at least six months; and (4) willingness to

provide verbal informed consent. The exclusion criteria were individuals who might

have difficulties in understanding and answering the questionnaire, even with the help

of facilitators. Initially, there were 4,500 rural-to-urban migrant workers who were




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edition (OSI-R) written by Osipow and adapted by Li.19

The scale consists of the




behavior on a Likert scale from 1 (never) to 5 (often).20

For the ORQ and PSQ, a



divided the participants into 3 parts according to tertiles of their scores as low,

moderate and high score groups. The following were the cutoff points for the OSI-R

subscales: ORQ: low <120, moderate 120-160, high >160; PSQ and PRQ: low <92,

moderate 92-100, high >100. In our sample, the Cronbach’s alpha coefficients for the

ORQ, PSQ and PRQ were 0.88, 0.86 and 0.91, respectively.



(MBI-HSS), which consisted of 19 test items divided into three dimensions:5, 7, 21

7




involves cognition and feelings affecting self-efficacy.22

Some item statements are






PA scale. To better describe burnout states and to more readily identify

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burnout-associated risk factors, a weighted burnout score was introduced. Structural

analysis indicates that exhaustion has the most consistent relationship with burnout;

therefore, the equation used for calculations was burnout=0.4×EE+0.3×DEP+0.3×PA.

Burnout score was classified into three categories: no burnout (total score from 1 to

2.49), mild burnout (2.50-4.49) and severe burnout (4.50-7). Rural-to-urban migrant

workers with mild or severe burnout were defined as “burnout cases”.23

The

MBI-HSS has previously demonstrated that it has high validity and reliability among

Chinese medical professionals.24

In our current study, the Cronbach’s alpha for EE,

DEP and PA was 0.70, 0.77 and 0.74, respectively.







The data were analyzed using SPSS for Windows Version 19.0 (SPSS, Inc., USA).

All statistical tests were two-sided, and a P value<0.05 was considered statistically




the correlations among the study variables. Hierarchical linear regression analyses

were performed to examine associations between occupational stress and MBI-HSS

scores. In the first step of the hierarchical linear regression analyses, the control

variables were added into the model. According to the independent-sample T-test and

one-way ANOVA analysis, variations such as age, gender, marital status, educational

level, smoking, drinking, physical training, hobbies, type of workplace, years of

practice, working hours per day and dull or repetitive work were included in the

model as potential confounders. In the second step, dimensions of OIS-R were added.

Variances of MBI-HSS scores explained by occupational stress were examined

byΔR2. A non-conditional logistic regression model was applied to estimate the

degree of association between each dimension of occupational stress and the

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MBI-HSS. The univariate model was first used to identify the potential risk factors,

and then the multivariable model was applied to confirm the identified associations.

RESULTS


Of the 3,806 respondents in the study, the average age was 31.35±7.60 years, with

22.04% being under the age of 25 years, 64.69% in 25-40 years old, 13.27% over 40

years old. Over half of participants (52.60%) were male, and66.55% of the

respondents were married.13.56% of participants had a junior college degree or

greater. Most of participants had good behavior customs: approximately






Factors Associated with Job Burnout

Table 1 shows differences in burnout subscales according to the respondent’s

demographics, behavior customs and job-related characteristics. Marital status,

education level, physical exercise, avocation, workplace type, working hours per day

and monotonous work were all associated with an emotional exhaustion score.









monotonous work who worked ≥10 h per day had higher emotional exhaustion scores

than other participants (P<0.05).



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Female migrant workers and those aged over 40 years old had lower

depersonalization scores compared to other subjects (P<0.05). Rural-to-urban migrant

workers with a junior school degree or below and who were widowed/divorced had

higher depersonalization scores than the other respondents (P<0.05). Rural-to-urban

migrant workers who had good behavior customs, worked during the day, had been

working ≤5 years, worked <8 h per day and whose work was not repetitive had lower










worked ≥10 h per day and whose work was not repetitive had higher personal











Table 3 presents relationship between occupational stress and job burnout in the

hierarchical linear regression analyses. After controlling potential confounders listed

in Table 1 and 2, a positive association of emotional exhaustion with role overload,

role insufficiency, role boundary, physical environment, psychological strain and

physical strain was observed. However, emotional exhaustion was significantly

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reversely associated with role ambiguity, responsibility and vocational strain. Role

overload, role ambiguity, role boundary, interpersonal strain and recreation positively

predicted depersonalization, whereas physical environment and social support were

negatively associated with depersonalization. A low level of personal accomplishment

was associated with high role boundary, high role insufficiency, low responsibility,

low social support, low physical environment, low self-care and low interpersonal

strain (in descending order of standardized estimates). The analysis showed that

demographics, behavior customs and job-related variables explained 17.6%, 13.4%

and 14.6% of the variance in emotional exhaustion, depersonalization and personal

accomplishment, respectively. Occupational stress was responsible for 14.1%, 15.7%

and 11.6% of the variance in emotional exhaustion, depersonalization and sense of

personal accomplishment, respectively.

In the univariate logistic regression, job strain /personal strain/ personal resources

are associated with the three dimensions of burnout. The results of the multivariable

logistic regression in Table 4 were adjusted for rural-to-urban workers’ characteristics

and occupational stress including gender, age, marital status, education level, smoking,

drinking, physical exercise, avocation, workplace type, years of practice, working

hours per day, monotonous work, job strain, personal strain, and personal resource.

Exposure to low job strain was associated with prevented high emotional exhaustion,

high depersonalization, and low sense of personal accomplishment. Migrant workers

with low job strain (OR=0.339, 95%CI=0.251-0.458) had less emotional exhaustion

than rural-to-urban migrant workers with high job strain. Respondents with low job

strain (OR=0.418, 95%CI=0.325-0.538) and moderate job strain (OR=0.287,

95%CI=0.242-0.341) had lower depersonalization than respondents with high job

strain. Additionally, subjects with low job strain (OR=0.768, 95%CI=0.604-0.977)

and moderate job strain (OR=0.450, 95%CI=0.379-0.534) had greater senses of

personal accomplishment, than migrant workers with high job strain, whereas

exposure to low personal strain was associated with prevent high emotional

exhaustion, and high depersonalization, Rural-to-urban migrant workers with low

personal strain (OR=0.588, 95%CI=0.487-0.709) and moderate personal strain

(OR=0.782, 95%CI=0.639-0.956) had less emotional exhaustion than respondents

with high personal strain. Respondents with moderate personal strain (OR=0.759,

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95%CI=0.610-0.944) had less depersonalization than respondents with high personal

strain. Moreover, exposure to low personal resources increased the risk of high

emotional exhaustion, high depersonalization and a low sense of personal

accomplishment. Rural-to-urban migrant with low and moderate personal resources


presenting emotional exhaustion, respectively, compared to rural-to-urban migrant

with high personal resources. Respondents with low and moderate personal resources


presenting depersonalization, respectively, compared to rural-to-urban migrant with

high personal resources. Additionally, subjects with low and moderate personal

resources had 3.944 (95%CI=2.935-5.299) times and 4.739 times risk of presenting

low personal accomplishment, respectively, compared to migrant workers with high

job strain.

To further explain the relationship between occupational stress and burnout, we

also calculated total scores for burnout. The burnout cases included respondents with

mild and severe burnout. The results of the univariate logistic regression showed that

the three dimensions of burnout are associated with job strain /personal strain/

personal resources. After controlling the confounders, Table 4 indicates that no

association was observed between personal resources and burnout. Exposure to low

(OR=0.025, 95% CI=0.007-0.082)/moderate job strain (OR=0.079, 95%

CI=0.024-0.256) and low personal strain (OR=0.585, 95% CI=0.366-0.935) was

associated with decreased burnout.

DISCUSSION


burnout among rural-to-urban migrant workers in China. The findings indicated that

burnout was significantly associated with occupational stress in this working

population. Reducing occupational stress could be an important strategy to prevent

job burnout among migrant workers.

Demographic characteristics including age, gender, education level and marital

status might be factors influencing the three dimensions of burnout. In our study,

males had more depersonalization than females, which was consistent with previous

studies.25

Male migrant workers had higher levels of personal accomplishment than

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female migrant workers, probably because men take on few household responsibilities

and tasks in dual-career family life.14

Migrant workers between 25 and 40 years of age

had a higher level of depersonalization. People in this age group make up the core

task force, which can be assigned more tasks, and the greater burden can leave people

feeling apathetic. Our findings that widowed/divorced migrant workers had more

emotional exhaustion and depersonalization than single and married workers are

supported by other studies.14, 26

This might be related to the social support provided by

a partner. Education level had significant effects on the emotional exhaustion and

depersonalization of migrant workers. Rural-to-urban migrant workers with a higher

education level had low levels of burnout. This is probably because migrant workers

with lower education accept unskilled jobs.



customs had a lower level of burnout. This is probably because respondents with good





hours worked per day. A similar result was observed in previous studies,15, 27, 28

in








high role overload and role boundary spent too much time and energy completing

their performance goals; thus, they would lack the time to relieve themselves of the

competitive pressures they faced. Furthermore, rural-to-urban migrant workers with

high psychological and physical strain felt continuously nervous. These factors may


low level of personal accomplishment. Previous studies suggested that social support

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improved the ability to manage stress and was effective in reducing burnout.29, 30

Our

hierarchical linear regression analysis showed that those with low social support had a

higher risk of developing high depersonalization and a low sense of personal

accomplishment, probably because social support may buffer the effects of stress.31, 32

Our current study indicated that job strain, personal strain and personal resources were

another predictor of job burnout. High job strain was significantly associated with the

three dimensions of burnout and the total job burnout score. Rural-to-urban migrant

workers work for long hours and are overloaded, which may lead to physical illness

and mental problems. Meanwhile, the pessimism with regard to facing work pressure

and the ability to vent work stress may lead to anxiety and depression. The effects on

both physical and mental health can easily lead to burnout. Our data suggested that

low personal resources was a risk factor for the three dimensions of burnout. This is

related to the fact that such individuals rarely engage in recreational activities and lack

social support.


Our study is a cross-sectional survey and could not provide a causal relationship for

the findings of the association between occupational stress and job burnout. We

collected the data through the participants’ self-reporting; thus there may be a

potential bias due to the self-report methods. The study population was selected from

the two towns only, and the generalization of the findings may be limited. All of the

findings obtained in the present study must be confirmed in future prospective studies.

CONCLUSION


burnout among migrant workers. Strategies to reduce occupational stress as well as

maintain an appropriate quantity and quality of work need to be developed and should

be crucial to preventing job burnout of migrant workers in China.





questionnaire development and distribution as well as data collation and analysis.

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Xiujuan Xu performed the statistical analyses. Lin Yun, Yuting Chen, Longmei Xu,

and Jiaxian Liu contributed to questionnaire development and distribution. Ruoling

Chen is assisted with improvements to the grammar in this manuscript. Linhua Liu,


contributed to data acquisition. Huanwen Tang conceived of the study and its design

and contributed to questionnaire development.







(XK1414).




Data sharing statement No additional data are available.

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Table 1 Univariate analysis of MBI-HSS score according to demographic, behavior custom

and job-related characteristics


Gender

Male 2017 28.05±5.41 0.87 17.34±3.96 0.049 32.84±6.42 0.000

Female 1789 28.02±5.38 17.09±3.85 31.83±5.93 Age

<25 839 27.97±5.33 0.502 17.22±3.81 0.000 32.33±6.08 0.157

25-40 2462 28.11±5.43 17.42±3.91 32.27±6.37 >40 505 27.82±5.40 16.31±3.96 32.86±5.62

Marital status Single 1216 28.00±5.39 0.034 17.44±3.74 0.012 32.30±6.19 0.061

Married 2533 28.01±5.37 17.11±3.98 32.44±6.22 Widowed/divorced 57 29.88±6.17 18.11±4.05 30.51±6.26

Education level Junior school or less 1466 28.38±5.57 0.009 17.10±4.01 0.002 32.21±6.15 0.205

High school 1824 27.82±5.37 17.17±3.91 32.55±6.34 Junior college or more 516 27.83±4.92 17.78±3.55 32.14±5.96

Smoking Yes 1037 28.06±5.34 0.893 17.47±3.97 0.020 32.75±6.65 0.024

No 2769 28.03±5.41 17.14±3.89 32.22±6.04 Drinking Yes 929 28.29±5.38 0.101 17.74±3.97 0.000 32.56±6.54 0.282

No 2877 27.96±5.40 17.06±3.88 32.30±6.10 Physical exercise Yes 1760 27.32±5.33 0.000 17.05±4.21 0.011 32.92±6.98 0.000

No 2046 28.66±5.37 17.38±3.64 31.89±5.43 Avocation Yes 2770 27.67±5.43 0.000 17.00±3.95 0.000 32.70±6.45 0.000

No 1036 29.03±5.15 17.83±3.75 31.48±5.46 Workplace type

Fixed 2932 26.94±4.98 0.000 16.53±3.72 0.000 33.68±5.72 0.000

Shift 874 31.72±5.12 19.57±3.63 28.29±6.08 Years of practice

≤5 year 1580 27.80±5.34 0.059 16.91±3.92 0.000 32.57±5.95 0.175

6-10 years 1043 28.14±5.20 17.51±3.73 32.13±6.24 >10 year 1183 28.04±5.40 17.40±4.03 32.30±6.53

Working hours per day < 8 hours 610 27.46±5.54 0.000 16.85±4.00 0.008 32.14±6.57 0.001

8-10 hours 2383 27.95±5.34 17.37±3.92 32.16±6.21 ≥10 hours 813 28.74±5.40 17.10±3.79 33.08±5.91

Monotonous work Yes 2343 28.62±5.54 0.000 17.46±3.85 0.000 32.01±6.10 0.000

No 1463 27.10±5.02 16.85±3.98 32.93±6.36 Whether troubles are

discussed when facing

work pressure

Yes 2363 27.91±5.35 0.063 17.28±4.02 0.275 32.32±6.49 0.585

No 1443 28.25±5.46 17.14±3.73 32.43±5.74

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* P<0.05; ** P<0.01

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Table 3 Hierarchical linear regression analyses of factors associated with MBI-HSS scores

* P<0.05; ** P<0.01

# Step 2 (β) adjusted for gender, age, marital status, education level, smoking, drinking, physical

exercise, avocation, workplace type, years of practice, working hours per day, monotonous work,

role overload, role insufficiency, role ambiguity, role boundary, responsibility, physical

environment, vocational strain, psychological strain, interpersonal strain, physical strain,

recreation, self-care, social support, rational coping.


Step 1 (β) Step 2 (β)# Step 1 (β) Step 2 (β)

# Step 1 (β) Step 2 (β)#

Gender -0.094 -0.193 -0.067 -0.073 -0.862** -0.957**

Age -0.182 -0.011 -0.474** -0.359** 0.334 0.220

Marital status 0.083 0.081 0.177 -0.095 -0.198 0.089

Education level -0.175 -0.322** 0.273** -0.012 0.009 0.092

Smoking 0.082 0.135 -0.031 0.155 -0.113 0.045

Drinking -0.399 0.245 -0.587** -0.121 0.297 0.064


Avocation 0.574** 0.360* 0.628** 0.216 -0.582* -0.013

Workplace type 4.622** 3.910** 2.979** 2.428** -5.117** -4.324**



Monotonous work -1.293** -0.818** -0.421** -0.257* 0.724** 0.551**

Role overload 0.167** 0.045** 0.009


Role ambiguity -0.144** 0.070** -0.106**

Role boundary 0.082** 0.149** -0.151**

Responsibility -0.052** -0.001 0.104**






Recreation 0.003 0.028** 0.014

Self-care -0.026 0.018 0.061**

Social support -0.018 -0.072** 0.099**


R2 0.176** 0.317** 0.134** 0.291** 0.146** 0.262**

△R2 0.176** 0.141** 0.134** 0.157** 0.146** 0.116**

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Variable Emotional exhaustion

OR (95% CI) #

Depersonalization

OR (95% CI) #

Personal accomplishment

OR (95% CI) #

Burnout

OR (95% CI) #

Job Strain

Low 0.339 (0.251, 0.458)** 0.418 (0.325,0.538)** 0.768(0.604, 0.977)* 0.025 (0.007, 0.082) **

Moderate 0.950 (0.806, 1.120) 0.287 (0.242, 0.341)** 0.450 (0.379, 0.534)** 0.079 (0.024, 0.256)**

High 1.000 1.000 1.000 1.000

Personal Strain

Low 0.588 (0.487, 0.709)** 0.841 (0.694, 1.020) 1.128 (0.935, 1.361) 0.585 (0.366, 0.935)*

Moderate 0.782 (0.639, 0.956)** 0.759 (0.610, 0.944)* 0.970 (0.785, 1.199) 0.630 (0.377, 1.050)

High 1.000 1.000 1.000 1.000

Personal Resources

Low 1.353 (1.002, 1.828)* 2.480 (1.840, 3.343)** 3.944 (2.935,5.299)** 5.502 (0.757, 39.995)

Moderate 2.046 (1.506, 2.780)** 1.889 (1.376, 2.592)** 4.739 (3.448, 6.514)** 5.473 (0.747, 39.500)

High 1.000 1.000 1.000 1.000

* P<0.05; **P<0.01

# Adjusted odds ratio (95% confidence interval), adjusted for gender, age, marital status, education level, smoking,

drinking, physical exercise, avocation, workplace type, years of practice, working hours per day, monotonous work,

job strain, personal strain, and personal resources.

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Section/Topic Item




Introduction



Methods



collection

4

Participants

6

(a) Give the eligibility criteria, and the sources and methods of selection of participants 4-5


applicable

4-5

Data sources/

measurement



4-5




why



(c) Explain how missing data were addressed 6--7



Results

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7




confounders

7





7-10




Discussion

Key results 18 Summarise key results with reference to study objectives 10-12



12



10-12


Other information



13





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Documents

BMJ Open · Complete List of Authors: Luo, Hao; Guangdong Medical University School of Public Health, Department of Environmental and Occupational Health Yang, Hui ; Guangdong Medical