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O
RIGINAL
A
RTICLE
Association between resilience and burnout of
front-line nurses at the peak of the COVID-19
pandemic: Positive and negative affect as
mediators in Wuhan
Xiaoning Zhang,
1,2,3,4,5
Xue Jiang,
1
Pingping Ni,
1,6
Haiyang Li,
7
Chong Li,
8
Qiong Zhou,
1
Zhengyan Ou,
1
Yuqing Guo
1
and Junli Cao
4,5
1
School of Nursing, Xuzhou Medical University, Xuzhou,
2
School of Nursing, Capital Medical University, Beijing,
3
Department of Neonatology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou,
4
Jiangsu Province Key
Laboratory of Anesthesiology, Xuzhou Medical University, Xuzhou,
5
Jiangsu Province Key Laboratory of Anesthesia
and Analgesia Application Technology, Xuzhou Medical University, Xuzhou,
6
Department of Neonatology, Suqian
First Hospital, Suqian,
7
Development Planning Office, Xuzhou Medical University, Xuzhou, and
8
Graduate School,
Xuzhou Medical University, Xuzhou, China
ABSTRACT: The outbreak of coronavirus disease 2019 (COVID-19) is having a dramatic effect on
the mental health of healthcare workers (HCWs). Upon the emergence of the COVID-19 pandemic, the
Chinese government dispatched about 42 000 HCWs to Wuhan City and Hubei Province to fight this
pandemic. This study briefly examines front-line nurses who experienced burnout, with the main
objective of investigating the mediating roles of positive and negative affect in the relationship between
resilience and burnout in Wuhan hospitals at the peak of the COVID-19 pandemic. A total of 180
front-line nurses voluntarily participated via a social media group. They completed the online
questionnaires, including the Maslach Burnout Inventory-General Survey (MBI-GS), the Positive and
Negative Affect Schedule (PANAS), the Connor–Davidson Resilience Scale (CD-RISC), demographics,
and work-related characteristics. Structural equation modelling (SEM) analysis was used to examine
the mediating effect of positive and negative affect on the relationship between resilience and burnout.
The total prevalence of burnout was 51.7%, of which 15.0% were severe burnout. These preliminary
results revealed that positive and negative affect fully mediated the effects of resilience on burnout,
emotional exhaustion, depersonalization, and reduced personal accomplishment of front-line nurses. It
is necessary to know the impact of resilience on HCWs with burnout through the positive and negative
Correspondence: Xiaoning Zhang, School of Nursing, Xuzhou Medical University, 209 Tongshan Road, Xuzhou 221004, China. Email:
xiaoning.zhang@ucl.ac.uk
Junli Cao, Graduate School, Xuzhou Medical University, 209 Tongshan Road, Xuzhou 221004, China. Email: caojl0310@aliyun.com
The first four authors contributed equally to this work.
Declaration of Conflict of interest: The authors declare that they have no conflict interests.
Xiaoning Zhang, PhD.
Xue Jiang, Bachelor.
Pingping Ni, Bachelor.
Haiyang Li, Master.
Chong Li, Master.
Qiong Zhou, Bachelor.
Zhengyan Ou, Bachelor.
Yuqing Guo, Bachelor.
Junli Cao, PhD.
Accepted January 22 2021.
©2021 John Wiley & Sons Australia, Ltd
International Journal of Mental Health Nursing (2021) ,– doi: 10.1111/inm.12847
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affect of individual backgrounds and situations, and how policymakers can deploy resilience
interventions to support front-line HCWs.
KEY WORDS: burnout, COVID-19 pandemic, front-line nurses, positive and negative affect, re-
silience.
INTRODUCTION
The coronavirus disease 2019 (COVID-19) outbreak
continues to be a public health emergency of interna-
tional concern (PHEIC), which is having a dramatic
effect on the mental health of healthcare workers
(HCWs). Following the COVID-19 outbreak in
Wuhan, the Chinese government dispatched about
42000HCWstoWuhanCityandHubeiProvince
from other parts of the country to fight the COVID-19
outbreak, representing the largest deployment of
HCWs and medical resources in the world. The every-
day use of personal protective equipment (PPE),
including gloves, gowns, clothing, masks, respirators,
goggles, and face shields, is mandatory for HCWs at
all working times to protect patients and themselves,
when providing care, and off-work contact with others
is discouraged (Smereka & Szarpak, 2020). Front-line
HCWs are under high stress, experiencing physical
exhaustion, stigmatization, and increased symptoms of
anxiety and depression (Mokhtari et al., 2020), they
are at increased risk of COVID-19 infection and may
be quarantined, which is related to severe mental
health problems (Anmella et al., 2020). Front-line
HCWs dealing with traumatic events, making life and
death decisions, are exposed to unprecedented emo-
tionally distress situations, such as the high risk of
infection, longer shifts and excessive workload, and
increased vulnerability; thus, they are at a greater risk
of negative mental health outcomes (Rivas et al.,
2020). More than 70% of the nurses in the task force
of front-line HCWs dispatched to Wuhan and Hubei
Province originated from secondary and tertiary hospi-
tals in China. They were closely exposed to COVID-19
patients and thus may suffer from vicarious traumati-
zation (Li et al., 2020), as well as experience severe
burnout (Murat et al., 2020). To evaluate immediate
mental health impact on front-line nurses working in
the wake of the COVID-19 pandemic (Su Hong &
Xiaoming, 2020), the optimal structure of a mentally
healthy life for each front-line nurse needs to be
mapped out, which differs as a function of each indi-
vidual’s background and situation.
Understanding the effects of the COVID-19 pan-
demic on burnout in front-line HCWs is an urgent
necessity to provide resilience interventions aimed at
improving their well-being (Digby et al., 2020). The
severe acute respiratory syndrome (SARS) epidemic of
2003 had significant psychosocial effects on HCWs,
29% of them reported emotional exhaustion as mea-
sured by the General Health Questionnaire (GHQ-12),
and being a nurse was found to have the highest signif-
icant association with emotional exhaustion (adjusted
OR 2.8, 95% CI 1.5–5.5) (Nickell et al., 2004). HCWs
exposed to the emotional distress and negative affect of
COVID-19 patients and their families had a high risk
of long-term psychological problems (Barello et al.,
2020), and reported significant pressure and somatic
symptoms, as well as higher levels and rate of emo-
tional exhaustion than those observed during the SARS
outbreak (Maunder et al., 2006). In a national COVID-
19 study among physicians in the USA, 21.8% reported
burnout, 47.9 and 60.2% had symptoms of anxiety and
distress, and 10.6% showed depression symptoms
(Civantos et al., 2020). A survey of 12 596 nurses in
April 2020 found moderate emotional exhaustion dur-
ing the COVID-19 pandemic and indicated that front-
line nurses had higher burnout scores (Chen et al.,
2020). Front-line nurses in Istanbul had high levels of
stress and burnout, as well as moderate depression
(Murat et al., 2020). These effects of the COVID-19
pandemic on HCWs differed with respect to occupa-
tion and risk perception, in particular. Part-time
employment status was found to be significantly associ-
ated with emotional exhaustion (adjusted OR 2.6, 95%
CI 1.2–5.4) (Nickell et al., 2004). Lifestyle changing
conditions in the workplace affected by the SARS out-
break were significantly associated with emotional
exhaustion (adjusted OR 2.2, 95% CI 1.4–3.5) (Nickell
et al., 2004).
It should be emphasized that emotion-related affects
are normal reactions to the unpredictable and threaten-
ing COVID-19 pandemic, which has particularly
directly affected HCWs (Vinkers et al., 2020). HCWs
are vulnerable to emotional issues, especially nurses
who screened positive for acute stress, depression, and
©2021 John Wiley & Sons Australia, Ltd
2 X. ZHANG ET AL.
anxiety, 75% reported at least moderate insomnia
symptoms (Shechter et al., 2020). Additionally, col-
lected evidence suggests that HCWs have an increased
risk for developing negative affect, as well as an
increased likelihood of experiencing transient stress-re-
lated symptoms (de Burgos-Berdud et al., 2020; Lai
et al., 2020; Shanafelt et al., 2020). The negative affect
of hostility and distress caused by the COVID-19 threat
and quarantine increased the association with situa-
tional fears (Brooks et al., 2020). Negative affect was
linked to nervousness and irritation during the
COVID-19 pandemic, and positive and negative affect
may serve as mediators (Perez-Fuentes et al., 2020). A
recent systematic review and meta-analysis showed that
HCWs had a high prevalence of negative affect, such
as depression (22.8%), anxiety (23.2%), and insomnia
(34.3%) during the COVID-19 pandemic (Pappa et al.,
2020). This pandemic was also reported to increase
social isolation and loneliness (Sciences, 2020), which
are markers of poor functioning across negative affect
(Matthews et al., 2019). The stress resulting from social
disruptions and health-related threats during the
COVID-19 pandemic, such as isolation and loneliness,
may cause mental health issues, and trigger or exacer-
bate mental illnesses (Vahia et al., 2020). Front-line
HCWs experienced psychological issues (Maunder
et al., 2006), a particular case reported fearing the psy-
chological impact of quarantine after two weeks of the
COVID-19 lockdown (Barello et al., 2020).
There has been limited research on the mental
health issues of HCWs associated with the current
COVID-19 pandemic (Pappa et al., 2020). However,
the prospect of front-line HCWs becoming psychologi-
cally unwell was reported to be lower than that related
to the psychological response to the pandemic (Holmes
et al., 2020). Front-line HCWs are exposed to increases
in workplace pressure and are the most severely hit by
patients’ overload, and thus struggle to cope with the
psychological challenges and threats (Barello et al.,
2020). The psychological and social impact of the
COVID-19 pandemic on HCWs is related to significant
negative mental health outcomes (Du et al., 2020).
After exposure to a highly challenging pandemic,
HCWs have to face and combat the burnout with psy-
chological resilience (Greenberg et al., 2020). Resili-
ence can be used to lessen the negative effects of
burnout in health facility settings and to prevent poor
psychosocial outcomes in nurses (Kim & Windsor,
2015). Social isolation makes the COVID-19 pandemic
different from other PHEIC, and resilience may be a
more effective response to control this situation, which
is vital to cope with stress and stay in balance; thus,
there is a need for HCWs with burnout to explore the
importance of resilience support (Goh et al., 2020).
Accordingly, it is urgently needed to explore the
importance of resilience in HCWs during the current
COVID-19 pandemic and the implications for HCWs
with burnout.
In this COVID-19 pandemic time, all HCWs are
extremely important worldwide, and the public also
needs them for the future, they make decisions on
how to allocate limited medical resources and bal-
ance their own physical and mental illnesses (Holmes
et al., 2020). China is a good example of how to con-
duct a successful emergency response to the pan-
demic, research should be conducted collaboratively
and rapidly to deal with the growing mental health
threats to HCWs. Policymakers must acknowledge
the challenges HCWs face and minimize the psycho-
logical harms inherent in dealing with burnout, and
ensure that the work ahead does not affect long-term
risks. What is the association between burnout and
resilience interventions for front-line HCWs, and
how can such consequences be mitigated using posi-
tive and negative affect? The answer to these ques-
tions is that more research is needed, particularly on
the association between resilience and burnout and
the mediating effect of positive and negative affect
or mitigation of the changes on burnout. The aim of
this study was to determine the mediating role of
positive and negative affect in the relationship
between resilience and burnout in front-line nurses.
The conceptual framework and hypothesis (Fig. 1)
were according to a previous report (Stewart et al.,
2019) as follows: (i) There are positive links between
resilience and burnout, resilience is correlated with
positive and negative affect and burnout; and (ii)
positive affect and negative affect play a mediating
role in the relationship between resilience and burn-
out, and could be used in predicting burnout.
METHODOLOGY
Participants and procedures
The potential participants were front-line nurses who
were assigned to the task force in Wuhan City. There-
fore, purposive sampling was used to recruit partici-
pants between March and April 2020. All participants
were informed of voluntary participation (Merlo et al.,
2017). Anonymously collected sociodemographic data
included lifestyle habits, work-related characteristics
©2021 John Wiley & Sons Australia, Ltd
ASSOCIATION BETWEEN RESILIENCE AND BURNOUT: POSITIVE AND NEGATIVE AFFECT AS MEDIATORS 3
(years of work, weekly working hours, type of employ-
ment contract, etc.), and psychological status (burnout,
resilience, and positive and negative affect).
Measures
Sociodemographic and work-related characteristics
Sociodemographic characteristics included age, sex
(male, female), marital status (married, single), number
of children (none, 1, 2), and the level of education (as-
sociate degree, bachelor’s degree, and master’s degree).
Lifestyle habits included sleeping time (4-5 hours, 6-7
hours, >8 hours) and weight change (stable, weight
loss, and weight gain). Work-related characteristics
included years of working (0-1 years, 2-4 years, 5-9
years, >10 years), level of hospital (tertiary A hospital,
tertiary B hospital, secondary hospital), type of hospital
(public, private), type of employment contract (perma-
nent, indefinite), head nurse (yes, no), weekly working
hours (16-23 hours, 24-31 hours, 32-40 hours), patient-
to-nurse ratio (1-5, 6-10, 11-15), part-time job (yes,
no), and amount of time spent with patients daily (50-
75%, 30-49%, <29%).
Burnout
Burnout was assessed using the Maslach Burnout
Inventory-General Survey (MBI-GS), a validated ver-
sion for measuring burnout in HCWs (Suttle et al.,
2020; Wu et al., 2020). This instrument encompasses
emotional exhaustion (tiredness, somatic symptoms,
and decreased emotional resources), depersonalization
(developing negative, cynical attitudes, and impersonal
feelings), and reduced personal accomplishment (feel-
ings of incompetence, inefficiency, and inadequacy)
(Kumar, 2007). The MBI-GS comprises 16 items
scored on a seven-point Likert scale ranging from 0
(never) to 6 (everyday); choosing ’once a week’ or more
frequently to either item is considered as a positive
response and indicates burnout (Portero de la Cruz
et al., 2020). Scores are categorized as mild, moderate,
or severe using established cut-offs (Guo et al., 2018).
High scores for emotional exhaustion and depersonal-
ization and low ones for personal accomplishment indi-
cate the incidence of burnout (Li et al., 2020a; Portero
de la Cruz et al., 2020). The Chinese version of the
MBI-GS shows satisfactory validity and reliability, the
Cronbach alpha coefficient for the whole scale was
0.86 and for three dimensions was 0.896, 0.747, and
0.825, respectively (Li et al., 2014; Li & Shi, 2003).
Resilience
The resilience was measured by the Connor–Davidson
Resilience Scale (CD-RISC), a 25-item scale consisting
of three subdomains (strength, tenacity, and optimism)
that assesses resilience to cope with adversity (Connor
& Davidson, 2003). Each item is rated on a five-point
Likert scale from 0 (not true at all) to 4 (true nearly all
the time); total scores range from 0 to 100, with higher
scores indicate greater resilience (Hou et al., 2020;
Ramirez-Granizo et al., 2020). The participants
responded to each item based on life events during the
past month (Ramirez-Granizo et al., 2020). The Cron-
bach alpha coefficient of the Chinese version was 0.91
for the total score, indicating satisfactory reliability and
validity, and the scale shows sound psychometric prop-
erties (Yu & Zhang, 2007).
Positive and negative affect
The positive affect and negative affect were measured
using the Positive and Negative Affect Schedule
(PANAS) (Watson et al., 1988), a self-report instrument
comprised of 10-item subscales: negative affects (fear,
nervousness, irritability, hostility, and shame) and posi-
tive affects (enthusiasm, attentiveness, pride, hopeful-
ness, and contentment) (Ravyts et al., 2019).
Participants are required to rate the degree to which
they feel a particular emotion using a five-point Likert
scale ranging from 1 (very slightly or not at all) to 5
(extremely) (Watson et al., 1988). Higher scores indi-
cate a higher level of either positive or negative affect.
FIG. 1 Conceptual framework and hypotheses
©2021 John Wiley & Sons Australia, Ltd
4 X. ZHANG ET AL.
The PANAS showed satisfactory reliability and validity
among Chinese in an US context (Deng et al., 2020);
the Cronbach alpha coefficient for the whole scale was
0.76 and for the subscales of positive and negative
affect was 0.93 and 0.90, respectively.
Statistical analyses
Data were analysed using the Stata 15 software (Stata
Corporation, College Station, TX, USA) for Windows
and AMOS 23 (IBM Corporation, Armonk, NY, USA).
Descriptive analysis was conducted to summarize the
frequencies and percentages of the categorical variables
or the mean and standard deviation (SD) of the quanti-
tative variables. The relationships between sociodemo-
graphic, work-related, and psychological characteristics
were compared using the chi-square test for categorical
variables. Pearson’s correlation analysis was performed
to preliminarily test the correlations among resilience,
positive and negative affect, and burnout.
Structural equation modelling (SEM) was performed
in four models by the maximum-likelihood method
implemented in the SPSS Amos v23.0 software (IBM
Corp) to examine the mediating role of positive and
negative affect in the association between resilience
and burnout (Bollen, 1989). In this study, resilience is
the predictor, and model 1 included positive and nega-
tive affect as mediators in the relationship between
resilience and burnout. In models 2, 3, and 4, the
dependent variable was emotional exhaustion, reduced
personal accomplishment, and depersonalization,
respectively. The bootstrapping method was used to
confirm the statistical significance of the mediating
effect (Efron & Tibshirani, 1985), which determines
whether an independent variable is related to a depen-
dent variable through a third variable—the mediator.
In this study, resilience is the predictor, burnout is the
dependent variable, and the positive affect and nega-
tive affect are used as the mediator. This method
determined the bias-corrected 95% confidence interval
from 2000 samples; if the interval excludes zero, it rep-
resents the significance of the effects (Preacher &
Hayes, 2008). A P-value <0.05 for the results indicated
statistical significance. The internal consistency was
examined by Cronbach’s alpha (>0.60 was considered
acceptable) (Frankel et al., 2015).
Patient and public involvement
Due to limited access to front-line nurses, all of them
were isolated off-work, to improve the questionnaires,
the distribution of measures was constrained to a sub-
sample of front-line nurses mainly through communica-
tion with others by social media apps, who received
and completed the questionnaires via WeChat social
media groups. If participants did not answer the cur-
rent question, the WeChat applet could not jump to
the next question. The IP addresses identified and
eliminated duplicate participants. Response bias was
examined before analysing the data (e.g. clicking the
same answer and moving rapidly to the next question).
A pilot study for the questionnaires was conducted on
eight nurses to improve clarity, consistency, and valid-
ity, and those three questionnaires were not changed
after the pilot study, the data of the pilot study were
combined with the final sample.
The first page of the electronic questionnaire pro-
vided guidance, and informed consent was asked after
the study guidance, all participants signed their names
on informed consent forms before completing the
questionnaires, which included an electronic study
summary of the research purpose, expected outcomes,
procedures, benefits, risks, and option not to take part.
Participants were required to tick a box indicating their
informed consent to participate before taking the
online survey, they had the option to withdraw from
the study at any time, and the survey data were only
accessible to study personnel. The project was
approved for research ethics by the Xuzhou Medical
University Research Ethics Committee (ID number:
XZ20200225) and was conducted in accordance with
the Helsinki Declaration.
RESULTS
Characteristics of participants and the
distribution of burnout
The sociodemographic and work-related characteristics
of the participants are shown in Table 1. Among the
200 participants who were approached, 20 declined
and 180 responded and completed all three question-
naires (response rate 90.0%). The mean age of the 180
participants was 28 (SD =4.5), 78.3% were female,
68.3% were married, 90.0% held a bachelor’s degree,
and 81.7% were permanent employees. The marital
status, type of employment contract, years of work, and
patient-to-nurse ratio were significantly different
between participants with and without burnout
(P<0.05). Most of the participants (98.9%) had a full-
time nursing job, 76.6% reported having more than
5 years of work experience.
©2021 John Wiley & Sons Australia, Ltd
ASSOCIATION BETWEEN RESILIENCE AND BURNOUT: POSITIVE AND NEGATIVE AFFECT AS MEDIATORS 5
Descriptive analysis of burnout, resilience, and
positive and negative affect
The total prevalence of burnout was 51.7%, of which
15.0% were severe burnout in Table 1. As shown in
Table 2, the mean scores for emotional exhaustion,
depersonalization, and reduced personal accomplish-
ment were 5.30 (SD =3.13), 2.91 (SD =2.68), and
2.33 (SD =2.62), respectively. The mean scores for
resilience tenacity, strength, and optimism were 34.23
TABLE 1 Sociodemographic and work-related characteristics of the participants and the distributions of dimensions of burnout (N=180)
Variables
Burnout
N(%) v
2
value P-valueNo (n=87) Yes (n=93)
Age
<27 years 19 (55.9) 15 (44.1) 34 (18.9) 4.136 0.247
28–30 years 37 (54.4) 31 (45.6) 68 (37.8)
31–35 years 19 (38.8) 30 (61.2) 49 (27.2)
>36 years 12 (41.4) 17 (58.6) 29 (16.1)
Gender
Male 15 (38.5) 24 (61.5) 39 (21.7) 1.943 0.163
Female 72 (51.1) 69 (48.9) 141 (78.3)
Marital status
Single 21 (36.8) 36 (63.2) 57 (31.7) 4.411 0.036
Married 66 (53.7) 57 (46.3) 123 (68.3)
Number of children
0 33 (47.8) 36 (52.2) 69 (38.3) 1.960 0.375
1 29 (43.3) 38 (56.7) 67 (37.2)
2 25 (56.8) 19 (43.2) 44 (24.4)
Level of education
Associate degree 6 (46.2) 7 (53.8) 13 (7.2) 0.400 0.927
Bachelor’s degree 78 (48.1) 84 (51.9) 162 (90.0)
Master’s degree 3 (60.0) 2 (40.0) 5 (2.8)
Type of employment contract
Permanent 77 (52.4) 70 (47.6) 147 (81.7) 5.260 0.022
Indefinite 10 (30.3) 23 (69.7) 33 (18.3)
Type of hospital
Public 79 (49.1) 82 (50.9) 161 (89.4) 0.330 0.566
Private 8 (42.1) 11 (57.9) 19 (10.6)
Amount of time spent with patients daily
50–75% 28 (47.5) 31 (52.5) 59 (32.8) 0.889 0.641
30–49% 40 (51.9) 37 (48.1) 77 (42.8)
<29% 19 (43.2) 25 (56.8) 44 (24.4)
Full-time job
No 2 (100.0) 0 (0.0) 2 (1.1) 0.232
Yes 85 (47.8) 93 (52.2) 178 (98.9)
Level of hospital
Tertiary A hospital 62 (53.9) 53 (46.1) 115 (63.9) 4.232 0.120
Tertiary B hospital 6 (33.3) 12 (66.7) 18 (10.0)
Secondary hospital 19 (40.4) 28 (59.6) 47 (26.1)
Head nurse
Yes 25 (59.5) 17 (40.5) 42 (23.3) 2.747 0.097
No 62 (44.9) 76 (55.1) 138 (76.7)
Years of working
0–1 years 0 (0.0) 8 (100.0) 8 (4.4) 12.244 0.005
2–4 years 22 (64.7) 12 (35.3) 34 (18.9)
5–9 years 43 (49.4) 44 (50.6) 87 (48.3)
>10 years 22 (43.1) 29 (56.9) 51 (28.3)
(Continued)
©2021 John Wiley & Sons Australia, Ltd
6 X. ZHANG ET AL.
(SD =7.61), 21.86 (SD =4.40), and 11.57 (SD =2.59),
respectively. The mean scores for positive affect and
negative affect were 30.74 (SD =6.24) and 21.44
(SD =.24), respectively.
Pearson’s correlation analyses
The analysis of the Pearson correlation coefficients
is shown in Table 3. Resilience showed significant
negative correlations with burnout (r=0.387,
P<0.01), emotional exhaustion (r=0.283,
P<0.01), depersonalization (r=0.400, P<0.01),
reduced personal accomplishment (r=0.388,
P<0.01), and negative affect (r=0.609, P<0.01);
and showed significant positive correlation with posi-
tive affect (r=0.224, P<0.01). Negative affect had
a significant positive correlation with burnout
(r=0.268, P<0.01), emotional exhaustion
(r=0.151, P<0.05), depersonalization (r=0. 297,
P<0.01), and reduced personal accomplishment
(r=0. 302, P<0.01). Positive affect had a signifi-
cant negative correlation with burnout (r=0.465,
p<0.01), emotional exhaustion (r=0.486,
P<0.01), depersonalization (r=0.387, P<0.01),
and reduced personal accomplishment (r=0.389,
P<0.01).
TABLE 1 (Continued)
Variables
Burnout
N(%) v
2
value P-valueNo (n=87) Yes (n=93)
Sleeping time
4–5 hours 16 (66.7) 8 (33.3) 24 (13.3) 3.812 0.149
6–7 hours 68 (45.3) 82 (54.7) 150 (83.3)
>8 hours 3 (50.0) 3 (50.0) 6 (3.3)
Weekly working hours
16–23 hours 4 (44.4) 5 (55.6) 9 (5.0) 0.287 0.901
24–31 hours 26 (51.0) 25 (49.0) 51 (28.3)
32–40 hours 57 (47.5) 63 (52.5) 120 (66.7)
Patient-to-nurse ratio
1–5 57 (45.2) 69 (54.8) 126 (70.0) 26.737 <0.001
6–10 10 (29.4) 24 (70.6) 34 (18.9)
11–15 20 (100.0) 0 (0.0) 20 (11.1)
Weight change
Stable 28 (44.4) 35 (55.6) 63 (35.0) 0.671 0.715
Weight loss 27 (51.9) 25 (48.1) 52 (28.9)
Weight gain 32 (49.2) 33 (50.8) 65 (36.1)
Prevalence Level
Burnout 93 (51.7) Mild Moderate Severe
41 (44.1) 38 (40.9) 14 (15.0)
TABLE 2 Mean and standard deviations of dimensions of burnout, resilience, and PANAS
a
(N=180)
Variables Mean SD Min. Max. Skewness Kurtosis
Burnout
Emotional exhaustion 5.30 3.13 0.00 12.00 0.11 0.84
Depersonalization 2.91 2.68 0.00 11.00 0.76 0.11
Reduced personal accomplishment 2.33 2.62 0.00 12.00 1.48 2.35
Resilience
Tenacity 34.23 7.61 9.00 49.00 0.25 0.89
Strength 21.86 4.40 6.00 32.00 0.47 1.42
Optimism 11.57 2.59 3.00 16.00 0.44 0.64
PANAS
Positive affect 30.74 6.24 13.86 44.00 0.30 0.44
Negative affect 21.44 5.08 12.00 41.14 0.98 2.29
a
Positive and Negative Affect Scale.
©2021 John Wiley & Sons Australia, Ltd
ASSOCIATION BETWEEN RESILIENCE AND BURNOUT: POSITIVE AND NEGATIVE AFFECT AS MEDIATORS 7
Structural equation modelling (SEM)
The mediating role of positive and negative affect in the
relationship between resilience and burnout
The standardized direct, indirect, and total effect esti-
mates of resilience on burnout in model 1 are pre-
sented in Table 4, and the path diagrams labelled on
each path of the relationship between resilience and
burnout are shown in Fig. 2. In model 1, resilience
had a significant indirect effect (b=0.322, P<0.05)
and total effect (b=0.361, P<0.05) on burnout,
and the path coefficient of the direct effect was
0.039 (BC 95% CI: 0.205, 0.108). The path coeffi-
cient of the indirect effect of resilience on burnout
TABLE 3 Correlations among resilience, emotional exhaustion, depersonalization, reduced personal accomplishment, and negative and positive
affect (N=180)
Variables
Emotional
exhaustion Depersonalization
Reduced personal accom-
plishment
Negative
affect
Positive
affect Resilience Burnout
Emotional exhaustion 1.000
Depersonalization 0.698*
*
1.000
Reduced personal
accomplishment
0.707*
*
0.853*
*
1.000
Negative affect 0.151*0.297*
*
0.302*
*
1.000
Positive affect –0.486*
*
–0.387*
*
–0.389*
*
0.231*
*
1.000
Resilience –0.283*
*
–0.400*
*
–0.388*
*
–0.609*
*
0.224*
*
1.000
Burnout 0.891*
*
0.923*
*
0.926*
*
0.268*
*
0.465*
*
0.387*
*
1.000
*P<0.05, **P<0.01
TABLE 4 Total, direct, and indirect effects of resilience on burnout (N=180)
Effects Paths Effect SE P BC 95% CI
Indirect effects Resilience ?Positive affect ?Burnout –0.113 0.047 0.013 –0.209 to –0.019
Resilience ?Negative affect ?Burnout –0.209 0.061 0.001 –0.353 to –0.108
Direct effects Resilience ?Burnout –0.039 0.080 0.584 –0.205 to 0.108
Resilience ?Positive affect 0.224 0.091 0.013 0.038 to 0.403
Resilience ?Negative affect –0.609 0.065 0.001 –0.723 to –0.470
Positive affect ?Burnout –0.505 0.040 0.002 –0.580 to –0.418
Negative affect ?Burnout 0.343 0.085 0.001 0.177 to 0.515
Total effect Resilience ?Burnout –0.361 0.051 0.001 –0.458 to –0.259
SE, standard error; BC 95% CI, bias-corrected 95% confidence intervals.
FIG. 2 Mediating roles of positive and negative affect between resilience and burnout in Model 1. *P<0.05, **P<0.01
©2021 John Wiley & Sons Australia, Ltd
8 X. ZHANG ET AL.
through positive and negative affect was 0.113 (BC
95% CI: 0.209, 0.019) and 0.209 (BC 95% CI:
0.353, 0.108), respectively. Burnout was directly
and negatively associated with positive affect
(b=0.505, P<0.01), but had a direct and positive
association with negative affect (b=0.343, P<0.01).
Since the direct effect of resilience to burnout
included zero, which was insignificant, the positive and
negative affect fully mediated the effects of resilience
on burnout.
The mediating role of positive and negative affect in the
relationship between resilience and burnout constructs
The standardized direct, indirect, and total effect esti-
mates of resilience on emotional exhaustion in model 2
are presented in Table 5, and the path diagrams
labelled on each path of the relationship between resi-
lience and emotional exhaustion are shown in Fig. 3.
In model 2, resilience had a significant indirect effect
(b=0.286, P<0.05) and total effect (b=0. 268,
P<0.05) on emotional exhaustion and the path coeffi-
cient of the direct effect was 0.018 (BC 95% CI:
0.169, 0.189). The path coefficient of the indirect
effect of resilience on emotional exhaustion through
positive and negative affect was 0.118 (BC 95% CI:
0.228, 0.021) and 0.168 (BC 95% CI: 0.300,
0.060), respectively. Emotional exhaustion was
directly and negatively associated with positive affect
(b=0.527, P<0.01), but had a direct and positive
association with negative affect (b=0.276, P<0.01).
Since the direct effect of resilience to emotional
exhaustion included zero, which was insignificant, the
positive and negative affect fully mediated the effects
of resilience on emotional exhaustion.
The standardized direct, indirect, and total effect
estimates of resilience on reduced personal accomplish-
ment in model 3 are presented in Table 6, and the
path diagrams labelled on each path of the relationship
between resilience and reduced personal accomplish-
ment are shown in Fig. 4. In model 3, resilience had a
significant indirect effect (b=0.314, P<0.05), and
total effect (b=0.365, P<0.05) on reduced personal
accomplishment and the path coefficient of the direct
effect was 0.051 (BC 95% CI: 0.214, 0.096). The
TABLE 5 Total, direct, and indirect effects of resilience on emotional exhaustion (N=180)
Effects Paths Effect SE P BC 95% CI
Indirect effects Resilience ?Positive affect ?Emotional exhaustion –0.118 0.052 0.012 –0.228 to –0.021
Resilience ?Negative affect ?Emotional exhaustion –0.168 0.061 0.004 –0.300 to –0.060
Direct effects Resilience ?Emotional exhaustion 0.018 0.090 0.870 –0.169 to 0.189
Resilience ?Positive affect 0.224 0.091 0.013 0.038 to 0.403
Resilience ?Negative affect –0.609 0.065 0.001 –0.723 to –0.470
Positive affect ?Emotional exhaustion –0.527 0.057 0.001 –0.627 to –0.405
Negative affect ?Emotional exhaustion 0.276 0.094 0.007 0.081 to 0.451
Total effects Resilience ?Emotional exhaustion –0.268 0.061 0.001 –0.387 to –0.150
SE, standard error; BC 95% CI, bias-corrected 95% confidence intervals.
FIG. 3 Mediating roles of positive and negative affect between resilience and emotional exhaustion in Model 2. *P<0.05, **P<0.01
©2021 John Wiley & Sons Australia, Ltd
ASSOCIATION BETWEEN RESILIENCE AND BURNOUT: POSITIVE AND NEGATIVE AFFECT AS MEDIATORS 9
path coefficient of the indirect effect of resilience on
reduced personal accomplishment through positive and
negative affect was 0.098 (BC 95% CI: 0.182,
0.017) and 0.216 (BC 95%CI: 0.366, 0.103),
respectively. Reduced personal accomplishment was
directly and negatively associated with positive affect
(b=0.437, P<0.01), but had direct and positive
association with negative affect (b=0.355, P<0.01).
Since the direct effect of resilience to reduced personal
accomplishment included zero, which was insignificant,
the positive and negative affect fully mediated the
effects of resilience on reduced personal accomplish-
ment.
The standardized direct, indirect, and total effect
estimates of resilience on depersonalization in model 4
are presented in Table 7, and the path diagrams
labelled on each path of the relationship between resi-
lience and depersonalization are shown in Fig. 5. In
model 4, resilience had a significant indirect effect
(b=0.294, P<0.05) and total effect (b=0.379,
TABLE 6 Total, direct, and indirect effects of resilience on reduced personal accomplishment (N=180)
Effects Paths Effect SE P BC 95% CI
Indirect effects Resilience ?Positive affect ?Reduced personal accomplishment –0.098 0.041 0.012 –0.182 to –0.017
Resilience ?Negative affect ?Reduced personal accomplishment –0.216 0.066 0.001 –0.366 to –0.103
Direct effects Resilience ?Reduced personal accomplishment –0.051 0.078 0.502 –0.214 to 0.096
Resilience ?Positive affect 0.224 0.091 0.013 0.038 to 0.403
Resilience ?Negative affect –0.609 0.065 0.001 –0.723 to –0.470
Positive affect ?Reduced personal accomplishment –0.437 0.041 0.001 –0.515 to –0.358
Negative affect ?Reduced personal accomplishment 0.355 0.090 0.001 0.176–0.530
Total effects Resilience ?Reduced personal accomplishment –0.365 0.046 0.001 –0.446 to –0.267
SE, standard error; BC 95% CI, bias-corrected 95% confidence intervals.
FIG. 4 Mediating roles of positive and negative affect between resilience and reduced personal accomplishment in Model 3. *P<0.05,
**P<0.01
TABLE 7 Total, direct, and indirect effects of resilience on depersonalization (N=180)
Effects Paths Effect SE P BC 95% CI
Indirect effects Resilience ?Positive affect ?Depersonalization –0.095 0.039 0.009 –0.176 to –0.019
Resilience ?Negative affect ?Depersonalization –0.200 0.062 <0.001 –0.351 to –0.097
Direct effects Resilience ?Depersonalization –0.085 0.084 0.349 –0.249 to 0.085
Resilience ?Positive affect 0.224 0.091 0.013 0.038 to 0.403
Resilience ?Negative affect –0.609 0.065 0.001 –0.723 to –0.470
Positive affect ?Depersonalization –0.424 0.051 0.001 –0.519 to –0.321
Negative affect ?Depersonalization 0.328 0.089 0.001 0.159 to 0.513
Total effects Resilience ?Depersonalization –0.379 0.049 0.001 –0.474 to –0.278
SE, standard error; BC 95% CI, bias-corrected 95% confidence intervals.
©2021 John Wiley & Sons Australia, Ltd
10 X. ZHANG ET AL.
P<0.05) on depersonalization and the path coefficient
of the direct effect was 0.085 (BC 95%CI: 0.249,
0.085). The path coefficient of the indirect effect of
resilience on depersonalization through positive and
negative affect was 0.095 (BC 95%CI: 0.176,
0.019) and 0.200 (BC 95%CI: 0.351, 0.097),
respectively. Depersonalization was directly and nega-
tively associated with positive affect (b=0.424,
P<0.01), but had a direct and positive association with
negative affect (b=0.328, P<0.01). Since the direct
effect of resilience to reduced personal accomplish-
ment included zero, which was insignificant, the posi-
tive and negative affect fully mediated the effects of
resilience on depersonalization.
DISCUSSION
HCWs across the world, working under extreme pres-
sure, are subjected to unprecedented stressful situa-
tions during the COVID-19 pandemic (Holmes et al.,
2020). There is growing evidence regarding the effect
of burnout among front-line nurses during the
COVID-19 pandemic (Chen et al., 2020; Sarboozi
Hoseinabadi et al., 2020). The excellent resilience
preparation of front-line nurses and the associated pos-
itive affect can reduce the risk of burnout. This study
proposes the prioritization and coordination of policy-
relevant research on burnout and positive and negative
affect, to ensure that resilience interventions are effi-
ciently targeted for the optimal mental health of front-
line HCWs as the COVID-19 pandemic unfolds. To
the best of our knowledge, this is the first study to
explore the mediating role of positive and negative
affect in the relationship between resilience and burn-
out among front-line nurses at the peak of the
COVID-19 pandemic.
Quarantine is associated with social and psychologi-
cal risks for mental health (Brooks et al., 2020), and is
a predictor of post-traumatic stress in HCWs even
3 years later (Wu et al., 2009). HCWs exposed to
SARS were immediately quarantined, which was a sev-
ere predictor of acute stress disorder (Bai et al., 2004).
Quarantined HCWs were significantly more likely to
report emotion exhaustion, and detachment from fam-
ily when caring for COVID-19 patients, and may have
a deteriorating work performance, show reluctance to
work, and suffer negative affect, such as irritability
(Brooks et al., 2020). Individuals who experienced liv-
ing with COVID-19 patients were concerned about
social isolation and increased negative affect (Holmes
et al., 2020). The effects of social isolation on the well-
being of HCWs should be widespread concerned with
increased negative affect and the practical implications
of pandemic resilience interventions (Perez-Fuentes
et al., 2020). A sense of losing direct social contacts
(Usher et al., 2020) is reported, as these front-line
nurses living in a single room off-work communicate
with their families or friends via video or audio calls.
Deploying mental health researchers to the pandemic
area may help the formation of emotional change initia-
tives aimed at reducing the spread of the burnout
among front-line nurses. Quarantine management dur-
ing the COVID-19 pandemic is crucial to understand
the burnout through positive and negative affect of
front-line HCWs to conduct resilience interventions
earlier.
Positive and negative affect can be influenced by
uncertainty, which requires making highly challenging
decisions to mitigate the risk of mental health issues
(Brooks et al., 2020). Increased negative affect in
response to the pandemic should be highlighted, and
front-line HCWs are exposed to traumatic events, such
FIG. 5 Mediating roles of positive and negative affect between resilience and depersonalization in Model 4. *P<0.05, **P<0.01
©2021 John Wiley & Sons Australia, Ltd
ASSOCIATION BETWEEN RESILIENCE AND BURNOUT: POSITIVE AND NEGATIVE AFFECT AS MEDIATORS 11
as a large number of deaths and severe cases (Li et al.,
2020). Acute or long-term mental impacts of the
COVID-19 pandemic on HCWs, worries, and uncer-
tainties about this pandemic are common, and cause
undue emotional distress and impairment to social and
occupational functioning (Brooks et al., 2020). HCWs
reported they were exposed to difficulty solving prob-
lems in emergency care and situations for which they
felt unprepared, and which may be similar to the
unprecedented COVID-19 pandemic challenges HCWs
are currently facing (Brooks et al., 2020). The COVID-
19 pandemic may cause HCWs to experience moral
injury (Greenberg et al., 2020), which is defined as the
emotional distress that violates the moral or ethical rule
(Litz et al., 2009). HCWs are likely to experience nega-
tive affect, feeling intensely afraid, ashamed, irritable,
or hostile, which can contribute to the development of
burnout (Williamson et al., 2018). These front-line
nurses reported fear being compulsorily required to
use PPE during work time, using elastomeric respira-
tor, and rated the respirator less favourably with
respect to comfort, and preferred to use them only in
certain higher risk situations (Hines et al., 2019),
although they can accept less convenience, but more
protective PPE to provide care for COVID-19 patients
(Hines et al., 2020). HCWs were affected and hurt by
experiencing the pandemic, no one is invulnerable,
they need to be resilient and prepared, and policies
need to provide resilience support for burnout through
negative affect.
There is a risk that the prevalence of burnout will
increase, and coping emergency responses to burnout
are expected during this extraordinary pandemic (Gun-
nell et al., 2020). Burnout may be caused by organiza-
tional and individual attributes, and organizational
attributes or health facility settings of front-line nurses
contribute to psychological distress (Cusack et al.,
2016). Individual attributes act as internal factors, such
as optimism and hope, spirituality, and a positive iden-
tity (Rees et al., 2015). Structuring effective, individual-
ized resilience interventions for coping in this
pandemic is critical (Folkman & Moskowitz, 2004).
The organizational attributes are mostly characterized
by social support, a supportive workplace environment,
and interventions (Delgado et al., 2017). Individual
attributes and organizational resources of front-line
nurses should be used by policymakers to manage
workplace adversity, including self-reliance, passion
and interest, positive thinking, and emotional intelli-
gence as self-efficacy mechanisms (Holmes et al.,
2020). Most HCWs found that support from colleagues
and leaders may protect their mental health, and man-
agers should reach out to HCWs who avoid attending
peer discussions (Holmes et al., 2020). Monitoring the
incidence of real negative affect is necessary, as burn-
out might be hidden, managers should keep a resilient
eye on burnout through negative affect. If negative
affect of front-line nurses is persistent or severe, the
awareness of its mediating roles in the relationship
between resilience and burnout individually is
required. As the situation progresses, managers should
help front-line nurses make sense of the burnout chal-
lenges through positive affect to safely provide discus-
sions on resilience.
The resilience interventions may be overwhelmed
by the pandemic, and this has personal relevance;
HCWs experience genuine feelings of negative affect
and burnout may affect their mental health. Front-line
HCWs need both managerial and psychological support
to balance mental issues and resilience in order to deal
with the COVID-19 pandemic (Digby et al., 2020).
This study advocates for an increased attention to
burnout in HCWs and highlights positive affect strate-
gies associated with resilience to be applied during the
COVID-19 pandemic (Vinkers et al., 2020). Several
potential resilience interventions can help mitigate the
burnout through positive and negative affect, by reli-
ably and iteratively delivering mental health, such as
with digital resources to efficiently manage the mental
health of HCWs in an adaptive and resilience manner
(Wind et al., 2020), which requires a range of interna-
tional collaborative research (Holmes et al., 2018).
Front-line nurses who provided care for COVID-19
patients, developed burnout, and associated mental
health problems should be aware of the consequences
of the avoidance of speaking about negative affect (Sal-
kovskis et al., 1999). The government bodies, scientific
organizations, and mental health providers, besides
providing funds for longer-term resilience interven-
tions, are important to protect the mental well-being of
HCWs, which may include eliciting community (Daw-
son et al., 2015), peer support (Zhao et al., 2019), and
assertiveness training (Solomonov et al., 2019). Due to
quarantine policy, digital resilience interventions, such
as social media apps, are mechanistically buffering
effects of burnout through positive affect to alleviate
burnout. HCWs who persistently avoid meetings or
experience negative affect may require sensitive and
positive discussion, and support from suitably experi-
enced or trained peers. HCWs have greater difficulty
in accessing mental health services during the pan-
demic, this study combined with those in the literature
©2021 John Wiley & Sons Australia, Ltd
12 X. ZHANG ET AL.
informs the development of research priorities. The
positive affect can be leveraged by resilience interven-
tions to mitigate and minimize the burnout, to boost
the well-being across the whole task force of HCWs
during the pandemic.
Monitoring the level of burnout through the positive
and negative affect of front-line HCWs is beneficial to
understand mediating mechanisms. It is needed to
understand how front-line HCWs can be supported to
optimize resilience strategies to mitigate burnout
through positive affect, and facilitate the resilience
interventions (Duan & Zhu, 2020). Research is needed
to deploy available resilience resources to HCWs,
which can be important for mitigating burnout through
positive affect on HCWs (Sehmi et al., 2019). It is criti-
cal that front-line HCWs are resilience and positively
supported, and personalized resilience interventions
are likely to be a key component to mitigate burnout
(Usher et al., 2020). The optimal resilience interven-
tions should be tailored to multiple front-line HCW
groups, deployed appropriate mental health resource,
and multidimensional perspectives. Once the COVID-
19 pandemic is over, policymakers should ensure that
they reflect on and learn from the difficult healthcare
experiences (Usher et al., 2020), rapidly learning from
existing successful resilience strategies to integrate the
proper medical and resilience resources and promoting
optimal mental health among HCWs, moving forward
to any future periods of the pandemic.
Resilience interventions and mental health support
initiatives aimed at reducing the burnout, which rec-
ommend active screening for burnout in front-line
HCWs during this pandemic (de Burgos-Berdud et al.,
2020). This study provides a mediating strategy, which
can be integrated with research efforts worldwide, tar-
geting mediators between resilience interventions and
burnout. This pandemic fosters the connection of glo-
bal researchers to support an international response to
the challenges and mental health of HCWs and should
be harnessed to benefit both society and the public in
the long term. Rising to this challenge, the social and
psychological effects of this pandemic should be coor-
dinated at a national and international level. Interna-
tional research funding is needed to establish a high-
level national and international group, mental health of
HCWs can be effectively leveraged by the world lead-
ing global infrastructures, and that firm evidence can
be translated into effective resilience interventions. The
creation of high-quality database of the psychological
effects of current and future pandemics on the whole
task force of front-line HCWs, detailing how to
implement a coordinated emergency response regard-
ing resilience interventions, should be brought together
under a national or international open data portal.
Limitations
This study has some limitations worth noting. Data
were obtained from self-reported questionnaires, all
front-line nurses were isolated off-work, and social
media is their main ways of communication with the
outside world, potential selection bias and the political
sway of social media should be considered. The results
of this study are preliminary and only represent a snap-
shot of the current mental status of front-line nurses,
and their validity needs to be further confirmed. The
sample of this study is not representative of all the
front-line nurses, and generalization of the results is
limited. Burnout may have been greater and sustained
among front-line HCWs during and after the COVID-
19 pandemic, longitudinal studies should be conducted
to examine long-term effects of burnout through posi-
tive and negative affect, by performing prolonged fol-
low-up of HCWs with a short-lived burnout to avoid
relapse from mental health problems (Rivas et al.,
2020). A small-size and localized research does not
benefit the need to guide policymakers, we must build
and harness longitudinal and international data sets
with new research strategies for recording detailed psy-
chological information (Holmes et al., 2018).
Conclusion
This study revealed the association between resilience
and burnout in front-line nurses, with positive and neg-
ative affect acting as mediators at the peak of the
COVID-19 pandemic. This study proposes to set out
immediate and longer-term resilience strategies for
front-line HCWs, and provides early insight into the
urgent need to support HCWs who are at higher risk
of negative affect to avoid burnout, through positive
affect to alleviate burnout. Immediate insights provide
evidence-based guidance for resilience interventions
responding to burnout through positive and negative
affect in HCWs to improve well-being during pan-
demics. The evidence is valuable for preventing burn-
out and guiding studies to creating resilience
interventions for front-line HCWs in future pandemics.
This study strongly recommends providing timely resili-
ence interventions to mitigate the massive mental
health impact of this pandemic on the current and
future well-being of HCWs.
©2021 John Wiley & Sons Australia, Ltd
ASSOCIATION BETWEEN RESILIENCE AND BURNOUT: POSITIVE AND NEGATIVE AFFECT AS MEDIATORS 13
RELEVANCE FOR CLINICAL PRACTICE
•Association between resilience and burnout of front-
line nurses, and positive and negative affect act as
mediators at the peak of the COVID-19 pandemic in
Wuhan.
•This study reveals immediate insights to provide evi-
dence on resilience interventions to support front-
line nurses with burnout through positive and nega-
tive affect.
•Further research is needed to confirm findings on
resilience interventions for burnout nurses during
the pandemic mediated by positive and negative
affect. HCWs can learn from previous pandemics on
how to become more resilient to cope with burnout.
ACKNOWLEDGEMENTS
The authors would like to thank all the front-line
nurses who participated in this study. We would like to
thank all the front-line healthcare workers for their
contribution to the world during this crisis.
ETHICS STATEMENT
Human participation was approved by the Xuzhou
Medical University Research Ethics Committee (ID
number: XZ20200225).
INFORMED CONSENT
Participants provided signed consent online.
FUNDING
This research was funded by the China Postdoctoral
Science Foundation (2020M670077ZX) and the Jiangsu
Planned Projects for Postdoctoral Research Funds. The
funders played no role in the conduct of the research
or preparation of the manuscript.
CONSENT FOR PUBLICATION
Not applicable.
Data Availability Statement
The data sets that support the findings of this study are
available from the corresponding author upon reason-
able request.
REFERENCES
Anmella, G., Fico, G., Roca, A. et al. (2020). Unravelling
potential severe psychiatric repercussions on healthcare
professionals during the COVID-19 crisis. Journal of
Affective Disorders,273, 422–424.
Bai, Y., Lin, C. C., Lin, C. Y., Chen, J. Y., Chue, C. M. &
Chou, P. (2004). Survey of stress reactions among health
care workers involved with the SARS outbreak.
Psychiatric Services (Washington, D. C.),55, 1055–1057.
Barello, S., Palamenghi, L. & Graffigna, G. (2020). Burnout
and somatic symptoms among frontline healthcare
professionals at the peak of the Italian COVID-19
pandemic. Psychiatry Research,290, 113129.
Bollen, K. A. (1989). Structural equations with latent
variables. New York New York John Wiley & Sons,35,
289–308.
Brooks, S. K., Webster, R. K., Smith, L. E. et al. (2020). The
psychological impact of quarantine and how to reduce it:
Rapid review of the evidence. Lancet,395, 912–920.
Chen, R., Sun, C., Chen, J. J. et al. (2020). A large-scale survey
on trauma, burnout, and posttraumatic growth among nurses
during the COVID-19 pandemic. International Journal of
Mental Health Nursing,30,102–116.
Civantos, A. M., Byrnes, Y., Chang, C. et al. (2020). Mental
health among otolaryngology resident and attending
physicians during the COVID-19 pandemic: National
study. Head and Neck,42, 1597–1609.
Connor, K. & Davidson, J. (2003). Development of a new
resilience scale: The Connor-Davidson Resilience Scale
(CD-RISC). Depression and Anxiety,18,76–82.
Cusack, L., Smith, M., Hegney, D. et al. (2016). Exploring
environmental factors in nursing workplaces that promote
psychological resilience: Constructing a unified theoretical
model. Frontiers in Psychology,7, 600.
Dawson, K. S., Bryant, R. A., Harper, M. et al.(2015).
Problem Management Plus (PM+): A WHO
transdiagnostic psychological intervention for common
mental health problems. World Psychiatry,14, 354–357.
De Burgos-Berdud, I., Valdes-Florido, M. J. & Lopez-Diaz,
A. (2020). Are healthcare workers during the COVID-19
pandemic at risk of psychosis? Findings from a scoping
review. General Hospital Psychiatry. https://doi.org/10.
1016/j.genhosppsych.2020.06.015. [Epub ahead of print].
Delgado, C., Upton, D., Ranse, K., Furness, T. & Foster, K.
(2017). Nurses’ resilience and the emotional labour of
nursing work: An integrative review of empirical literature.
International Journal of Nursing Studies,70,71–88.
Deng, J., Guo, Y., Shi, H. et al. (2020). Effect of
discrimination on presenteeism among aging workers in
the United States: Moderated mediation effect of positive
and negative affect. International Journal of
Environmental Research and Public Health,17, 1425.
Digby, R., Winton-Brown, T., Finlayson, F., Dobson, H. &
Bucknall, T. (2020). Hospital staff well-being during the
first wave of COVID-19: Staff perspectives. International
Journal of Mental Health Nursing.
©2021 John Wiley & Sons Australia, Ltd
14 X. ZHANG ET AL.
Du, J., Dong, L., Wang, T. et al. (2020). Psychological symptoms
among frontline healthcare workers during COVID-19
outbreak in Wuhan. General Hospital Psychiatry,67 144–
145. https://doi.org/10.1016/j.genhosppsych.2020.03.011
Duan, L. & Zhu, G. (2020). Psychological interventions for
people affected by the COVID-19 epidemic. Lancet
Psychiatry,7, 300–302.
Efron, B. & Tibshirani, R. (1985). The bootstrap method for
assessing statistical accuracy. Behaviormetrika,12, 55.
Folkman, S. & Moskowitz, J. T. (2004). Coping: Pitfalls and
promise. Annual Review of Psychology,55, 745–774.
Frankel, L., Fisher, J. O., Power, T. G., Chen, T. A., Cross, M.
B. & Hughes, S. O. (2015). Confirmatory factor analysis of
the Feeding Emotions Scale. A measure of parent emotions
in the context of feeding. Appetite,91,107–113.
Goh, Y. S., Ow Yong, Q. Y. J., Chen, T. H., Ho, S. H. C.,
Chee, Y. I. C. & Chee, T. T. (2020). The impact of
COVID-19 on nurses working in a University Health
System in Singapore: A qualitative descriptive study.
International Journal of Mental Health Nursing. https://
doi.org/10.1111/inm.12826
Greenberg, N., Docherty, M., Gnanapragasam, S. & Wessely,
S. (2020). Managing mental health challenges faced by
healthcare workers during covid-19 pandemic. BMJ,368,
m1211.
Gunnell, D., Appleby, L., Arensman, E. et al. (2020). Suicide
risk and prevention during the COVID-19 pandemic.
Lancet Psychiatry,7, 468–471.
Guo, Y. F., Luo, Y. H., Lam, L., Cross, W., Plummer, V. &
Zhang, J. P. (2018). Burnout and its association with
resilience in nurses: A cross-sectional study. Journal of
Clinical Nursing,27, 441–449.
Hines, S. E., Brown, C., Oliver, M. et al. (2019). User
acceptance of reusable respirators in health care.
American Journal of Infection Control,47, 648–655.
Hines, S. E., Oliver, M. S., Gucer, P. & McDiarmid, M. A.
(2020). Self-reported impact of respirator use on health
care worker ability to perform patient care. American
Journal of Infection Control,48, 1556–1558.
Holmes, E. A., Ghaderi, A., Harmer, C. J. et al. (2018). The
Lancet Psychiatry Commission on psychological
treatments research in tomorrow’s science. Lancet
Psychiatry,5, 237–286.
Holmes, E. A., O’Connor, R. C., Perry, V. H. et al. (2020).
Multidisciplinary research priorities for the COVID-19
pandemic: A call for action for mental health science.
Lancet Psychiatry,7, 547–560.
Hou, T., Zhang, T., Cai, W. et al. (2020). Social support and
mental health among health care workers during
Coronavirus Disease 2019 outbreak: A moderated
mediation model. PLoS One,15, e0233831.
Kim, M. & Windsor, C. (2015). Resilience and work-life
balance in first-line nurse manager. Asian Nurs Res
(Korean Soc Nurs Sci),9,21–27.
Kumar, S. (2007). Burnout in psychiatrists. World Psychiatry
Official Journal of the World Psychiatric Association,6,
186–189.
Lai, J., Ma, S., Wang, Y. et al. (2020). Factors associated with
mental health outcomes among health care workers
exposed to coronavirus disease 2019. JAMA Netw Open,3,
e203976.
Li, Z., Ge, J., Yang, M. et al. (2020). Vicarious traumatization
in the general public, members, and non-members of
medical teams aiding in COVID-19 control. Brain,
Behavior, and Immunity,88, 916–919.
Li, X., Guan, L., Hui, C., Bo, Z. & Sophie, C. D. (2014).
Core self-evaluation and burnout among nurses: The
mediating role of coping styles. PLoS One,9, e115799.
Li, J., Li, J. J., Xie, X. et al. (2020a). Game consumption and
the 2019 novel coronavirus. The Lancet Infectious
Diseases,20, 275–276.
Li, C. P. & Shi, K. (2003). The influence of distributive
justice and procedural justice on job burnout. Acta
Psychologica Sinica,35, 677–684.
Litz,B.T.,Stein,N.,Delaney,E.et al. (2009). Moral injury and
moral repair in war veterans: A preliminary model and
intervention strategy. Clinical Psychology Review,29, 695–706.
Matthews, T., Danese, A., Caspi, A. et al. (2019). Lonely young
adults in modern Britain: Findings from an epidemiological
cohort study. Psychological Medicine,49,268–277.
Maunder, R. G., Lancee, W. J., Balderson, K. E. et al.
(2006). Long-term psychological and occupational effects
of providing hospital healthcare during SARS outbreak.
Emerging Infectious Diseases,12, 1924–1932.
Merlo, L. J., Curran, J. S. & Watson, R. (2017). Gender
differences in substance use and psychiatric distress
among medical students: A comprehensive statewide
evaluation. Substance Abuse,38, 401–406.
Mokhtari, R., Moayedi, S. & Golitaleb, M. (2020). COVID-19
pandemic and health anxiety among nurses of intensive
care units. International Journal of Mental Health Nursing
29, 1275–1277.
Murat, M., Kose, S. & Savaser, S. (2020). Determination of
stress, depression and burnout levels of front-line nurses
during the COVID-19 pandemic. International Journal of
Mental Health Nursing. https://doi.org/10.1111/inm.12818
Nickell, L. A., Crighton, E. J., Tracy, C. S. et al. (2004).
Psychosocial effects of SARS on hospital staff: Survey of a
large tertiary care institution. CMAJ,170, 793–798.
Pappa, S., Ntella, V., Giannakas, T., Giannakoulis, V. G.,
Papoutsi, E. & Katsaounou, P. (2020). Prevalence of
depression, anxiety, and insomnia among healthcare workers
during the COVID-19 pandemic: A systematic review and
meta-analysis. Brain, Behavior, and Immunity 88,901–907.
P
erez-Fuentes, M. C., Molero Jurado, M. M., Martos Martınez,
A. & G
azquez Linares, J. J. (2020). Threat of COVID-19 and
emotional state during quarantine: Positive and negative
affect as mediators in a cross-sectional study of the Spanish
population. PLoS One,15, e0235305.
Portero de la Cruz, S., Cruz, S., Cebrino, J., Herruzo, J. &
Vaquero-Abellan, M. (2020). A multicenter study into
burnout, perceived stress, job satisfaction, coping strategies,
and general health among emergency department nursing
staff. Journal of Clinical Medicine,9,1007.
©2021 John Wiley & Sons Australia, Ltd
ASSOCIATION BETWEEN RESILIENCE AND BURNOUT: POSITIVE AND NEGATIVE AFFECT AS MEDIATORS 15
Preacher, K. & Hayes, A. (2008). Asymptotic and resampling
strategies for assessing and comparing indirect effects in
multiple mediator models. Behavior Research Methods,
40, 879–891.
Ramirez-Granizo, I. A., Sanchez-Zafra, M., Zurita-Ortega, F.,
Puertas-Molero, P., Gonzalez-Valero, G. & Ubago-Jimenez,
J. L. (2020). Multidimensional self-concept depending on
levels of resilience and the motivational climate directed
towards sport in schoolchildren. International Journal of
Environmental Research and Public Health,17,534.
Ravyts, S. G., Dzierzewski, J. M., Raldiris, T. & Perez, E.
(2019). Sleep and pain interference in individuals with
chronic pain in mid- to late-life: The influence of negative
and positive affect. Journal of Sleep Research,28, e12807.
Rees, C. S., Breen, L. J., Cusack, L. & Hegney, D. (2015).
Understanding individual resilience in the workplace: The
international collaboration of workforce resilience model.
Frontiers in Psychology,6, 73.
Rivas, V., Hernandez-Huerta, D., Silva, C. P. D. & Gomez-
Arnau, J. (2020). Reactive psychosis in a health care
worker during the COVID-19 pandemic. The Primary
Care Companion for CNS Disorders,22, 20l02692.
Salkovskis, P. M., Clark, D. M., Hackmann, A., Wells, A. &
Gelder, M. G. (1999). An experimental investigation of the
role of safety-seeking behaviours in the maintenance of
panic disorder with agoraphobia. Behavior Research and
Therapy,37, 559–574.
Sarboozi Hoseinabadi, T., Kakhki, S., Teimori, G. & Nayyeri,
S. (2020). Burnout and its influencing factors between
frontline nurses and nurses from other wards during the
outbreak of Coronavirus Disease -COVID-19- in Iran.
Investigacio
´n y Educacio
´n en Enfermerı
´a,38.
Sciences, T. A. O. M. (2020). Survey results: Understanding
people’s concerns about the mental health impacts of the
COVID-19 pandemic [Online]. [Accessed July 18 2020].
Available from URL: [https://acmedsci.ac.uk/file-download/
99436893]
Sehmi, R., Maughan, B., Matthews, T. & Arseneault, L.
(2019). No man is an island: Social resources, stress and
mental health at mid-life. British Journal of Psychiatry
217, 638–644.
Shanafelt, T., Ripp, J. & Trockel, M. (2020). Understanding
and addressing sources of anxiety among health care
professionals during the COVID-19 pandemic. JAMA,
323, 2133–2134.
Shechter, A., Diaz, F., Moise, N. et al. (2020). Psychological
distress, coping behaviors, and preferences for support
among New York healthcare workers during the COVID-
19 pandemic. General Hospital Psychiatry,66,1–8.
Smereka, J. & Szarpak, L. (2020). The use of personal
protective equipment in the COVID-19 pandemic era.
American Journal of Emergency Medicine,38, 1529–1530.
Solomonov, N., Bress, J. N., Sirey, J. A. et al. (2019).
Engagement in socially and interpersonally rewarding
activities as a predictor of outcome in "engage" behavioral
activation therapy for late-life depression. American
Journal of Geriatric Psychiatry,27, 571–578.
Stewart, M. T., Reed, S., Reese, J., Galligan, M. M. &
Mahan, J. D. (2019). Conceptual models for
understanding physician burnout, professional fulfillment,
and well-being. Current Problems in Pediatric and
Adolescent Health Care,49, 100658.
SuHong,M.S.&Xiaoming,X.U.(2020).Immediate
psychological impact on nurses working at 42 government-
designated hospital during COVID-19 outbreak in China: A
cross-sectional study. Nursing Outlook,69(1), 6–12. https://
www.sciencedirect.com/science/article/pii/S0029655420306102
Suttle, M. L., Chase, M. A., Sasser, W. C. 3rd et al.(2020).
Burnout in pediatric critical care medicine fellows.
Critical Care Medicine,48, 872–880.
Usher, K., Durkin, J. & Bhullar, N. (2020). The COVID-19
pandemic and mental health impacts. International
Journal of Mental Health Nursing,29, 315–318.
Vahia, I. V., Blazer, D. G., Smith, G. S. et al. (2020).
COVID-19, mental health and aging: A need for new
knowledge to bridge science and service. American
Journal of Geriatric Psychiatry,28, 695–697.
Vinkers, C. H., van Amelsvoort, T., Bisson, J. I. et al. (2020).
Stress resilience during the coronavirus pandemic.
European Neuropsychopharmacology,35,12–16.
Watson, D., Clark, L. & Tellegen, A. (1988). Watson D,
Clark LA, Tellegen A. Development and validation of
brief measures of positive and negative affect - the panas
scales. J Pers Soc Psychol 54: 1063–1070. Journal of
Personality and Social Psychology,54, 1063–1070.
Williamson, V., Stevelink, S. A. M. & Greenberg, N. (2018).
Occupational moral injury and mental health: Systematic
review and meta-analysis. British Journal of Psychiatry,
212, 339–346.
Wind, T. R., Rijkeboer, M., Andersson, G. & Riper, H.
(2020). The COVID-19 pandemic: The ’black swan’ for
mental health care and a turning point for e-health.
Internet Interventions,20, 100317.
Wu, P., Fang, Y., Guan, Z. et al. (2009). The psychological
impact of the SARS epidemic on hospital employees in
China: Exposure, risk perception, and altruistic acceptance
of risk. Canadian Journal of Psychiatry,54, 302–311.
Wu, Y., Wang, J., Luo, C. et al. (2020). A comparison of
burnout frequency among oncology physicians and nurses
working on the frontline and usual wards during the
COVID-19 epidemic in Wuhan, China. Journal of Pain
and Symptom Management,60, e60–e65.
Yu, N. X. & Zhang, J. (2007). Factor analysis and
psychometric evaluation of the connor-davidson resilience
scale (CD-RISC) with Chinese people. Social Behavior
and Personality: an International Journal,35,19–30.
Zhao, Y., Yu, F., Wu, Y., Zeng, G. & Peng, K. (2019).
Positive education interventions prevent depression in
Chinese adolescents. Frontiers in Psychology,10, 1344.
©2021 John Wiley & Sons Australia, Ltd
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