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Received: 9 June 2022
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Revised: 19 December 2022
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Accepted: 22 January 2023
DOI: 10.1002/smi.3230
RESEARCH ARTICLE
Trajectories of burnout and psychological well‐being among
psychotherapists during the Covid‐19 pandemic: Results of a
1‐year prospective study
Angelika Van Hoy
|Marcin Rzeszutek
Faculty of Psychology, University of Warsaw,
Warsaw, Poland
Correspondence
Angelika Van Hoy, Faculty of Psychology,
University of Warsaw, Stawki 5/7, 00‐183
Warsaw, Poland.
Email: angelikahoun@psych.uw.edu.pl
Funding information
Ministry of Science and Higher Education;
New Ideas of POB V project implemented
within the scope of the ‘Excellence Initiative ‐
Research University’ Program, Grant/Award
Number: PSP: 501‐D125‐20‐5004310
Abstract
The main goal of this study was to investigate the trajectories of the changes in
burnout and subjective well‐being (SWB) among psychotherapists in relation to social
support, self‐efficacy, sociodemographic, and work‐related factors, with additional
control for the subjectively experienced Covid‐19 related distress. This study was
carried out over a 1‐year period during the critical time of the Covid‐19 pandemic.
We reached 226 Polish psychotherapists, of which 207 psychotherapists (91.6%)
participated in all three measurements. The participants completed the following
measurements: the Maslach Burnout Inventory‐Human Service Survey, the Satis-
faction with Life Scale, the General Self‐Efficacy Scale, the Multidimensional Scale of
Perceived Social Support, and a questionnaire regarding sociodemographic, work‐
related factors and Covid‐19 related distress. Several trajectories were observed
for both burnout and SWB, which were differently associated with the level of social
support, self‐efficacy, and sociodemographic and work‐related factors. We did not
find a significant relationship between Covid‐19 related distress and the inclusion of
either burnout or SWB trajectories. This study calls for greater interest in the psy-
chological health of psychotherapists. More specifically, our findings may entail some
implications for the practice of psychotherapy by creating customized intervention
programs to reduce burnout and enhance well‐being in this specific occupation.
KEYWORDS
burnout, Covid‐19, perceived social support, psychotherapist, self‐efficacy, subjective well‐
being
1
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INTRODUCTION
1.1
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Psychological health of psychotherapists
The psychological health of psychotherapists is still a relatively
understudied research area in clinical psychology and
psychotherapy, which have traditionally focussed much more on the
clients of psychotherapy rather than on the psychotherapists (Lee
et al., 2020; Mullenbach & Skovholt, 2016; Simionato &
Simpson, 2018). However, working as a psychotherapist has been
linked to multiple stressors, including constant demands for
empathy, high degrees of emotional strain, and compassion fatigue,
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, pro-
vided the original work is properly cited.
© 2023 The Authors. Stress and Health published by John Wiley & Sons Ltd.
Stress and Health. 2023;1–17. wileyonlinelibrary.com/journal/smi
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1
which all pose a risk of psychological distress, impairment, or even
burnout in this occupation (see e.g. Barnett, 2008; El‐Ghoroury
et al., 2012; Estacio, 2019; Farber, 1990; Farber & Heifetz, 1982;
Luther et al., 2017; Skovholt & Trotter‐Mathison, 2016). Several
authors also underlined the role of contextual and environmental
factors (e.g., working hours, caseload, and negative clientele), which
are responsible for the risk of burnout among psychotherapists (e.g.
Raquepaw & Miller, 1989; Rupert & Kent, 2007; Steel et al., 2015).
The literature suggests that when experiencing symptoms of
burnout, psychotherapists have problems maintaining therapeutic
relationships, thus endangering their clients (Berjot et al., 2017;
Rupert & Morgan, 2005; Rzeszutek & Schier, 2014). Moreover,
burnout symptoms are related to the risk of co‐occurring mental
disorders, substance use, and an increase in job turnover intentions
(Garcia et al., 2014; Rosenberg & Pace, 2006). In contrast to
burnout, studies on psychological well‐being and professional
quality of life among psychotherapists are even less prevalent than
burnout research (e.g., Laverdière et al., 2018,2019; Skovholt &
Trotter‐Mathison, 2016). However, there is evidence that psycho-
therapists provide better care to their clients when they care for
themselves (Mullenbach & Skovholt, 2016). Additionally, clients
choose to work with psychotherapists who are perceived as psy-
chologically healthy, satisfied with their own lives, and capable of
maintaining a personal balance between work and their private life
(Lambert & Barley, 2001; Norcross & VandenBos, 2018; Wogan &
Norcross, 1985). Some researchers have noticed that the low
quality of life of a psychotherapist may greatly hinder their ability
to establish a therapeutic relationship (Enochs & Etzbach, 2004;
Holmqvist & Jeanneau, 2006; Skovholt & Trotter‐Mathison, 2016).
The aforementioned issues support the need for further studies
regarding psychotherapists' mental health. Both negative (burnout)
and positive perspectives (well‐being) are crucial components that
are related to the overall processes and outcomes of psychother-
apy. However, the causal interferences on the factors related to the
psychological health of psychotherapists are still lacking (Lee
et al., 2020; Simionato & Simpson, 2018). In our 1‐year prospective
study, we examined the trajectories of the changes in burnout and
subjective well‐being (SWB) of psychotherapists.
1.2
|
A person‐centred approach to burnout and
well‐being research
Previous longitudinal studies showed that burnout is rather stable
over time (e.g., Hakanen et al., 2008; Schaufeli & Enzmann, 1998;
Taris et al., 2005). This finding is intriguing because the first oper-
ationalizations of burnout highlighted that this syndrome should
change over time (Freudenberger, 1974; Maslach, 1982). Some au-
thors have claimed that the reason for this apparent stability of
burnout is the methods used for assessment, that is, the dominance
of a variable‐centred approach (Huttell et al., 2013). Although the
variable‐centred approach follows a standard correlational pattern
with the sole concern being isolated burnout dimensions, the person‐
centred design focuses on the multidimensional experiences of
burnout, that is, the various configurations (profiles) of burnout that
may deviate from the observed correlational shape (Leiter & Mas-
lach, 2016; Mäkikangas & Kinnunen, 2016). In other words, by
applying a person‐centred analysis of burnout, we can go beyond the
traditional all‐or‐nothing attitude (i.e., individuals with burnout and
those without), represented by the cut‐off scores in burnout in-
ventories (Bianchi et al., 2015). Until now, to our best knowledge,
only Berjot et al. (2017) examined profiles of burnout among clinical
psychologists and argued that this approach may better identify
subgroups of therapists who are at higher risk of burnout given their
specific work‐related conditions. Similar findings pointing to the high
level of temporal stability have been found in the area of SWB,
especially regarding its cognitive aspects, such as life satisfaction
(Diener et al., 2016; Luhmann et al., 2012). But as in burnout
research, the dominance of the variable‐centred approach precludes
more complex insights into the unique trajectory of SWB over time,
which have been identified in more recent studies (e.g., Li et al., 2022;
Moreno‐Agostino et al., 2021).
Nevertheless, an increasing number of authors argued that not
only prospective, person‐centred studies are needed in the field of
occupational burnout and SWB, but also studies more embedded
in theoretical models, especially those that underscore the role
dynamic processes in these variables (Kelloway & Francis, 2013;
Matthews & Ritter, 2019; Taylor et al., 2017). In our study, we
focussed on three theories, which served us in framing our hy-
potheses. First, we followed the adaptation theory, which explicitly
underlines that stressor‐strain association unfolds over time
(Diener et al., 2006; Ritter et al., 2016). However, in contrast to
resource‐based stress theories (e.g. conservation of resources
theory; Hobfoll, 2011) showing the link between prolonged expo-
sure to stressors and long‐term work strain, adaptation theorists
observed that people can uniquely adapt to the work stressors
with an eventual return to a baseline level of work‐related well‐
being over time (Ritter et al., 2016). This idea can also be
strengthened in light of the well‐examined honeymoon‐hangover
effect concerning fluctuations in employees' job satisfaction over
time, especially those with various levels of work experience
(Boswell et al., 2009; Chan & Schmitt, 2000). Finally, little is
known about the psychosocial resources that may be responsible
for individual patterns of change in both, burnout and SWB over
time (Leiter & Maslach, 2016). In our study, following the job
demands‐resources model highlighting the potential role of imbal-
ance between job demands and the potential resources at work in
the burnout process and/or poor well‐being at work (Demerouti
et al., 2001), we concentrated on social support (e.g.,
Halbesleben, 2006; Schwarzer & Knoll, 2007) and self‐efficacy (e.g.
Shoji et al., 2016; Łuszczyńska et al., 2005). We also focussed on
these psychosocial factors in the context of burnout and SWB
because until now they have predominantly been studied using a
variable‐oriented framework only (Shoji et al., 2016).
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van HOY and RZESZUTEK
1.3
|
Burnout and well‐being of psychotherapists
during the Covid‐19 pandemic
Lastly, our study was conducted during a critical period, that is, the
Covid‐19 pandemic, which posed a high level of psychological
distress in various occupations (e.g., Olagunju et al., 2021; Sasaki
et al., 2020). Psychotherapists have had to face numerous new
challenges and obstacles when it comes to their therapeutic practice,
which all were related to chronic work‐related stress and the
potential risk of burnout (Brillon et al., 2021; Rokach &
Boulazreg, 2020). One of the most prominent challenges encom-
passed switching partly or even entirely to providing their services
online, which emphasized the risk of anonymity and intimacy of
therapeutic relationships. It was also found that especially at the
beginning of this pandemic, many psychotherapists overextended
themselves with work due to their positive intentions of helping as
many clients as possible (Rokach & Boulazreg, 2020). At the same
time, they were much less effective in taking care of themselves and
their professional and private quality of life (ibidem). However, in
light of the above‐mentioned adaptation theory, we may assume that
some psychotherapists could also acclimate to the pandemic‐related
stressors in their occupation, even if the actual threat of the Covid‐
19 virus was unchanged. This latter trend has been found by au-
thors investigating well‐being fluctuations in various occupations
during the Covid‐19 pandemic (Hu & Subramony, 2022; Michel
et al., 2021).
1.4
|
Current study
Until now, studies on burnout and well‐being among psycho-
therapists were either conducted in a cross‐sectional framework
or (if longitudinal) followed only an analysis of changes in the
mean scores of the study variables (Lee et al., 2020; Simionato &
Simpson, 2018). In other words, they presented general trends for
the whole study sample and neglected the potential trajectories
of the study variables in particular subgroups of the participants.
Thus, the novelty of the current study is that we used latent
class growth analysis in this specific sample (LCGA; Warde-
naar, 2020). Moreover, to our best knowledge, no studies have
been conducted on how the Covid‐19 pandemic was related to
burnout and well‐being fluctuations among psychotherapists in
the prospective, person‐centred approach. Therefore, little is
known about the heterogeneity of changes in these variables
within this sample of participants and the psychosocial factors
responsible for these changes. Thus, our study is mainly explor-
ative. However, based on other studies conducted during the
Covid‐19 pandemic, yet dealing with different study samples (e.g.
Hu & Subramony, 2022; Michel et al., 2021), we investigated the
trajectories of changes in burnout and SWB in relation to social
support, self‐efficacy, sociodemographic, work‐related factors,
while controlling for the COVID‐19‐related distress. We formu-
lated three hypotheses:
Hypothesis 1 There is heterogeneity of change in burnout and SWB
levels (as operationalized by the level of life satisfaction)
among psychotherapists; that is, different classes of burnout
trajectories and SWB can be observed during the study period.
Hypothesis 2 The level of social support and self‐efficacy (from the
one measurement) would be positively related to inclusion to
low burnout trajectories and high SWB trajectories, respec-
tively (for the whole study period).
Hypothesis 3 The burnout and SWB trajectories are related to
sociodemographic and work‐related characteristics among our
participants.
2
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METHODS
2.1
|
Participants and procedure
We designed a prospective study using standardized psychometric
questionnaires in an online format; the participants were psycho-
therapists in Poland. The online questionnaires were distributed
among professional psychotherapeutic associations of various psy-
chotherapy approaches in Poland, and the associations sent this
information to their members. The eligibility criteria encompassed
certification (or being in the process of certification) in a particular
psychotherapeutic approach and psychotherapeutic practice for at
least 1 year. The study was carried out in three phases during the
critical time of the Covid‐19 pandemic. The participants were first
contacted in June 2020. The second phase was carried out
6 months later, that is, in January 2021, when the participants who
provided their contact information in the first round were sent the
online link again. Six months later, in June 2021, the participants
were sent the online link for the final time, concluding the third
phase of the study. Participation was anonymous and voluntary, and
the participants received no incentive to participate in the survey.
The study protocol was accepted by the Polish Institutional Ethics
Committee.
We managed to reach 226 psychotherapists, out of which 207
psychotherapists (91.6%) participated in all three measurements. The
dropout rate was 8.4%. Only 19 psychotherapists participated in the
first two measurements and did not participate in the third mea-
surement. Table 1presents the demographic characteristics of the
sample. Testing the likelihood ratio, there was no relationship be-
tween dropout and participants' gender, λ(1) =0.14, p>0.05, marital
status, λ(3) =7.28, p>0.05, profession, λ(2) =2.79, p>0.05, or
number of working hours weekly, λ(2) =0.29, p>0.05. A student's t‐
test for independent samples revealed no statistical differences be-
tween the participants taking part in all three measurements and
those participating only in the first two regarding age, t(224) =1.00,
p>0.05, and work experience, t(224) = −0.83, p>0.05.
Finally, it is worth mentioning a few words about the organi-
zation of psychotherapy in Poland (Suszek et al., 2017). Namely, to
van HOY and RZESZUTEK
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practice the psychotherapy profession in Poland, potential candi-
dates must possess a master's degree, but no specific field of study
is required. Further, they must undergo 4 years of training at a
psychotherapy school in the chosen approach that the individual
wishes to practice. During this time, trainees are required to
conduct psychotherapy under constant supervision by a licenced
psychotherapist. After graduation, individuals can obtain a ‘Psy-
chotherapist's Certificate’ by passing an exam which is conducted
by the society of psychotherapists in Poland. Most psychotherapists
tend to work in private practices. Lastly, it is important to note that
in Poland, specific legal regulations for this profession are still
nonexistent, although in the near future such regulations will
appear.
2.2
|
Measures
2.2.1
|
Maslach Burnout Inventory‐Human Service
Survey
Burnout was assessed using the Maslach Burnout Inventory‐Human
Service Survey (MBI‐HS; Maslach et al., 1996). The Polish adapta-
tion of the MBI‐HS was bought from Mind Garden, the official
distributor of the MBI‐HS. The MBI‐HS contains 22 items and eval-
uates burnout and its three components: (1) emotional exhaustion
(EE), nine items; (2) personal accomplishment (PA), eight items; and
(3) depersonalization (DP), five items. For each item, the participants
indicated symptom frequency on a Likert‐type scale ranging from
TABLE 1Demographic characteristics of the study sample.
Only two measurements All three measurements Total sample
n%n%n%
Gender
Women 17 89.5 179 86.5 196 86.7
Men 2 10.5 28 13.5 30 13.3
Age 27–60 M=42.53;
SD =9.59
26–68 M=40.64;
SD =7.75
26–68 M=40.79;
SD =7.91
Relationship status
In stable relationship 10 52.6 157 75.8 167 73.9
Divorced/separated 3 15.8 21 10.1 24 10.6
Widow/widower 2 10.5 2 1.0 4 1.8
Single 4 21.1 27 13.0 31 13.7
Profession
Psychologists 17 89.5 155 74.9 172 76.1
Psychiatrists 0 0.0 5 2.4 5 2.2
Other 2 10.5 47 22.7 49 21.7
Work experience (in years) 1–20 M=7.24;
SD =5.55
1–35 M=8.57;
SD =6.77
1–35 M=8.46;
SD =6.68
Working hours weekly
1–10 3 15.8 41 19.8 44 19.5
11–20 6 31.6 69 33.3 75 33.2
More than 30 10 52.6 97 46.9 107 47.3
The change in the work setting due to Covid‐19 pandemic
Only for the time of the lockdown 5 26.3 44 21.3 49 21.7
I am currently partially working online 8 42.1 111 53.6 119 52.7
Before, I worked partially online during
a pandemic and now too
2 10.5 34 16.4 36 15.9
Currently I only work online 4 21.1 31 15.0 35 15.5
Before and now I only worked online 0 0.0 2 1.0 2 0.9
I stopped working at all during the
lockdown
1 5.3 1 0.5 2 0.9
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van HOY and RZESZUTEK
0 (never) to 6 (every day). All of the summed responses comprise an
overall index, with higher values indicating higher burnout. We
decided to use the MBI‐HS in our study for the following reasons: it is
the most popular and widely used burnout inventory, and it is
focussed especially on helping professions (Leiter & Maslach, 2016;
Maslach et al., 2001).
2.2.2
|
Satisfaction with Life Scale
To assess life satisfaction, we used the Polish adaptation of the
Satisfaction with Life Scale (SWLS; Diener et al., 1985). SWLS is a
common tool to measure the cognitive aspects of SWB and comprises
five items, each assessed on a 7‐point scale ranging from 1 (strongly
disagree) to 7 (strongly agree). A higher level of life satisfaction is
indicated by a higher total score.
2.2.3
|
General Self‐Efficacy Scale
The level of self‐efficacy was examined using the Polish adaptation of
the General Self‐Efficacy Scale (GSES; Schwarzer & Jerusalem, 1995).
The GSES is a 10‐item tool that measures the general (global)
construct of self‐efficacy. The respondents rate various statements
on a 4‐point Likert scale and the global index of self‐efficacy is ob-
tained as the sum of all items.
2.2.4
|
Multidimensional Scale of Perceived Social
Support
To evaluate social support, we chose the Multidimensional Scale of
Perceived Social Support (MSPSS; Zimmet et al., 1990). The MSPSS
questionnaire contains 12 items that measure perceived social sup-
port from three sources—family, friends, and a significant other—as
well as the total support level. In our study, we followed the total
support index. The participants rated several sentences on a 7‐point
Likert‐type scale (from very strongly disagree to very strongly agree).
MSPSS is also an internationally known tool to assess the various
aspects of perceived social support.
Finally, the participants answered a survey on sociodemographic
and work‐related characteristics (see Table 1). Stress related to the
Covid‐19 pandemic was measured by two questions located at the end
of the sociodemographic questionnaire. The participants were asked
to rate on a 1–5 scale how stressful they have found the Covid‐19
situation as a psychotherapist. We also asked if participants moved
their practice to the online setting due to the COVID‐19 pandemic.
2.3
|
Data analysis
In the preliminary analysis, descriptive statistics and Pearson corre-
lation coefficients were calculated. Next, LCGA (Wardenaar, 2020)
was used to extract subgroups of respondents with different trajec-
tories of changes regarding EE, depersonalization, PA, and satisfaction
with life. Burnout dimensions, that is, EE, depersonalization and pro-
fessional accomplishment, and satisfaction with life were measured in
three consecutive measurements. Covid‐19 distress, general self‐
efficacy, and social support were measured once during the first
measurement. We used LCGA because we wanted to extract sub-
groups of participants with different slopes and intercepts regarding a
change in three consecutive measurements (see Jung & Wick-
rama, 2008; Ram & Grimm, 2009). Our objective was to extract groups
with different starting points and pace of change. We did not include
random effects in our analysis. We wanted to observe heterogeneity in
trends instead of basing our conclusions on individual trajectories. In
each case, five models were assessed: a model with one general tra-
jectory for the whole sample, a model with two different trajectories, a
model with three different trajectories, a model with four different
trajectories, and a model with five different trajectories. The model
with the lowest value of the Bayesian information criterion (BIC) fit
index was chosen on the condition that the extracted profiles were
detected in at least 20% of the cases in the sample. The extracted
classes representing different trajectories were then compared in
terms of general self‐efficacy and social support using ANOVA and
Student's t‐test for independent samples. LCGA was performed with
the use of the lcmm package (Proust‐Lima et al., 2017) working in the R
Statistics 4.1.2 software environment. All other calculations were
performed with the use of IBM SPSS 27.0 software.
3
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RESULTS
Table 2presents the descriptive statistics for the analyzed variables.
The table presents the mean values, standard deviations, minimum
and maximum values, and the values of skewness and kurtosis. The
values of skewness and kurtosis did not exceed the range from −1.0
to 1.0. Therefore, parametric statistical tests were used in subse-
quent analyses.
Table 3presents the values of Pearson's correlation coefficients
for the analyzed variables. Statistically significant correlations are
marked with asterisks.
The main analysis was based on LCGA. Table 4presents the
values of the BIC index values and profile distribution for all analyzed
models. The models with the best fit, that is, the lowest value of the
BIC fit index and extracted profiles detected in at least 20% of cases
of the sample, are marked with bold font. The above‐mentioned re-
sults were in line with our first research hypothesis.
Three different trajectories were extracted from analyses of EE.
Two different trajectories were extracted in analyses of deperson-
alization, PA, and satisfaction with life.
Figure 1depicts the extracted trajectories for EE. For analyses
regarding EE, a trajectory of a stable high level (relative to other
classes) of EE (profile 1), a trajectory of a stable medium level of EE
(profile 2), and a trajectory of a stable low level of EE (profile 3) were
detected.
van HOY and RZESZUTEK
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Figure 2depicts the extracted trajectories for depersonalization.
In the analysis regarding depersonalization, a trajectory of deper-
sonalization increasing in the third measurement (profile 1) and
depersonalization decreasing in the third measurement (profile 2)
were detected.
Figure 3depicts the extracted trajectories for professional
accomplishment. In analyses of professional accomplishment, a tra-
jectory of a stable high level (relative to the other class) of profes-
sional accomplishment (profile 2) and a stable low level of
professional accomplishment (profile 1) were detected.
Figure 4depicts the extracted trajectories for satisfaction with
life. In analyses of satisfaction with life, a trajectory of a stable high
level (relative to the other class) of satisfaction with life (profile 2)
and of a low level of satisfaction with life increasing in the third
measurement (profile 1) were detected.
The extracted subgroups of respondents with different trajec-
tories were compared in terms of sociodemographic and work‐
related variables, as well as general self‐efficacy and social support.
Three groups with different trajectories of EE were compared using
ANOVA. Two groups with different trajectories of depersonalization
and PA were compared using a Student's t‐test for the independent
samples. Table 5presents the mean values of the analyzed variables
in the subgroups of those participants with detected trajectories of
change regarding levels of EE, depersonalization, PA, and satisfaction
with life, which is in accordance with our second research hypothesis.
According to the values of MANOVA, there were statistically
significant differences between the participants with different tra-
jectories of EE, F(12,483) =2.85, p<0.001, η
2
=0.07, the partici-
pants with different trajectories of depersonalization, F
(6,219) =2.96, p<0.01, η
2
=0.08, the participants with different
trajectories of PA, F(6,219) =7.77, p<0.001, η
2
=0.18, and between
the participants with different trajectories of satisfaction with life, F
(6,219) =10.99, p<0.001, η
2
=0.23.
The participants with different trajectories of EE differed in
terms of age, social support from significant others, social support
from family, and total level of social support. According to the Gabriel
posthoc test, the mean age of the participants with the lowest values
of EE (profile 3) was significantly higher than the mean age of par-
ticipants with the highest level of EE (profile 1), t=3.03, p<0.01, and
was higher than the mean age of participants with the medium level
of EE (profile 2), t=3.48, p<0.01. The level of social support was
significantly higher in the subgroup of participants with the lowest
level of EE (profile 3) than in the subgroup of participants with the
highest level of EE (profile 1). This applies to the level of social
support from significant others, t=2.67, p<0.05, from family,
t=2.94, p<0.05, and total level of social support, t=3.09, p<0.01.
The participants with a lower level of depersonalization (profile
1) were also characterized by a higher level of social support from
all sources than those with a higher level of depersonalization
(profile 2). The participants with a lower level of PA (profile 1) were
also characterized by a lower level of social support from all
sources and by a lower level of general self‐efficacy than those with
a higher level of PA (profile 2). The participants with lower levels of
satisfaction with life (profile 1) were also characterized by lower
levels of social support from all sources and by lower levels of
general self‐efficacy than those with higher levels of satisfaction
with life (profile 2).
Table 6presents the distributions of the demographic charac-
teristics in the subgroups of participants with different trajectories
regarding EE, depersonalization, PA, and satisfaction with life. The
table shows the values of the statistical test for associations between
profiles of changes and analyzed variables based on the likelihood
ratio. Here, we present only statistically significant sociodemographic
and work‐related correlates. Thus, the participants with the lowest
EE (profile 3) were in stable relationships. In addition, the participants
with the lowest level of EE (profile 3) worked less a week than
those with the highest level of EE (profile 1). The number of partic-
ipants with professional backgrounds other than being a psychologist
or a psychiatrist was higher in the group with lower depersonaliza-
tion (profile 1). The number of participants in a stable relationship
TABLE 2Descriptive statistics for the analyzed variables in
the study sample.
Variables MSD min Max S K α
Emotional exhaustion
Measurement I 16.12 7.96 1 41 0.50 −0.26 0.88
Measurement II 16.07 7.97 1 41 0.52 −0.22 0.88
Measurement III 16.55 8.97 0 46 0.65 0.14 0.91
Depersonalization
Measurement I 4.11 3.66 0 19 0.33 0.24 0.73
Measurement II 4.10 3.69 0 19 0.35 0.26 0.74
Measurement III 4.18 3.54 0 16 0.14 0.97 0.77
Professional accomplishment
Measurement I 29.71 5.99 2 40 −0.80 0.68 0.82
Measurement II 29.68 6.05 2 40 −0.81 0.63 0.83
Measurement III 29.79 6.56 0 40 −0.30 0.81 0.86
Satisfaction with life
Measurement I 24.84 4.72 11 35 −0.61 0.55 0.85
Measurement II 24.79 4.75 11 35 −0.63 0.49 0.85
Measurement III 25.27 4.60 9 35 −0.59 0.70 0.86
Covid‐19 distress 3.24 0.98 1 5 −0.09 −0.62 ‐
General self‐efficacy 30.99 3.90 13 40 −0.24 0.35 0.90
Social support
Significant other 23.71 5.20 4 28 −0.65 0.60 0.96
Family 20.69 5.26 4 28 −0.71 0.01 0.93
Friends 22.94 4.47 4 28 −0.23 0.94 0.96
Total 67.33 12.57 12 84 −0.33 0.36 0.94
Abbreviations: K, kurtosis; M, mean value; max, maximum value; min,
minimum value; S, skewness; SD, standard deviation; α, Cronbach's α
reliability coefficient.
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TABLE 3Correlation coefficients between the analyzed variables in the study sample.
Variables
1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17.
Emotional exhaustion
Measurement I ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐
Measurement II 0.99** ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐
Measurement III 0.68** 0.67** ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐
Depersonalization
Measurement I 0.45** 0.46** 0.39** ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐
Measurement II 0.46** 0.46** 0.40** 0.99** ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐
Measurement III 0.38** 0.40** 0.51** 0.57** 0.56** ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐
Professional accomplishment
Measurement I −0.29** −0.29** −0.16* −0.29** −0.29** −0.21** ‐‐‐ ‐‐‐‐‐‐‐‐
Measurement II −0.29** −0.29** −0.17* −0.29** −0.29** −0.21** 0.99** ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐
Measurement III −0.24** −0.22** −0.20** −0.19** −0.20** −0.28** 0.74** 0.74** ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐
Satisfaction with life
Measurement I −0.13 −0.14* −0.09 −0.14* −0.14* −0.15* 0.31** 0.32** 0.21** ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐
Measurement II −0.14* −0.14* −0.10 −0.14* −0.14* −0.16* 0.32** 0.32** 0.21** 0.99** ‐ ‐ ‐ ‐ ‐ ‐ ‐
Measurement III −0.12 −0.12 −0.17* −0.12 −0.13 −0.19** 0.33** 0.34** 0.34** 0.71** 0.71** ‐ ‐ ‐ ‐ ‐ ‐
Covid‐19 distress 0.17** 0.18** 0.08 0.06 0.05 0.10 −0.11 −0.11 −0.15* −0.10 −0.09 −0.09 ‐ ‐ ‐ ‐ ‐
General self‐efficacy −0.13 −0.14* −0.06 −0.20** −0.21** −0.13 0.42** 0.42** 0.26** 0.46** 0.46** 0.33** −0.04 ‐ ‐ ‐ ‐
Social support
Significant other −0.19** −0.20** −0.07 −0.26** −0.26** −0.20** 0.21** 0.23** 0.12 0.30** 0.31** 0.25** 0.08 0.32** ‐ ‐ ‐
Family −0.20** −0.21** −0.09 −0.27** −0.26** −0.23** 0.29** 0.30** 0.25** 0.43** 0.44** 0.37** −0.09 0.34** 0.57** ‐ ‐
Friends −0.16* −0.17* −0.16* −0.27** −0.28** −0.23** 0.28** 0.31** 0.20** 0.21** 0.22** 0.21** 0.09 0.32** 0.65** 0.48** ‐
Total −0.22** −0.23** −0.13 −0.31** −0.32** −0.27** 0.31** 0.33** 0.23** 0.37** 0.39** 0.34** 0.03 0.39** 0.88** 0.82** 0.82**
*p<0.05, **p<0.01.
van HOY and RZESZUTEK
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TABLE 4BIC index values and profile distribution for analyzed models in the study sample.
Variables No. of profiles BIC
Frequency distribution for trajectories
Profile 1% Profile 2% Profile 3% Profile 4% Profile 5%
Emotional exhaustion 1 4623.25 100
2 4341.80 63.9 36.1
3 4248.66 21.1 38.3 40.5
4 4233.55 21.6 36.1 8.8 33.5
5 4231.85 30.8 5.3 8.8 35.2 19.8
Depersonalization 1 3546.21 100
2 3299.01 76.2 23.8
3 3193.09 61.7 32.6 5.7
4 3159.11 4.4 48.9 36.1 10.6
5 3128.52 33.9 13.7 41.4 6.2 4.8
Personal accomplishment 1 4242.53 100
2 4017.70 25.6 74.4
3 3898.81 51.5 9.3 39.2
4 3823.14 28.2 4.0 19.8 48.0
5 3772.84 7.9 21.1 44.5 0.9 25.6
Satisfaction with life 1 3879.56 100
2 3632.54 32.2 67.8
3 3501.23 46.7 9.7 43.6
4 3464.86 9.7 38.8 4.8 46.7
5 3431.66 12.8 37.4 3.1 41.9 4.8
Note: The models with the best fit are marked with bold font.
Abbreviation: BIC, Bayesian information criterion.
FIGURE 1 Detected profiles of the
trajectories regarding changes in emotional
exhaustion among the study participants.
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van HOY and RZESZUTEK
was higher in the group with higher satisfaction with life (profile 2).
All these findings are consistent with our third research hypothesis.
4
|
DISCUSSION
The results were in accordance with our first hypothesis: for the case
of burnout and SWB level, we observed different classes of trajec-
tories among psychotherapists during the 1‐year period. Our findings
bring an important argument in favour of the proponents of a person‐
centred approach, both in burnout research (e.g., Leiter & Mas-
lach, 2016; Mäkikangas & Kinnunen, 2016) and in the SWB area
(Busseri & Sadava, 2011; Lazić et al., 2021). Regarding burnout
studies, it seems that this syndrome is not necessarily a stable syn-
drome, which has been found in many previous longitudinal studies
adopting a variable‐centred design (e.g., Brouwers & Tomic, 2000;
Schaufeli & Enzmann, 1998; Taris et al., 2005). Solely using this latter
approach may hinder us from capturing burnout changes over time,
preclude us from identifying the factors responsible for these
changes, and finally lead us to misleading conclusions (Dunford
et al., 2012; Huttell et al., 2013). Specifically, several researchers
have criticized both the traditional three‐dimensional model of
burnout as a sequence of isolated stages over time, as well as its
mostly independent assessment via cut‐off scores in burnout in-
ventories (Bianchi et al., 2015). Yet Schaufeli and Buunk (2004)
argued that the cut‐off scores in burnout inventories follow the
arbitrary statistical norms observed within convenience research
samples, so they cannot be treated as representative of the general
population. Furthermore, burnout is highly specific to the working
environment (e.g., culture, norms values), so a particular burnout
FIGURE 2 Detected profiles of the
trajectories regarding changes in
depersonalization among the study
participants.
FIGURE 3 Detected profiles of the
trajectories regarding changes in personal
accomplishments among the study participants.
van HOY and RZESZUTEK
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9
score is not universal for all professions (Bianchi et al., 2015; Tobi &
Kampen, 2013). In other words, focussing on multidimensional ex-
periences of burnout—that is, various configurations of burnout that
may deviate from the observed correlational patterns—may provide
better insights into why and for whom burnout syndrome may
change over time. This may greatly help in burnout prevention or
creating more customized burnout intervention programs, which
should move away from the traditional one‐size‐fits‐all attitude
(Leiter & Maslach, 2016). As far as our participants are concerned, it
would be advisable to publicise the problem of burnout among psy-
chotherapists more, as this topic is still relatively understudied not
only in academic research (Simionato & Simpson, 2018) but is also
often underemphasized in the process of clinical training in this
occupation (Lee et al., 2020; Norcross & VandenBos, 2018). In
particular, there is a need to include, in the standards for psycho-
therapy education, more courses on how psychotherapists can cope
with work stress and potential burnout symptoms in their everyday
professional and private lives (Norcross & VandenBos, 2018).
Furthermore, another advantage of implementing the person‐
centred approach, both on the theoretical and practical levels, can
be found in the research on SWB. However, this topic is still much
less examined when compared with burnout (e.g., Lazić et al., 2021;
Rzeszutek et al., 2019). Further investigation of the heterogeneity of
SWB and how it changes over time (e.g., Li et al., 2022; Moreno‐
Agostino et al., 2021) may help us to better understand the com-
plex structure of SWB. For example, it is still not entirely known why
its cognitive (life satisfaction) and affective components are interre-
lated but at the same time constitute separable constructs, here
when taking into account its temporal stability, predictors, and
different backgrounds (Luhmann et al., 2012; Lyubomirsky, 2011;
Schimmack et al., 2002). Regarding psychotherapists, more research
is necessary on self‐care behaviours, which have been found as the
most important factors promoting personal and professional quality
of life in this profession (Laverdière et al., 2018; Rupert &
Dorociak, 2019).
Our findings were also mostly in line with our second hypothesis
because we observed that both social support and self‐efficacy were
positively related to inclusion to, respectively, low burnout trajec-
tories and high SWB trajectories among the studied sample. More
specifically, higher social support was associated with inclusion in the
low EE trajectory, low depersonalization trajectory, high professional
accomplishment trajectory, and high life satisfaction trajectory. Self‐
efficacy level predicted inclusion to a high professional accomplish-
ment trajectory, as well as a high life satisfaction trajectory. On the
one hand, our results are in line with a vast number of studies
pointing to the mitigating effect of social support in stress adaptation,
both in the general population (Cohen & Wills, 1985; Schwarzer &
Knoll, 2007), in clinical samples (Wang et al., 2021), and the area of
occupational stress (Łuszczynska & Cieslak, 2005). On the other
hand, our data point again to the adaptive role of self‐efficacy in the
case of work‐related stress and occupational well‐being (Shoji
et al., 2016). Some authors have noted that self‐efficacy acts as a
personal resource against work‐related stress and strain (Hahn
et al., 2011), enhancing the process of employees' adaptation to
organizational changes and conflicts within it (Unsworth & Ma-
son, 2012). However, the vast majority of the above‐mentioned
studies, both in the social support and self‐efficacy areas, were
conducted either in a cross‐sectional framework or if using a longi-
tudinal design, they only followed the variable‐oriented pattern.
Therefore, our study provides insights into the heterogeneous
pattern of dynamic relationships of these variables with burnout and
SWB, which has never been investigated in this way among psy-
chotherapists (Lee et al., 2020; Simionato & Simpson, 2018). More
specifically, psychotherapists should be more aware of intrapsychic
(e.g. personality, cognitive functioning) and social factors, which are
relevant to their mental functioning (Rzeszutek & Schier, 2014).
FIGURE 4 Detected profiles of the
trajectories regarding changes in satisfaction
with life among the study participants.
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van HOY and RZESZUTEK
Finally, in support of our third research hypothesis, we observed
consistent sociodemographic and work‐related correlates of inclusion
to the trajectories of burnout dimensions and SWB. Namely, older age,
stable relationship, lower workload, and professional background
other than being a psychologist or psychiatrist were associated with,
respectively, low burnout trajectories and high SWB trajectories
throughout the entire study period. The role of younger age as a
burnout predictor among psychotherapists has been found in previous
studies (e.g., Ackerley et al., 1988; Berjot et al., 2017; van der Ploeg
et al., 1990). This trend is often discussed in light of the tendency of
many young psychotherapists to have too high and unrealistic expec-
tations about their roles in this occupation (Rupert & Kent, 2007). The
TABLE 5Mean values of the analyzed variables in the subgroups of participants with detected trajectories of change regarding levels of
emotional exhaustion, depersonalization, personal accomplishments, and satisfaction with life among the study participants.
Trajectory based on Variables
Profile 1 Profile 2 Profile 3
F/tdf pη
2
/dM SD MSD MSD
Emotional exhaustion Age 39.06 7.03 39.21 7.23 43.22 8.41 7.59 2223 0.001 0.06
Work experience 8.05 4.99 8.45 6.68 8.68 7.47 0.14 2223 0.873 0.01
General self‐efficacy 30.13 4.50 30.86 3.41 31.57 3.96 2.26 2223 0.107 0.02
Social support
Significant other 21.94 6.17 23.98 4.88 24.38 4.76 3.76 2223 0.025 0.03
Family 18.90 5.43 20.70 4.76 21.62 5.44 4.32 2223 0.014 0.04
Friends 22.00 5.28 22.71 3.98 23.65 4.38 2.35 2223 0.098 0.02
Total 62.83 15.06 67.39 10.00 69.65 12.84 4.77 2223 0.009 0.04
Depersonalization Age 41.16 7.75 39.63 8.35 ‐ ‐ 1.24 224 0.216 0.19
Work experience 8.48 6.62 8.37 6.93 ‐ ‐ 0.11 224 0.915 0.02
General self‐efficacy 31.26 3.68 30.15 4.47 ‐ ‐ 1.83 224 0.069 0.29
Social support
Significant other 24.26 4.74 21.94 6.17 ‐ ‐ 2.54 73.68 0.013 0.45
Family 21.25 5.18 18.89 5.15 ‐ ‐ 2.93 224 0.004 0.46
Friends 23.53 4.04 21.06 5.22 ‐ ‐ 3.65 224 0.001 0.57
Total 69.04 11.17 61.89 15.12 ‐ ‐ 3.75 224 0.001 0.59
Personal accomplishments Age 40.34 8.52 40.95 7.71 ‐ ‐ −0.50 224 0.617 0.08
Work experience 8.53 7.24 8.43 6.50 ‐ ‐ 0.09 224 0.926 0.01
General self‐efficacy 28.48 2.96 31.86 3.82 ‐ ‐ −6.92 127.28 0.001 0.93
Social support
Significant other 22.40 5.30 24.16 5.10 ‐ ‐ −2.25 224 0.026 0.34
Family 18.64 5.14 21.39 5.13 ‐ ‐ −3.52 224 0.001 0.54
Friends 21.07 4.58 23.58 4.25 ‐ ‐ −3.81 224 0.001 0.58
Total 62.10 12.98 69.14 11.95 ‐ ‐ −3.78 224 0.001 0.58
Satisfaction with life Age 41.95 7.36 40.25 8.12 ‐ ‐ 1.52 224 0.131 0.22
Work experience 8.31 6.87 8.53 6.61 ‐ ‐ −0.23 224 0.819 0.03
General self‐efficacy 28.95 3.81 31.97 3.57 ‐ ‐ −5.83 224 0.001 0.83
Social support
Significant other 21.73 5.97 24.65 4.51 ‐ ‐ −3.72 112.66 0.001 0.58
Family 17.85 5.31 22.04 4.68 ‐ ‐ −6.02 224 0.001 0.86
Friends 22.08 4.65 23.35 4.33 ‐ ‐ −2.00 224 0.046 0.28
Total 61.66 12.96 70.04 11.47 ‐ ‐ −4.92 224 0.001 0.70
Abbreviations: d, Cohen's deffect size measure; F, between‐group effect test; M, mean value; p, statistical significance; SD, standard deviation; t,
Student's t‐test for independent samples; η
2
, eta‐squared effect size measure.
van HOY and RZESZUTEK
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TABLE 6Distributions of demographic characteristics in the subgroups of participants with different trajectories regarding emotional
exhaustion, depersonalization, personal accomplishments, and satisfaction with life among the study participants.
Profile 1 Profile 2 Profile 3
λdf pn %n%n%
Trajectories based on emotional exhaustion
Gender
Women 44 91.7 74 85.1 78 85.7 1.43 2 0.488
Men 4 8.3 13 14.9 13 14.3
Relationship status
In stable relationship 36 75.0 75 86.2 56 61.5 19.60 6 0.003
Divorced/separated 6 12.5 4 4.6 14 15.4
Widow/widower 0 0.0 0 0.0 4 4.4
Single 6 12.5 8 9.2 17 18.7
Profession
Psychologists 37 77.1 66 75.9 69 75.8 5.69 4 0.223
Psychiatrists 2 4.2 3 3.4 0 0.0
Other 9 18.8 18 20.7 22 24.2
Working hours weekly
1–10 5 10.4 12 13.8 27 29.7 12.18 4 0.016
11–20 14 29.2 34 39.1 27 29.7
More than 30 29 60.4 41 47.1 37 40.7
Trajectories based on depersonalization
Gender
Women 151 87.8 45 83.3 ‐ ‐ 0.68 1 0.410
Men 21 12.2 9 16.7 ‐ ‐
Relationship status
In stable relationship 127 73.8 40 74.1 ‐ ‐ 2.74 3 0.433
Divorced/separated 19 11.0 5 9.3 ‐ ‐
Widow/widower 4 2.3 0 0.0 ‐ ‐
Single 22 12.8 9 16.7 ‐ ‐
Profession
Psychologists 130 75.6 42 77.8 ‐ ‐ 8.70 2 0.013
Psychiatrists 1 0.6 4 7.4 ‐ ‐
Other 41 23.8 8 14.8 ‐ ‐
Working hours weekly
1–10 34 19.8 10 18.5 ‐ ‐ 2.94 2 0.230
11–20 52 30.2 23 42.6 ‐ ‐
More than 30 86 50.0 21 38.9 ‐ ‐
Trajectories based on personal accomplishments
Gender
Women 51 87.9 145 86.3 ‐ ‐ 0.10 1 0.752
Men 7 12.1 23 13.7 ‐ ‐
12
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van HOY and RZESZUTEK
observation that the number of participants with professional back-
grounds other than psychologists or psychiatrists was higher in the
group with lower burnout is intriguing and needs further examination.
The positive role of stable relationships was consistent with more
recent studies on burnout (Simionato & Simpson, 2018), as well as well‐
being predictors among psychotherapists (Laverdière et al., 2018;
Summers et al., 2021). Finally, we found that participants with the
lowest levels of burnout symptoms (see EE declared fewer hours of
work during the week than those with the highest levels of EE). This
result is also in line with previous studies on that topic in this sample
(Simionato & Simpson, 2018), but may also be discussed specifically in
the Polish context, where many psychotherapists are usually highly
overloaded because of severe underinvestment in the mental health
sector (Suszek et al., 2017). This negative trend increased during the
Covid‐19 pandemic. Specifically, many psychotherapists in Poland
overextended themselves not only due to objective reasons (i.e.
excessive client overload) but also because of their positive intentions
of helping as many clients as possible, which may be a universal pattern
during this pandemic in this specific occupation (Rokach & Bou-
lazreg, 2020). However, this over‐optimistic, altruistic attitude also
had the price of subsequent psychological distress, tiredness and
feeling drained, which was experienced by psychotherapists during this
TABLE 6(Continued)
Profile 1 Profile 2 Profile 3
λdf pn %n%n%
Relationship status
In stable relationship 43 74.1 124 73.8 ‐ ‐ 1.92 3 0.589
Divorced/separated 7 12.1 17 10.1 ‐ ‐
Widow/widower 2 3.4 2 1.2 ‐ ‐
Single 6 10.3 25 14.9 ‐ ‐
Profession
Psychologists 40 69.0 132 78.6 ‐ ‐ 3.76 2 0.153
Psychiatrists 3 5.2 2 1.2 ‐ ‐
Other 15 25.9 34 20.2 ‐ ‐
Working hours weekly
1–10 17 29.3 27 16.1 ‐ ‐ 5.09 2 0.078
11–20 19 32.8 56 33.3 ‐ ‐
More than 30 22 37.9 85 50.6 ‐ ‐
Trajectories based on satisfaction with life
Gender
Women 64 87.7 132 86.3 ‐ ‐ 0.09 1 0.771
Men 9 12.3 21 13.7 ‐ ‐
Relationship status
In stable relationship 45 61.6 122 79.7 ‐ ‐ 13.68 3 0.003
Divorced/separated 13 17.8 11 7.2 ‐ ‐
Widow/widower 0 0.0 4 2.6 ‐ ‐
Single 15 20.5 16 10.5 ‐ ‐
Profession
Psychologists 48 65.8 124 81.0 ‐ ‐ 6.54 2 0.038
Psychiatrists 3 4.1 2 1.3 ‐ ‐
Other 22 30.1 27 17.6 ‐ ‐
Working hours weekly
1–10 17 23.3 27 17.6 ‐ ‐ 1.89 2 0.390
11–20 26 35.6 49 32.0 ‐ ‐
More than 30 30 41.1 77 50.3 ‐ ‐
van HOY and RZESZUTEK
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13
critical period of the pandemic (Brillon et al., 2021). It seems that many
psychotherapists established unrealistic expectations about their
ability to help and forgot about one fundamental issue when they do
not care for themselves, they are unable to properly help others
(Rokach & Boulazreg, 2020).
4.1
|
Strengths and limitations
The current study has several strengths, including the theoretically
driven and longitudinal person‐centred methodological design with a
large sample of psychotherapists with very low dropout levels
observed during the critical period of the Covid‐19 pandemic. Never-
theless, a few limitations should be underscored. First, for organiza-
tional reasons, our samples of psychotherapists were heterogeneous
regarding psychotherapy approaches and work‐related characteris-
tics. However, this is a relatively common shortcoming in the literature
on the psychological functioning in this occupation (Simionato &
Simpson, 2018) and is hard to avoid when trying to conduct reasonable
longitudinal research in this particular study sample. Second, this study
was based on self‐report measures, so one should not forget about the
related issues of social desirability bias. In particular, the MBI‐HS
evaluates burnout risk only, so it cannot be used for burnout diag-
nosis among our participants. Moreover, we assessed the level of
Covid‐19 related psychological distress in a very simple manner.
Perhaps a more thorough examination of these variables would obtain
a more comprehensive picture of how the Covid‐19 pandemic influ-
enced their professional lives. Finally, our study sample was a conve-
nient sample of Polish therapists (see also selection bias) and thus
cannot be treated as representative of the Polish therapists.
5
|
CONCLUSION
Our results may provide some important theoretical and clinical
implications. First, our study underlines the need for further appli-
cation of a prospective, person‐centred approach in burnout (Leiter &
Maslach, 2016) and SWB research (Lazić et al., 2021; Li et al., 2022).
This methodological design may bring us closer to understanding how
burnout and SWB change over time while identifying the factors
responsible for these changes. However, one should not forget about
the fact that the progress in the field of occupational burnout and
SWB needs more theory‐driven studies, highlighting explicitly the
role of dynamic processes in these variables (Diener et al., 2006;
Ritter et al., 2016).
From a clinical perspective, our study calls for greater interest in
the psychological health of psychotherapists. Although this research
was conducted in Poland, our findings may entail universal implica-
tions for the practice of psychotherapy by creating customized
intervention programs to reduce burnout and enhance well‐being in
this specific occupation. In particular, there is a need to create more
programs for psychotherapists on how they may cope with work
stress in their everyday life by concentrating on their self‐care
behaviours and learning to greater focus on their physical, emotional,
and spiritual needs, which may help them to balance their personal
and professional lives and, in turn, avoid burnout and enhance the
satisfaction from this occupation (McCormack et al., 2018; Nor-
cross & VandenBos, 2018).
ACKNOWLEDGEMENTS
This project has received funding from the New Ideas of POB V
project implemented within the scope of the ‘Excellence Initiative ‐
Research University’ Program (number PSP: 501‐D125‐20‐
5004310).
CONFLICT OF INTEREST STATEMENT
All the authors declare no conflict of interest.
DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available from the
corresponding author upon reasonable request.
ETHICS STATEMENT
All procedures performed in studies involving human participants
were in accordance with the ethical standards of the institutional
and/or national research committee and with the 1964 Helsinki
Declaration and its later amendments or comparable ethical
standards.
ORCID
Angelika Van Hoy
https://orcid.org/0000-0003-3456-9433
Marcin Rzeszutek https://orcid.org/0000-0002-4230-3806
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How to cite this article: Van Hoy, A., & Rzeszutek, M. (2023).
Trajectories of burnout and psychological well‐being among
psychotherapists during the Covid‐19 pandemic: Results of a
1‐year prospective study. Stress and Health, 1–17. https://doi.
org/10.1002/smi.3230
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