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The prevalence and associated factors of job burnout among medical workers at COVID-19 vaccination sites: A cross-sectional study

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Background Medical workers at COVID-19 vaccination sites are exposed to burnout syndrome due to repetitive and overload vaccination work. The purpose of our study was to investigate the prevalence of burnout among these medical workers and to explore its associated factors. Methods A cross-sectional study was conducted at COVID-19 vaccination sites in May 2021 in Nanjing, China. The online questionnaire included demographic, job and COVID-19 related characteristics, Chinese Maslach Burnout Inventory, and Social Support Rating Scale. Hierarchical multiple regression model was used to identify the risk factors for job burnout of medical workers. Results The overall prevalence of burnout symptoms among medical workers at COVID-19 vaccination sites was 44.5% with a breakdown in severity as follows: 122 (28.7%) mild, 53 (12.5%) moderate, and 14 (3.3%) severe cases. Hierarchical multiple linear regression analysis indicated that education level, health condition, job titles, self-reported increased work intensity, monthly income and social support were significantly related to job burnout (p < 0.05), which explained 37.6% of the variance of job burnout score (F = 16.046, p < 0.01). Conclusion The burnout symptoms were relatively common among medical workers at COVID-19 vaccination sites. More attention should be paid to medical workers with master degree or higher, poor health condition, junior or middle job titles, increased work intensity, lower income and low level of social support. Interventions that aim to reduce workload and increase social support can be effective approaches to prevent job burnout among medical workers during controlled COVID-19 period.
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The prevalence and associated factors of job
burnout among medical workers at COVID-19
vaccination sites: A cross-sectional study
Yinan Qian
Yangzhou City Jiangdu District Municipal Center for Disease Control and Prevention
Na Wang
Nanjing Municipal Center for Disease Control and Prevention
Lili Gou
Nanjing Municipal Center for Disease Control and Prevention
Lei Yuan
Nanjing Medical University
Lijun Lu
Nanjing Medical University
Mohammad Sulaiman Fadhi Al-shdifat
Nanjing Medical University
Xupeng Chen
Nanjing Municipal Center for Disease Control and Prevention
Jiaping Chen
Nanjing Medical University
Sijun Liu ( sjliu@njmu.edu.cn )
Nanjing Medical University
Article
Keywords: job burnout, COVID-19, pandemic, vaccination, medical workers
Posted Date: September 25th, 2023
DOI: https://doi.org/10.21203/rs.3.rs-3353701/v1
License: This work is licensed under a Creative Commons Attribution 4.0 International License. 
Read Full License
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Abstract
Background
Medical workers at COVID-19 vaccination sites are exposed to burnout syndrome due to repetitive and
overload vaccination work. The purpose of our study was to investigate the prevalence of burnout among
these medical workers and to explore its associated factors.
Methods
A cross-sectional study was conducted at COVID-19 vaccination sites in May 2021 in Nanjing, China. The
online questionnaire included demographic, job and COVID-19 related characteristics, Chinese Maslach
Burnout Inventory, and Social Support Rating Scale. Hierarchical multiple regression model was used to
identify the risk factors for job burnout of medical workers.
Results
The overall prevalence of burnout symptoms among medical workers at COVID-19 vaccination sites was
44.5% with a breakdown in severity as follows: 122 (28.7%) mild, 53 (12.5%) moderate, and 14 (3.3%)
severe cases. Hierarchical multiple linear regression analysis indicated that education level, health
condition, job titles, self-reported increased work intensity, monthly income and social support were
signicantly related to job burnout (
p
 < 0.05), which explained 37.6% of the variance of job burnout score
(
F
 = 16.046,
p
 < 0.01).
Conclusion
The burnout symptoms were relatively common among medical workers at COVID-19 vaccination sites.
More attention should be paid to medical workers with master degree or higher, poor health condition,
junior or middle job titles, increased work intensity, lower income and low level of social support.
Interventions that aim to reduce workload and increase social support can be effective approaches to
prevent job burnout among medical workers during controlled COVID-19 period.
BACKGROUND
According to the International Classication of Diseases 11th revision (ICD-11) issued by WHO, job
burnout has been recognized as a new public health concern1. The term “job burnout” was rst
introduced by Freudenberger and is used to refer to a state of exhaustion and fatigue caused by constant
emotional input of individuals in the service industry2. Job burnout was divided into three dimensions:
emotional exhaustion (EE), depersonalization (DP) and decreased personal accomplishment (DPA)3. EE
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is a symptom of lack of energy, enthusiasm and feeling of great tiredness. DP refers to the indifference
and perfunctory attitude towards the objects one served and DPA is the tendency to evaluate ones work
achievements negatively4.
In recent years, it has been demonstrated that medical workers were more likely to experience job burnout
than other professionals due to the high requirements and stressful risk of work5,6. Job burnout among
medical workers would lead to a negative impact on the physical and mental health of the individual,
affect daily diagnosis and treatment, and even affect social stability7,8,9,10. A study of physicians in the
UK and Ireland pointed out that job burnout was becoming an increasingly common phenomenon within
the medical profession11. A systematic review of 182 studies involving 109,628 physicians in 45
countries demonstrated the overall burnout prevalence ranged from 0–80.5%12. COVID-19 has become a
worldwide pandemic with severe impacts on health care and economy, greatly increasing work risk
among medical workers13. During the initial COVID-19 pandemic, a survey conducted among medical
workers from 69 countries all around the world reported a job burnout prevalence of 51%14. Meanwhile, a
cross-sectional study in 133 cities in China showed that 36.5% of front-line medical staffs were suffering
from job burnout15.
Previous studies widely conrmed that job burnout is associated with social support16, which refers to
the material and spiritual support that an individual can obtain from the outside when facing stressful
events, acting as an external resource in the process of reducing burnout17. Social support has a
benecial buffering effect and perform a meditating role between individuals’ job stress and burnout16,18.
In addition, several demographic and job characteristics were suggested to be inuencing factors in job
burnout among medical workers based on previous studies, such as gender, marital status, health
condition, monthly income, working hour and so on4,19,20,21.
According to China's National Bureau of Statistics, there were 102,314 conrmed cases of COVID-19 and
4,636 deaths in 2021, China. Vaccination is one of the most effective and economical health measures to
prevent and control COVID-19. It has been reported that a total of 2835.33million doses of SARS-CoV-2
vaccines were administered nationwide in 202122. In Nanjing, centralized vaccination sites were set up in
each administrative district for the convenience of citizens to get vaccinated. High workload and
temporary working conditions are the major challenges for medical workers at centralized sites, which
may contribute to negative emotions and even burnout.
However, studies of job burnout among medical workers at centralized vaccination sites are limited. After
the prolonged public health crisis of COVID-19, it is worth exploring how about the mental health status of
medical workers for vaccination. Therefore, the aim of this study was to explore the prevalence of job
burnout and identify its association with demographic, job, and COVID-19 related characteristics, as well
as social support among medical workers at vaccination sites in Nanjing, China. In addition, we aim to
put forward some coping strategies for job burnout of them.
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METHODS
2.1 Study Design and Procedure
A cross-sectional study was conducted among medical workers at COVID-19 centralized vaccination sites
in Nanjingfrom 13 to 26May, 2021. The sites were temporary workplace for the convenience of people
who need to be vaccinatedduring epidemic. Thetasksof medical workersat the sites were consultation,
vaccination, observation after vaccination and treatment of adverse reactions. The study was conducted
in Xuanwu, Jianye, Pukou and Jiangbei districts (four of twelve districts in Nanjing).Participants were
included in our study based on the following criteria: (1) taking part in the vaccination related work
against Nanjing COVID-19 epidemic at thecentralizedsites, (2) beingclinicians, nurses, medical
technicians and administrativestaffsfrom community hospitals, and (3) consenting to participate in this
survey.
This study was approved by the Ethics Committee of Nanjing Medical University with the approval
number FWA00001501. All subjects signed informed consent forms.
Theinternet-basedquestionnaire was uploaded to the online professional survey platformof SO-JUMPin
this study. Additionally,therewere guidelines to inform medical workers that their participation would be
voluntary.The questionnaire included demographic, job and COVID-19 related information and the
following validated scales: The Chinese Maslach Burnout Inventory (CMBI) and the Social Support Rating
Scale. Each account could only submit one questionnaire to avoid the possibility of repeated
participation and each question was designed to be mandatory to avoid missing data.
The sample size was estimated using PASS15 based on thestudy design, expected statistical power and
estimatedprevalence of job burnout. We assume that a 50% prevalence of burnout would be observed,
the permissible absolute error was 5% and that α=0.05 (two sided). A minimum sample size of 402 was
required. Out of 434 participants selected, a total of 425 participants (48 male and 377 female) aged 21-
58 years completed the survey with a response rate of 97.93%.
2.2 Measures
Data were collected using a self-administrated questionnaire divided into three sections.
The rst section covered the demographic characteristics, job characteristics and COVID-19 related
characteristics. The demographic characteristics included information concerning age (years), gender
(“male” or “female”), marital status, education level and health condition. Marital status was dened as
married orunmarried(“single”,“divorced” and “widowed”). Education level was recorded as “bachelor
degree or below” and “master degree and above”. The health condition was recorded as poor, fair and
good. The job characteristics included job tenure (years), working hours per day (“6-7”, “8-9” and “10”),
job title (“No title”, “Junior title”, “Middle title” and “Senior title”), monthly income (“5,000 RMB” or
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“>5,000 RMB”) andself-reported of increased work intensity after working in the centralized vaccination
site (“Yes” or “No”). The COVID-19 related characteristics included frequently concern about the latest
epidemic of COVID-19 (“Yes” or “No”), self-assessmentrisk of contracting COVID-19 in the course of work
(“Low”, “Average” and “High”), vaccination of COVID-19 (“Yes” or “No”), vaccination of COVID-19 for at
least one family member (“Yes” or “No”).
The second section was the CMBI23, which was developed from the Maslach Burnout Inventory and
shown to adapt tothebackground of Chinese culture.The structure of the CMBI was also validated in
three different occupational samples: teachers, police ocers, and health care workers.The scale
contains 15 items with three dimensions: emotional exhaustion (EE; ve items), depersonalization (DP;
ve items), and decreased personal accomplishment (DPA; ve items). Each item was measured by a
seven-point Likert scale. Higher scores reect greater job burnout. The severity of job burnout was
classied into four levels—none, slight, moderate and severe according to the number of scores meeting
or exceeding the cutoff values (i.e., EE 25, DP 11 and DPA16). None job burnout was dened as no
score met the cutoff values. Slight, moderate and severe job burnout were dened as one, two and three
number of three scores met or exceeded cutoff values, respectively. In this study, the Cronbachs α
coecient for CMBI was 0.824.
The third section was the Social Support Rating Scale25, which reected staff member’s perceived
support from colleagues, supervisors, spouses, family members and friends. The scale contains 12 items,
ranked on a ve-point Likert scale from 0 (never) to 4 (very much). Higher score reects higher level of
social support. The scale of the Chinese version showed good reliability and validity. Cronbachs
α
coecients for the scale in this study was 0.920, which indicated acceptable internal consistency.
2.3 Statistical analyses
Continuous variables were described using mean and standard deviation. Differences among groups
were compared by utilizing the
t
-test and one-way ANOVA. Categorical variables were described using
frequency and percentage. Hierarchical multiple regression was performed to select variables that were
responsible for the largest proportion of the explained variance. Only variables with
p
< 0.1 in univariate
analysis were kept in the hierarchical multiple regression. All analyses were conducted using the
Statistical Package for Social Sciences for Windows version 26.0 (SPSS, Chicago, IL).
P
<0.05 was
considered statistically signicant.
RESULTS
3.1 Characteristics of participants
A total of 425 participants completed the questionnaires and were enrolled in the nal analyses. The
average age and job tenure of the participants were 32.71±7.53 and 11.11±7.89 years, respectively. Most
Page 6/19
participants were females (88.7%) and had been married (72.7%).About 41.6% ofthem had middle title
and senior title. Detailed demographic, job and COVID-19 related characteristics were in Table1.
3.2 Job burnout among medical workers at centralized
vaccination sites
Among the 425 medical workers, the mean score of job burnout was 37.29±12.26, and those of EE, DP
and DPA were 18.02±7.88, 7.20±3.52 and 12.08±5.31, respectively. Overall, 22.1% of participants reported
severe EE, 14.8% reported severe DP and 26.6% reported severe DPA. Moreover, the prevalence rates of
slight, moderate and severe burnout were 28.7%,12.5%, and 3.3%, respectively (Table 2).
3.3 Univariate analysis of job burnout among medical
workers
The results ofassociation betweendemographic, job and COVID-19 related characteristics and
burnoutamong medical workersare shown in Table1. There were signicant differences in CMBI scores
among different education level and job title groups for job burnout (
t
=−2.965,
p
=0.003 and
F
=6.389,
p
<0.001). Increasing working hours per day was positively associated with job burnout (
F
=6.448,
p
=0.002). The “married” participants reported higher burnout score compared to those who were
“unmarried” (
t
=−3.077,
p
=0.002). Those who had “good” health condition reported lower burnout score
(
F
=59.578,
p
<0.001). Those who frequently concerned about the latest epidemic of COVID-19 had lower
scores than those who did not (
t
=2.854,
p
=0.005). Participants who had increased work intensity reported
higher scores compared with those who did not (
t
=−6.258,
p
<0.001). Medicalworkerswhoassessedthat
they were more likely to be infected with COVID-19duringwork had higher burnout scores (
F
=11.544,
p
<0.001). Participants who reported vaccination of COVID-19 for at least one family member had lower
scores (
t
=−3.095,
p
=0.002).
3.4 Hierarchical multiple regression analysis among
medical workers
The results of hierarchical multiple linear regression analysis of the factors inuencing job burnout
scores were displayed in Table 3. For job burnout, only education level, health condition, job title,
increased work intensity,monthly incomeand social support showed signicant results in the nal model
with an adjusted
R
² value of 0.376. Master degree or higher, poor health condition, junior and middle job
titles, increased work intensity, low monthly incomeand lowlevel ofsocial support predicted a high level
of job burnout (
F
=16.064,
p
<0.001). The effects of different variables on the variance in job burnout score
were displayed in the multiple linear regression model. Demographic-, job-, COVID-19-, and social support-
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related characteristics accounted for 24.2%, 4.9%, 2.2%, and 6.5% of the variance in job burnout,
respectively.
DISCUSSION
This was the rst study attempted to assess the prevalence and associated factors of job burnout among
medical workers at centralized COVID-19 vaccination sites in Nanjing, China. In this study, the prevalence
rate of burnout (which included mild, moderate and severe burnout) was 44.5%. We compare the result
with some studies which is also selected CMBI to assess burnout in China in recent years. Fu et al.
conducted a burnout survey in Wuhan one year after the beginning of the COVID-19 pandemic, and the
results indicated that approximately 37.39% of medical staff experienced burnout26. A cross-sectional
study containing 1133 female nurses from September to October 2020 during controlled COVID-19 period
showed that 60.20% were experiencing varying degrees of burnout4. The study of 880 doctors and nurses
in February 2020 during COVID-19 outbreak found the overall prevalence of burnout was 73.98%27.
Before the outbreak of COVID-19, a survey among 1573 medical workers from 10 hospitals in Xinjiang
reported a burnout prevalence of 63.32%28. Therefore, we concluded that the situation of burnout among
medical workers in China is serious and is of concern. More attention is still needed to ensure the ecient
implementation of prevention and control programs during controlled COVID-19 period.
Our results demonstrated that medical workers who have a master's degree or higher were more likely to
report burnout than those who have a bachelor's degree or lower. Lowe et al. reported that nurses in the
United States had higher levels of burnout when they had a higher educational level, which was in line
with this study. The possible explanation may be that medical workers with high educational level usually
have solid basic knowledge and a deep understanding of their specialty. As a result, they are more likely
to experience higher level of mental stress and tension as they have greater responsibilities and are
required to solve complex situations at work29.
In our study, health condition was an inuencing factor of burnout, which was in agreement with the
study among Chinese palliative nurses and healthcare professionals in Italy30,31. Medical workers in poor
health might not achieve the work performance and increase the risk of burnout. Therefore, in order to
decrease the burnout for medical workers, it is essential to adopt preventive interventions which focus on
identifying and improving health conditions.
The results of this study indicated that medical workers who had junior or middle professional title
experienced more burnout, consistent with previous ndings. Lu Y et al. found that the prevalence of job
burnout in middle job title was higher than that of other groups among biosafety laboratory staffs during
the COVID-19 epidemic in Xinjiang32. Additionally, Zhu X et al. found that nurses who were in junior
positions reported higher job burnout level33. We inferred this relationship as medical workers with junior
and middle titles had higher promotion pressure which could lead to long-term negative work emotions
and job burnout. This result suggests that we should pay more attention to the working status of medical
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workers with junior and middle job titles who were suffering from greater occupational pressure, and care
about their physical and mental conditions, so as to reduce the job burnout level of them.
In this study medical workers who had increased work intensity at centralized vaccination sites reported
higher level of burnout. Liu et al. found that higher work intensity could increase job burnout among
healthcare professionals during the COVID-19 outbreak in China27. Moreover, Amir et al. reported that
increased workload was a predictor of nurses' burnout in central Uganda during COVID-19 epidemic34.
Previous study has suggested that workload was associated with burnout and increase burnout
positively and signicantly. In this circumstance medical workers will be unable to perform their roles
effectively within the required time and this will lead to burnout35. Our results revealed that half of
medical workers were in charge of vaccination with higher work intensity. It was dicult for them to get
as much of a sense of accomplishment from the tedious and repetitive work as they did from saving lives
in hospitals. In addition, increased work intensity can drain mental and physical strength and may cause
exhaustion. Besides, lower income was found associated with a higher job burnout score, which was
consistent with the previous study36. Under high work pressure, accompanied by low income, such
situations can exacerbate negative feelings during work and lead to job burnout.
Social support is dened as assistance and protection given by others. It includes formal (supervisors) or
informal (family, coworkers)37. Yang S et al. observed that self-reported social support was negatively
associated with burnout syndrome in medical workers of southwest China38. A survey in nursing home
workers during the COVID-19 pandemic in Spain found that perceived social support had a protective
effect on burnout39. There is evidence that social support from occupational environment, such as
coworker and supervisor support can help reduce burnout, thus increase the quality of health care40. Our
study revealed that social support was an important factor independently associated with job burnout.
This relationship can be explained by the fact that social support is an important external work resource
that can prevent and reduce stress, alleviating occupational burnout. Social support can provide medical
workers with psychological support about the negative emotions related to work. In this way, they will
have encouragement which can help them gain stronger self-condence and improve their ability to cope
with stress, thereby reducing the occurrence of occupational burnout. Considering the protective effect of
social support on health41, job burnout can be prevented or alleviated by actively providing appropriate
social support for medical workers in varied ways. In addition, improving medical workers’ perception and
utilization of social support is an important measure to prevent and reverse burnout42.
However, this study has the following limitations. First, this study was a cross-sectional design. Job
burnout and associated factors were measured simultaneously. Therefore, it was impossible to draw
causal relationships between them. Second, the participant sample was from centralized vaccination
sites in Nanjing, China; which limited the applicability of the results to relevant populations in other cities.
Third, the proportion of female medical workers is relatively high, which requires that future studies
should focus on the proportion of medical staff. Finally, we did not collect the information of job
positions or whether the medical workers have dependents, what may contribute to job burnout. Further
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studies are required to elucidate the relationship between job position or dependents’ support and
burnout.
According to our results, from the perspective of medical workers, they are advised to seek mental health
screening43. Additionally, attending health campaigns such as mindfulness-based stress practice and
small-group programs may also be affective6. From the perspective of organizations and decision-
makers, we recommend that they should focus on reducing workload, improving income policy while
offering more social support for medical workers. On one hand, it is of great necessity to optimize the
proper ow of personnel within the department, and alleviate the work intensity by reducing working
hours44. On the other hand, social support can be improved through creating a good working atmosphere.
Specically, offering leadership commitment, strengthening education about the role of emotions and
maintaining a culture of appreciation45,46.
To the best of our knowledge, this is the rst study to investigate the job burnout among medical workers
at centralized vaccination sites during the controlled COVID-19 period. As a public health emergency,
COVID-19 has seriously affected medical workers’ physical and mental health in the past three years47,48.
Moreover, it takes time for medical workers to recover from the negative emotions that may contribute to
burnout during the post-acute crisis period49.Although the pandemic period of the COVID-19 has passed,
we cannot ignore the possibility of future public health emergencies. As a result, our research suggests
implementing targeted interventions and health policies that address job burnout in order to support
Chinese medical workers for vaccination throughout other similar health crises that may arise in the long
run.
CONCLUSION
In summary, job burnout among medical workers was common at centralized COVID-19 vaccination sites
in Nanjing, China during controlled COVID-19 period. The result of our study showed that high level of job
burnout was associated with high educational level, poor health condition, junior and middle job titles,
increased work intensity, lower income and low level of social support. Therefore, it is necessary to
provide social support at various levels and pay high attention to the physical and mental health of
medical workers. Efforts also should be made to rationally arrange the workload of medical workers,
thereby releasing job burnout and promote the quality of vaccination.
Declarations
Ethics approval and consent to participate
The study protocol was approved by the Ethics Review Committee of Nanjing Medical University (No.
FWA00001501).
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Consent for publication
We obtained consent for publication from all participants.
Availability of data and materials
The data involved in the study are available from the corresponding author on reasonable request.
Competing interests
The authors declare that the research was conducted in the absence of any commercial or nancial
relationships that could be construed as a potential conict of interest.
Fundings
This study was funded by the Science and Technology Climbing Engineering Scientic Research
Innovation Project from Nanjing Medical University (JX103SYL202200324), International MPH Research
Assistance Project of Nanjing Medical University,the Philosophy and Social Science Project in
Universities and Colleges of Jiangsu Province (2021SJA0305) and Nanjing Medical Science and
Technology Development Special Fund Project (GBX21314).
Authors' contributions
SJL designed the study and critically reviewed, commented on and revised important intellectual content.
NW, LLG, XPC, YNQ, MSFA, and JPC collect data. YNQ, LY and LJL conducted statistical analysis and
interpretation of the data. YNQ drafted the article. SJL has modied the draft grammatical sentences.
Acknowledgements
The authors thank all research participants.
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Tables
Table 1 Participants’ characteristics and association with job burnout among medical workers
Page 15/19
Variables Categories
N (%)
M±SD t/F p
Demographic characteristics
Age (year) <30 165
(38.8) 35.88±12.17 1.720 0.162
30-39 187
(44.0) 38.76±12.58
40-49 57
(13.4) 37.00±11.51
50 16
(3.8) 35.81±11.11
Gender Male 48
(11.3) 40.08±12.56 1.677 0.094
Female 377
(88.7) 36.94±12.19
Marital status Unmarried
*
116
(27.3) 34.34±11.45 -3.077 0.002
Married 309
(72.7) 38.40±12.39
Education level Bachelor
degree or
lower
403
(94.8) 36.89±12.14 -2.965 0.003
Master degree
or higher 22
(5.2) 44.77±12.38
Health condition Poor 19
(4.5) 54.47±7.42 59.578 <0.001
Fair 154
(36.2) 42.23±11.49
Good 252
(59.3) 32.98±10.66
Job characteristics
Job tenure (year) 10 241
(56.7) 37.14±12.73 -0.294 0.769
>10 184
(43.3) 37.49±11.64
Hours/day 6-7 57
(13.4) 34.88±10.71 6.448 0.002
8-9 237
(55.8) 36.16±12.64
Page 16/19
10 131
(30.8) 40.39±11.67
Increased work intensity Yes 372
(87.5) 38.64±11.84 -6.258 <0.001
No 53
(12.5) 27.85±11.00
Job title No 45
(10.6) 29.91±9.21 6.389 <0.001
Junior 203
(47.8) 38.17±12.48
Middle 142
(33.4) 37.95±12.15
Senior 35
(8.2) 39.03±12.08
Monthly income (RMB) 5,000 155
(36.5) 38.81±12.76 1.933 0.054
>5,000 270
(63.5) 36.43±11.90
COVID-19 related characteristics
Frequently concern about the latest
epidemic of COVID-19 Yes 403
(94.8) 36.90±12.24 2.854 0.005
No 22
(5.2) 44.50±10.57
Self-assessmentrisk of contracting
COVID-19 in the course of work Low 192
(45.2) 34.64±11.81 11.544 <0.001
Average 136
(32.0) 37.89±11.98
High 97
(22.8) 41.72±12.25
Vaccination of COVID-19 Yes 391
(92.0) 37.20±12.23 -0.525 0.600
No 34
(8.0) 38.35±12.22
Vaccination of COVID-19 for at least
one family member Yes 392
(92.2) 36.77±12.15 -3.095 0.002
No 33
(7.8) 43.58±12.00
Note.*Unmarried includes single, divorced and widowed.
Page 17/19
Table 2. Prevalence ofburnoutat different levels among medical workers
Groups
N %
No burnout 236 55.5
Slight burnout 122 28.7
Moderate burnout 53 12.5
Severe burnout 14 3.3
Table 3 Hierarchical multiple linear regression of job burnoutamong medical workers
Page 18/19
Variables (Reference Group) Model 1 Model 2 Model 3 Model 4
Demographic characteristics 
Gender (Male) Female -0.099*-0.093*-0.101*-0.072
Marital status (Unmarried) Married 0.065 0.037 0.047 0.047
Education level (Bachelor degree or
lower) Master degree
or higher 0.130** 0.128** 0.126** 0.116**
Health condition (Poor) Fair -0.492** -0.494** -0.466** -0.369**
Good -0.856** -0.805** -0.747** -0.610**
Job characteristics 
Job title (No) Junior - 0.233** 0.215** 0.180*
Middle - 0.212*0.204*0.178*
Senior - 0.124*0.128*0.0103
Hours/day (6-7) 8-9 - -0.056 -0.052 -0.061
10 - -0.050 -0.051 -0.060
Increased work intensity (No) Yes - 0.173** 0.169** 0.170**
Monthly income (5,000RMB) >5,000RMB -0.121** -0.123** -0.098*
COVID-19 related characteristics 
Frequently concern about the latest
epidemic of COVID-19 (No) Yes - - -0.109** -0.075
Self-assessmentrisk of contracting
COVID-19 in the course of work
(Low)
Average - - 0.043 0.028
High - - 0.056 0.043
Vaccination of COVID-19 for at least
one family member (Yes) No - - 0.116** 0.074
Social support - ----0.270**
0.250 0.311 0.339 0.401
Adjusted
0.242 0.291 0.313 0.376
F
28.007** 15.533** 13.065** 16.046**
Page 19/19
Note.
*
p
0.05, **
p
0.01. In model 1, demographic characteristics were added. In model 2, job
characteristics were added. In model 3, COVID-19 related characteristics were added. In model 4, social
support was added.
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