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PARENTAL BURNOUT: TEMPORAL NETWORK 1
Parental Burnout Features and the Family Context: A Temporal Network Approach in
Mothers
M. Annelise Blanchard1,2, Yorgo Hoebeke1, & Alexandre Heeren1,2,3
1 Psychological Sciences Research Institute, Université catholique de Louvain, Louvain-la-
Neuve, Belgium
2 Belgian National Science Foundation (F.R.S.-FNRS), Brussels, Belgium
3 Institute of Neuroscience, Université catholique de Louvain, Brussels, Belgium
Author Note
M. Annelise Blanchard https://orcid.org/0000-0002-9605-7022
Yorgo Hoebeke https://orcid.org/0000-0003-2565-8311
Alexandre Heeren https://orcid.org/0000-0003-0553-6149
We acknowledge and sincerely thank all participants who gave time out of their daily life
for two months for this study. Correspondence concerning this article should be addressed to M.
Annelise Blanchard. Psychological Sciences Research Institute, Université Catholique de
Louvain, Place du Cardinal Mercier, 10, B-1348, Louvain-la-Neuve, Belgium. E-mail:
marie.blanchard@uclouvain.be.
This manuscript has been accepted in Journal of Family, with the DOI:
https://doi.org/10.1037/fam0001070. Anonymized data, R code, and all materials are
available via the Open Science Framework: https://osf.io/pshdn/.
© 2023, American Psychological Association. This paper is not the copy of record and may not exactly
replicate the final, authoritative version of the article. Please do not copy or cite without authors'
permission. The final article will be available, upon publication, via its DOI: 10.1037/fam0001070
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Abstract
Many parents have days where they encounter emotional exhaustion, emotional distance from
their children, and feeling fed up with being a parent. Some parents experience these
characteristics to a severe extent—a clinical phenomenon termed parental burnout. Parental
burnout arises when parents chronically endure severe stress without sufficient resources to cope,
which may lead to detrimental consequences not only for the parent, but also for their partner
(e.g., marital conflict) and children (i.e., neglect and violence). However, uncertainty remains
regarding how these features interact and trigger one another over time (potentially becoming
increasingly severe), nor how the daily variations of the family context influence these features.
Therefore, in this study, we recruited 50 parents (with main analyses focusing on 43 mothers with
a coparent, and sensitivity analyses with the full sample) from the general population to rate the
core features of parental burnout and the family context daily over 56 days. We used multilevel
vector autoregressive models to generate network models. Results suggest that exhaustion
contributes to parental burnout: it self-predicts and is closely associated with feeling fed up and
finding children difficult to manage. Distance, by contrast, is mainly negatively connected to
sharing positive moments with children. Contextual variables also interact with parental burnout
features, illustrating the relevance of examining parenting within the family system context. If
future research confirms a central role of exhaustion in parental burnout development, prevention
efforts can focus on decreasing parental exhaustion.
Keywords: parenting, parental burnout, daily diary, family system, network approach
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Parental Burnout Features and Their Family Context: A Temporal Network Approach in
Mothers
Parents have a challenging role, without question: they are responsible for caring for their
children and raising them into adults. While parenting can be rewarding and joyful on some days,
it can be difficult and frustrating on others. And for some parents, parenting brings overwhelming
amounts of stress and exhaustion (e.g., pressure to be a perfect parent, dysfunctional family
dynamics) without sufficient resources (e.g., supportive partner or extended family, emotional
regulation skills) to cope (Mikolajczak & Roskam, 2018). If this imbalance persists for too long,
the parent can experience parental burnout, involving four features: emotional exhaustion,
emotional distance from the children, feeling fed up with parenting, and a sense of contrast with
the previous parental self (Roskam et al., 2018). Research into parental burnout has only recently
begun in earnest (Roskam et al., 2017), but it is already widespread across many languages and
cultures (e.g., Arikan et al., 2020; Furutani et al., 2020; Mousavi et al., 2020).
Indeed, parental burnout is reported in countries around the world, with the highest
prevalence rates rising to 8% of parents (Roskam et al., 2021). It has been linked with negative
consequences for the parent (e.g., suicidal ideation and addiction), the couple (e.g., marital
conflict), and the child (e.g., neglect and abuse) in cross-sectional research (Hansotte et al., 2021;
Mikolajczak et al., 2018), as well as in longitudinal (Mikolajczak et al., 2019) and intervention
(Brianda et al., 2020) research. Parenting stress and pressure have only increased during the
COVID-19 pandemic (Griffith, 2020), with more parents (especially those with fewer resources)
feeling exhausted and burned out (Aguiar et al., 2021; Kerr et al., 2021). This growing priority to
help parents in difficulty, as well as the association of parental burnout with severe consequences
for the children, highlights the pressing need to understand precisely how parental burnout
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develops and persists. Only with specific knowledge on the development of parental burnout can
practitioners effectively prevent and treat parents with parental burnout.
Because parental burnout research is still in its early days, however, critical gaps remain
in the literature. For example, most research has only investigated parental burnout as a cohesive
and unitary phenomenon, with all four features (i.e., exhaustion, distance, feeling fed up, and
contrast) summed into one whole. However, the few studies that have investigated these features
separately (e.g., Blanchard, Roskam, et al., 2021; Hansotte et al., 2021; Kalkan et al., 2022) have
all found that specific parental burnout features have distinct associations with family-related
variables. For instance, emotional distance is most strongly associated with neglect toward
children. In addition, the literature on parental burnout (Mikolajczak & Roskam, 2018) and on
burnout more generally (Lee & Ashforth, 1993; Leiter, 1993) posits that exhaustion is the first
step toward developing burnout. This possibility promotes investigating of the four features of
parental burnout separately, to examine whether certain features are implicated in the instigation
or maintenance of parental burnout.
Another crucial area for growth in the parental burnout literature involves the temporal
unfolding of parental burnout. Until now, most research has utilized a cross-sectional approach
and only investigated parental burnout at one timepoint. There are a growing number of
longitudinal studies, but most did not investigate the evolution of the different features of parental
burnout (e.g., Mikolajczak et al., 2019; Yang et al., 2021). Among the few studies that did (e.g.,
Aguiar et al., 2021; Roskam & Mikolajczak, 2021), they demonstrate that the features evolve in
different patterns—and suggest that emotional exhaustion is the first step to developing parental
burnout. Yet these studies all investigated parental burnout at month-long intervals, as required to
understand the long-term evolution of parental burnout. However, the experience of parenting is
something that varies from day to day, according to the ebb and flow of daily interactions with
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the children (e.g., Rodriguez & Silvia, 2022), partner or extended family (e.g., Gillis & Roskam,
2019), and wider family context (e.g., Malinen et al., 2017). It thus is also relevant to examine
how the experience of parenting (including specifically feeling exhausted, fed up, and distant
from one’s children) can fluctuate from day to day, and how these fluctuations interact with the
family context. These characteristics—feeling exhausted, distant, and fed up—are sometimes
experienced to an extreme extent, as when a parent is in a severe state of burnout; but all parents
will sometimes feel exhausted, distant, or fed up to some degree. To allow us to examine how
parental burnout might develop, as well as how these characteristics are experienced by most
parents and influenced by the family context, we decided to focus on the experiences of parents
in the general population. However, our data collection ended up including almost all mothers.
We therefore focus the analyses in this manuscript on this sample of mothers (but include
sensitivity analyses with the full sample in the supplementary materials).
Our goal was therefore to examine the fluctuating experiences of the parental burnout
features and interactions with the children, partner, and wider family environment (e.g., social
support and parenting resources). We wanted to examine how these variables interact with each
other and with the family context, to better understand their daily dynamics. To model the
dynamic interactions between many variables, we used temporal network analyses, which are
especially suited to visualizing dynamic multivariate relationships. Specifically, we generated
three networks: 1) a temporal network to examine which variables predicted others from one day
to the next; 2) a contemporaneous network to inspect how variables interrelated within the same
day; and 3) a between-subjects network to observe the mean-level relationships between
variables.
Methods
Sample Size
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Since there is currently no possibility to estimate a priori power analyses for temporal
networks (especially without previous temporal network analyses on the same variables), we
preregistered recruiting a minimum of 40 participants with 80% compliance, based on studies
with a similar number of timepoints and nodes (Curtiss et al., 2019; de Vos et al., 2017; Lutz et
al., 2018). We recruited parents that had at least one child living at home.
Participants
We recruited fifty French-speaking parents in Belgium through Facebook parenting pages
and other online spaces. Of these, three were single parents
1
, who were not included in the
primary analyses (since these networks include nodes for partner support and conflict) but are
included in a sensitivity analysis in the supplementary materials (with all 50 parents; see Figure
S5). As only four parents were men (not enough to make strong conclusions on the experience of
fathers), we focus our analyses in this study on the 43 mothers with coparents; this is a deviation
from our preregistration. However, we include a sensitivity analysis with all 47 parents with a
coparent in the supplementary materials (see Figure 3). All parents with a partner were in a
man/woman relationship.
The total sample included in the main results therefore includes 43 mothers (see Table 1
for further demographic information). Participants’ net family income was average-to-high for
Belgium (see supplementary materials; Statbel, 2021). The mean number of surveys answered
per person was 52.28, with a compliance rate of 93%.
Baseline Measures
Parental Burnout Assessment (PBA)
1
“Single parents” here refers to a parent who is not parenting with a partner (e.g., someone with whom the
participant shares childcare duties daily, typically but not necessarily living together).
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We assessed parental burnout using the PBA (Roskam et al., 2018), which measures the
four features of parental burnout. Parents answered questions about emotional exhaustion (9
items; e.g., When I get up in the morning and have to face another day with my child(ren), I feel
exhausted before I’ve even started), emotional distance toward their child(ren) (3 items; e.g., I’m
no longer able to show my child(ren) how much I love them), feeling fed up (5 items; e.g., I can’t
take being a parent anymore), and a sense of contrast with their previous parental self (6 items;
e.g., I’m ashamed of the parent I’ve become). Participants rated each item using a 7-point Likert
scale ranging from 0 (never) to 6 (every day), and relevant items were reverse-scored. Scores
could range from 0 to 138. Within the present sample, internal reliability was good for both the
global scale (Cronbach’s α = .97) and the individual subscales (Exhaustion: α = .94; Distance: α
= .84; Feeling Fed Up: α = .89; Contrast: α = .91).
Balance of Risks and Resources (BR2) Questionnaire
We examined participants’ parenting risks and resources using the BR2, including
perfectionist personality traits, stress management capabilities, parenting practices, co-parenting,
etc. (Mikolajczak & Roskam, 2018). Each of the 39 items took a bipolar form, with a risk factor
on the left side (e.g., I find it difficult to reconcile my family life and my professional life) and the
corresponding resource on the right side (e.g., I can easily reconcile my family life and my
professional life). Parents answered each item from -5 to 5, with a negative number indicating the
risk factor statement more closely mirrored their experience, and a positive number indicating the
resource statement more closely reflected their experience. A rating closer to |5| signifies a
stronger endorsement of the (negative or positive) statement, while 0 indicates that the parent
possessed neither risk factor nor resource factor. Scores could range from -195 to 195, with a
negative score indicating parents have more risks than resources (and vice versa for a positive
score).
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Beck Depression Inventory-II (BDI-II)
We assessed depression symptoms using the BDI-II (Beck et al., 1996). Participants
answered 21 questions, each time choosing the one statement out of four that most described how
they felt during the past two weeks regarding specific symptoms (e.g., I do not feel sad to I am so
sad and unhappy that I can’t stand it) on a four-point scale from 0 to 3. Scores could range from
0 to 63. Within this sample, internal reliability was high, with a Cronbach’s α of .93.
Generalized Anxiety Disorder Scale (GAD-7).
We examined trait anxiety using the GAD-7 (Spitzer et al., 2006). Participants indicated
their anxiety over the past two weeks on seven items (e.g., worrying too much about different
things) using a four-point scale from 0 (Not at all) to 3 (Nearly every day). Scores could range
from 0 to 21. Internal reliability was high within this sample, with a Cronbach’s α of .93.
Daily Diary Survey
The daily dairy survey consisted of 11 items focused on the parenting experience and
family context. Ten items were assessed with a slider scale from 0 (not at all) to 100 (absolutely).
These items focused on parental burnout (specifically emotional exhaustion, emotional distance,
and feeling fed up
2
), partner relationship (partner support and conflict), children-focused
relationship (finding children difficult to manage, sharing positive moments, and getting angry),
resources, and social support. Parents answered the last item, measuring hours spent with kids, by
entering a number between 0 and 24. The exact items can be found in Table 2, and we have
2
We do not include a daily item for “a sense of contrast with the previous parental self,” since although this is an
important component of parental burnout, its definition implies a stability over time (e.g., likely changing only over
months or longer) that cannot be captured with daily surveys.
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previously described their development procedure and psychometric properties (Blanchard,
Revol, et al., 2022).
3
We reverse-scored parents’ responses for emotional distance and resources.
Daily Diary Procedure
First, a researcher conducted an introductory briefing session with each parent
individually over a video call, with the experimenter explaining the overall study and
demonstrating the daily diary items and software to the participant. We used formr, an open-
source software, to collect data (Arslan et al., 2020); for further information, see Blanchard,
Revol, et al. (2022). Participants received the link to complete the demographic questionnaires,
and then, the next day, the first of the 56 daily surveys started. Parents received a notification
(email or text message, as they preferred) with that day’s survey each evening, between 6 PM and
9 PM (we asked parents to choose a time after most of their interactions with their children were
over for the day). Participants received compensation: Each participant received 10€, and those
who completed at least 80% of surveys received an additional 25€. Parents provided written
informed consent to share their anonymized data, and the project received approval from the local
Biomedical Institutional Review Board (approval date: May 11, 2020; protocol title: PBNET).
Each day, daily diary questions appeared in a random order (except ‘Time with Kids’, which
always appeared last). We decided on a daily sampling scheme since parenting is typically more
active at some times of the day over others (e.g., on weekdays: in the early morning before school
but mostly the afternoon/evening after school). In addition, previous research suggests that
parenting exhaustion varies from day to day (Gillis & Roskam, 2019); we assume that emotional
3
These items were conceptualized, created and piloted within the context of the experience sampling methodology
(ESM) literature, which entails asking questions from one to many times a day over a long period of time. During
this development period, described in detail in Blanchard, Revol, et al. (2022), we decided that a frequency of once
per day would be most suitable for the parents (as many parents described spending mostly the evening with their
children) as well as the items themselves (which we hypothesized would vary daily). Since these items were only
assessed once per day, we describe this data collection as a “daily diary,” despite the items themselves being created
in the context of ESM literature.
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distance, feeling fed up, and other parenting-related variables would also vary daily, and so a
daily sampling scheme would be suitable to investigate these variables. The eight weeks of data
collection ran from April through June of 2021. Further procedure information is reported in the
supplementary materials.
Data Analysis
We performed all analyses using R Statistical Software (v4.1.0; R Core Team, 2021) and
used packages mlVAR (Epskamp et al., 2019) to estimate network models and qgraph (Epskamp
et al., 2012) to visualize them.
Assumptions: Normality & Stationarity
We checked for violations of normality using the Kolmogorov-Smirnov test, correcting
for multiple testing with Bonferroni, following Aalbers et al. (2019). We also examined residual
plots. We found that no variables were normally distributed. We followed our preregistration and
log-transformed any variables with skew or kurtosis values outside the acceptable range of -2 to 2
(Aalbers et al., 2019). However, this transformation did not render variables closer to a normal
distribution. We therefore attempted a different transformation using the R package LambertW,
which uses an automatic procedure to optimally transform heavy-tailed and skewed data (Goerg,
2015). Although Kolmogorov-Smirnov tests suggested this transformed data was still not
normally distributed, the skew and kurtosis values were at least within the range of -2 to 2. We
thus used this transformation for the remainder of analyses (a deviation to our preregistration of
only using a log transformation for non-normal data). As preregistered, we conducted a
sensitivity analysis to examine whether transforming data to adhere to a normal distribution
would change the pattern of results; it did (specifically for the temporal network, which was
sparser with transformed data; see the supplementary materials for more details). As there is little
information on how transforming non-normal intensive longitudinal data could impact the
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interpretation of temporal network analyses (Blanchard, Contreras, et al., 2022), we report
network analyses based on the raw results in the manuscript. However, we report and discuss a
model estimated from the transformed data in the supplementary materials (see Figure S3).
To check for violations of stationarity, we used the Kwiatkowski-Phillips-Schmidt-Shin
unit root test (KPSS; Kwiatkowski et al., 1992) to verify that the variance of all variables
remained stable over time, as recommended by Jordan et al. (2020). We conducted the KPSS test
for each variable of each participant, correcting for multiple testing with Bonferroni. All variables
appeared stationary.
Network Analyses
We modeled the parenting and family-related variables as networks using a multilevel
vector autoregressive (VAR) approach. VAR models regress a variable at time t on itself and on
all other variables at time t-1: they thus estimate how well each variable predicts all other
variables at the next timepoint (Epskamp et al., 2018). To account for the dependency of
timepoints within subjects, we estimated the VAR model using a multilevel framework. This
resulted in a temporal network, which visualizes the associations between variables from one
timepoint to the next using arrows, while controlling for all other associations. The participant
means are then used to generate a between-subjects network. This network shows the associations
between variables on average across participants, collapsing across time (and controlling for all
other variables). The between-subjects network is most comparable to partial-correlation cross-
sectional networks (showing mean-level associations remaining after controlling for all other
associations). Next, the contemporaneous (e.g., within the same timepoint) associations between
all variables are estimated by regressing the residuals of the multilevel VAR model on all other
residuals from that same timepoint. The resulting contemporaneous network visualizes how
variables are related within the same timepoint, after controlling for all other contemporaneous
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associations and temporal associations; it can thus be thought of as a partial-correlation network.
Epskamp et al. (2018) propose that the contemporaneous network likely captures processes that
occur more quickly than the lag interval in the data (e.g., daily for this study).
Model Specifications. We estimated the multilevel VAR model through sequential
estimation of univariate multilevel regression models (Bringmann et al., 2013; Epskamp et al.,
2018). We allowed random effects in the model to be correlated. When visualizing the
contemporaneous and between-subject networks, we used the “and’’ rule, requiring edges
included in the networks to have both coefficients (from node X to node Y and vice-versa) be
significant.
Additional Analyses
We calculated strength centrality (i.e., how connected a node is with other nodes) for all
nodes in all networks (McNally, 2016). We report both the specific methods and the centrality
indices in the supplementary materials (see Figure S1). We also estimated the stability of these
centrality indices using case-dropping, following the method and code of Jongeneel et al. (2020).
The exact methods and results are reported in the supplementary materials (see Figure S2), but
overall, the centrality indices were stable for the contemporaneous and temporal networks, and
not stable for the between-subjects network.
Transparency & Openness
We report how we determined our sample size, all data exclusions as well as the reason
for exclusion, all manipulations (e.g., none), and all measures (either in the manuscript or in the
supplementary materials). This study was exploratory, but we preregistered our study design, data
collection procedure, and analysis plan following the preregistration template for experience
sampling methodology (ESM; i.e., intensive longitudinal data collected in participants’ everyday
lives) research from Kirtley et al. (2021): https://osf.io/dz7nv. We specify in the manuscript
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where we deviated from this preregistration. We also share the R code, materials, and
anonymized data on the Open Science Framework (https://osf.io/pshdn/), as well as our
supplementary materials (https://osf.io/g5t7h/).
Results
Descriptive Statistics
For each variable, intra-individual means, standard deviations, and intraclass coefficient
correlations, can be found in Table S1. For all baseline measures (e.g., depression, anxiety,
parental burnout), the vast majority of the sample was below clinical cut-offs, confirming that
this sample was indeed from the general population.
Network Analyses
Temporal Network
Figure 1 represents how one variable at one timepoint predicts another (or itself) at the
next time point, accounting for all other associations (edge values can be found in Table S2).
Most variables temporally predicted themselves (i.e., autocorrelated), except for distance, feeling
fed up, and anger toward children. Of note, therefore, emotional exhaustion was the only parental
burnout feature that self-predicts from one timepoint to the next. No bidirectional associations or
cycles were present in the graph. The strongest edge pointed from positive moments with
children negatively predicting distance. Among variables relating to the family context, partner
conflict at one timepoint predicted greater social support at the next, which in turn predicted less
partner support. Increased partner support, for its part, predicted decreased emotional distance.
Contemporaneous Network
For this network showing partial correlations within the same day (with edge values in
Table S3), the strongest edge still negatively connected emotional distance with sharing positive
moments with children. Emotional exhaustion and feeling fed up were strongly connected to one
PARENTAL BURNOUT: TEMPORAL NETWORK 14
another, as well as to perceived resources and finding children difficult to manage. Finding
children difficult to manage, for its part, showed a strong connection to getting angry with
children, which was strongly connected with feeling fed up. Within one day, partner support and
partner conflict were only weakly connected to each other, and isolated from other nodes.
Between-Subjects Network
For this network visualizing the mean-level partial associations between variables (with
edge values in Table S4), the thickest edge still negatively connected emotional distance and
sharing positive moments with children. Mothers who reported higher levels of exhaustion also
tended to report higher levels of feeling fed up, greater resources, and less partner conflict.
Feeling fed up was negatively associated with partner support and positively associated with
finding children difficult to manage. Social support, for its part, was positively associated with
partner conflict and negatively associated with getting angry with children.
Sensitivity Analyses
We conducted four different sensitivity network analyses: a model with all 47 mothers
(including the four fathers), a model with Gaussianized-transformed data, a model with raw data
including the variable ‘Time with Kids,’ and a model including all 50 parents (including single
parents, and therefore without the partner-related variables). The specific changes between those
networks and the one reported in this manuscript are detailed in the supplementary materials, but
overall, the networks remain relatively consistent: most thick edges remain in all networks. The
major exception is with the Gaussianized-transformed temporal network, which is sparser than
the temporal network with raw data. One interesting small difference is an edge from feeling fed
up toward exhaustion appeared in three of the sensitivity analyses: the network with all parents
with co-parents (n=47; Figure S3), network with all parents with coparents, including the variable
‘time with kids’ (n=47, Figure S5), and the network with all participants (including single
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parents) and no coparenting-related nodes (n=50; Figure S6). With this additional edge, one
cycle appears in the network: exhaustion at one timepoint predicted finding children difficult to
manage at the next timepoint, which in turn predicted feeling fed up, which predicted exhaustion.
Discussion
This study examined how parents’ daily experiences of feeling exhausted, distant, and fed
up interacted with each other and with their family context. Exhaustion predicted finding children
difficult to manage in the temporal network, which in turn predicted feeling fed up. In the
contemporaneous network (i.e., within the same day), exhaustion, feeling fed up, and finding
children difficult to manage were all related to one another, while in the between-subjects
network, exhausted mothers were more likely to also feel fed up, and feeling fed up was
associated in turn with finding children difficult to manage. These three variables therefore seem
closely connected. Interestingly, exhaustion was the only parental burnout feature to self-predict
itself in the temporal network. Taken together, this suggests exhaustion as a kickstart toward
activating the key features of parental burnout, which dovetails with prior theoretical
(Mikolajczak & Roskam, 2018) and empirical works (Le Vigouroux et al., 2022; Roskam &
Mikolajczak, 2021).
Conversely, emotional distance was not connected with either of the other two parental
burnout features in any network. Instead, it was strongly connected in all three networks with
sharing positive moments with children, and in the temporal network, this edge points from
sharing positive moments toward distance. This is consistent theoretically, as sharing positive
moments with children builds closeness. Previous empirical research supports this as well; for
example, a previous daily diary study found that on days where parents report more warmth (i.e.,
less distance) toward their adolescent children, these children reported feeling more loved
(Coffey et al., 2020). Importantly, research on quality time with children highlights that positive
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moments between a parent and child do not need to be intensive, long, or preplanned but can be
spontaneous and part of everyday activities, as long as both child and parent enjoy the time spent
together (Hsin, 2009; Kremer-Sadlik & Paugh, 2007).
Distance being disconnected from the other features of parental burnout also holds with
the theory that exhaustion drives the development of parental burnout (Mikolajczak & Roskam,
2018), as well as burnout more generally (Lee & Ashforth, 1993; Leiter, 1993). The present
sample consists of mostly mothers from the general population, with overall low levels of
parental burnout. It therefore makes sense that exhaustion is especially connected to other
variables in the present networks, as exhaustion is the first active component of parental burnout
for this population. Previous literature has suggested that emotional distance might play a role in
maintaining parental burnout once it has developed (e.g., Blanchard, Roskam, et al., 2021),
although this possibility has not yet been investigated empirically. Nonetheless, if emotional
distance were crucial in maintaining parental burnout, it would make sense that it would only be
very connected to other related variables for parents with high levels of parental burnout. This
therefore remains an important question to examine within samples of parents with severe
parental burnout: are all three features (emotional exhaustion, emotional distance, and feeling fed
up) closely connected in the context of parental burnout? Does emotional distance play a key role
in maintaining parental burnout?
When examining the role of the family context, there were no connections between
partner variables or social support and any parental burnout variables that emerged in all three
networks (and in fact, these nodes were all isolated in the contemporaneous network). However,
distinct connections appeared in the temporal (e.g., partner support negatively predicting
distance) and between-subjects (e.g., partner support negatively correlating with feeling fed up;
partner conflict negatively correlating with exhaustion) networks. These demonstrate that the
PARENTAL BURNOUT: TEMPORAL NETWORK 17
family context (i.e., interactions with the partner and wider social support) interrelates with a
parent feeling exhausted, fed up, and distant. For its part, the node representing resources has
strong connections with the parental burnout features, particularly with exhaustion; an edge
connects these two nodes in all three networks. Interestingly, all edges connecting resources with
other nodes pointed toward resources in the temporal network. For instance, when mothers feel
exhausted one day, they feel like they have less resources the next day. On the other hand, if they
spend positive moments with their children and if they feel distant toward their children, they feel
like they have more resources the next day. This suggests that after mothers are more distant with
their children, they feel like they regain resources later on. This is consistent with a previous
longitudinal study that examined the differential course of the features of parental burnout
(Roskam & Mikolajczak, 2021). The authors suggested that, similarly to theories in the job
burnout literature, detachment might “protect parents from negative affect and cognitions about
parenting.” Overall, these contextual variables interact and predict parental burnout features and
child-related behaviors, illustrating how parenting experiences are informed by and
interconnected with the family context as a whole.
Taking these results together, exhaustion emerges as a potential kickstart of parental
burnout development, as it is highly self-predictive (and so likely accumulates over time) and
could lead to a downstream negative cascade through its close connections with feeling fed up
and finding children difficult to manage. If future studies confirm that exhaustion drives the
initiation of parental burnout, it would be a key target for prophylactic intervention. This coheres
with network theory, which posits that targeting a highly central (e.g., very connected within the
network) node in an early intervention could lead to a beneficial cascade that “turns off” other
nodes (McNally, 2016). Identifying parents at risk of burnout, such as exhausted parents, and
preventing them from developing severe parental burnout, is a critical goal, since parental
PARENTAL BURNOUT: TEMPORAL NETWORK 18
burnout has severe psychological consequences (e.g., neglect and violence toward children;
Mikolajczak et al., 2018, 2019). To target exhaustion, one option could involve practitioners
focusing on the individual family. For example, such interventions could revolve around
decreasing parenting stressors, increasing restorative time for the parents, or lessening the
parenting load over the long term (e.g., increasing routines and predictability for the children, or
better balancing family demands; Masten, 2018; Mikolajczak & Roskam, 2018). Intervening on
emotional exhaustion could also stem from a more system-based perspective, however, by
centering on bolstering adequate parenting resources, family resilience, and community support
(e.g., Masten, 2018).
Emotional exhaustion can also arise from intense social pressure to be a “good parent” (at
least in Euro-American countries, with their intensifying parenting norms; Roskam et al., 2021).
Indeed, studies have suggested that high societal standards for parenting can lead to parental
exhaustion (Kawamoto et al., 2018; Sorkkila & Aunola, 2020). Parents (and particularly mothers)
themselves also describe the pressure they feel to embody the ideal parent, leading them to
overinvest and exhaust themselves (Hubert & Aujoulat, 2018). This is not new: Hays discussed
the ideal of such child-centered and selfless parenting (often expected and prescribed specifically
of the mother) decades ago, terming it “intensive parenting” (Hays, 1996). Parenting experts and
researchers pushed for such “intensive parenting” ideals (e.g., Bradley et al., 1997), implying that
parents (and particularly mothers) should “constantly delight in their child, never feel as though
their child is demanding, accommodate the child’s needs, and never want to leave the child” (Liss
et al., 2013). As Hays deftly argues, these prescriptions are unrealistically demanding and
actively lead to parental guilt, as well as culturally specific and indifferent to diverse parenting
compositions and needs (Hays, 1998). Researchers have posited that parents with unattainable
parenting goals will use too many resources to (try to) reach these impossible goals, thereby
PARENTAL BURNOUT: TEMPORAL NETWORK 19
exhausting themselves and being at risk of parental burnout (Le Vigouroux et al., 2022).
Prevention efforts could therefore also attempt to alter parents’ vision of parenting to something
more realistic. For example, for parents (particularly mothers) who prioritize their children’s
needs above all else and strive to always be positive and warm (as suggested by intensive
parenting methods; Hays, 1998), practitioners may help them to better balance the needs of others
and to be firm while also being compassionate (Dupont et al., 2022). Another possibility would
be to increase parents’ resources, such as by improving parents’ emotional competencies, which
have been shown to buffer against the effects of parental perfectionism (Lin et al., 2021). Of
course, most helpful would be to shift Euro-American societal parenting norms themselves to be
more attainable, but shifting societal norms is slow work implicating political agendas, legal
definitions, expert consensus, and broad social narratives.
The present study has limitations. A first limitation is that the residuals were not normally
distributed, which is an assumption for the multilevel VAR model. In a recent scoping review
about temporal network analyses, less than a quarter of studies examined whether the assumption
of normality is violated (Blanchard, Contreras, et al., 2022), and little is known about how non-
normal data, or transforming the data, might affect the results or interpretation—although
hopefully this will be a target for future statistical and theoretical development. Therefore, we
chose to report the raw data in this manuscript and the transformed networks in the
supplementary materials as a sensitivity analysis, similarly to Faelens et al. (2021). The results
were similar, except for the temporal network generated from transformed data containing fewer
edges. We also assessed the stability of the centrality indices to gauge how sensitive our results
were to specific subsamples, through case-dropping: the only network that was not stable was the
between-subjects network. This is understandable, since individual participants have much more
sway on the results of the between-subjects network (as it collapses across all temporal
PARENTAL BURNOUT: TEMPORAL NETWORK 20
information and uses only mean responses). However, to truly assess whether our results are
stable and accurate would require replication in another sample.
Another limitation is the specificity of our sample. Participants were mostly Belgian
mothers, and we did not collect information about racial or cultural identification. The sample
was a convenience sample (recruited mainly through online parenting pages), and mostly mothers
ended up participating. Since we only had four fathers participate, we are not able to confidently
generalize the present results to fathers, although it is promising that the sensitivity analysis that
includes all parents with coparents, including four fathers (n=47) in Figure S3 is very similar to
the results with just the 43 mothers with coparents. However, one main difference is that Figure
S3 involves a feedback loop between exhaustion, finding children difficult to manage, and feeling
fed up: a thin edge connecting feeling fed up and emotional exhaustion is present in Figure S3 but
not in Figure 1 (with only mothers, n=43). We nonetheless believe that emotional exhaustion
plays a key role in the parental burnout network for mothers, even without this feedback loop in
Figure 1, particularly as emotional exhaustion is still the parental burnout variable with a self-
predicting loop. In any case, this study should be repeated with a sample of mostly or all fathers,
to examine if the network structure is consistent or not—particularly in light of intensive
parenting norms falling especially on mothers, even if through gendered assumptions and not
explicitly (Hays, 1998).
Although this study is the first of its kind and grants new information on how parents’
daily experiences interact with their family context, there is substantial cultural variation in
parenting experiences and parental burnout, specifically relating to individualism (Roskam et al.,
2021). Although the results in this manuscript might generalize to other parents in individualistic
cultures, there might be important differences with parents in collectivist cultures. For example,
parents might have different visions of “ideal” parenting, different childrearing goals, and
PARENTAL BURNOUT: TEMPORAL NETWORK 21
different expectations of community parenting support (Bornstein, 2012). Future studies should
therefore examine daily parenting experiences and the family context in other populations.
Conclusions
Mothers experience different levels of exhaustion, feeling fed up, and distance from their
children every day, and these variables continuously interact with each other and with the family
context. In line with previous research, exhaustion appears as a potential jumpstart to parental
burnout, since it is strongly associated with feeling fed up and finding children’s behavior
difficult to manage, as well as the only parental burnout feature to self-predict from one day to
the next. In this unselected sample, emotional distance is not connected to the other parental
burnout features. Sharing positive moments with children does, however, predict feeling less
distant. Contextual variables (partner support, partner conflict, social support) also interact with
parental burnout variables in various networks (e.g., from one day to the next, on a group level),
emphasizing the relevance of viewing parenting and parental burnout within its context of the
family system.
PARENTAL BURNOUT: TEMPORAL NETWORK 22
Author Contributions
M. Annelise Blanchard: Conceptualization, Methodology, Software, Formal Analysis,
Investigation, Writing – Original Draft. Yorgo Hoebeke: Software, Formal Analysis, Writing –
Review & Editing. Alexandre Heeren: Conceptualization, Methodology, Writing – Review &
Editing.
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Table 1
Demographic Information
Demographic Variable
Mean
SD
Min
Max
Age of parents
37.30
4.08
30
50
Number of children (living under same roof)
2.02
0.80
1
4
Age of children in years (living under same roof)
7.95
5.39
0.01
21.70
Parental Burnout Assessment (total score)
38.81
26.13
8
131
Balance between Risks and Resources (BR2)
53.60
52.43
-100
159
Generalized Anxiety Disorder-7 Questionnaire
7.98
5.60
1
20
Beck Depression Inventory
12.28
10.560
0
52
PARENTAL BURNOUT: TEMPORAL NETWORK 32
Table 2
Parental Burnout and Family Context: Daily Diary Items
Category
Item (English version)
Emotional Exhaustion
I felt exhausted while caring for my children.
Emotional Distance
I felt close to my children in both good and bad situations. (Rev)
Feeling Fed Up
I felt overwhelmed caring for my children.
Partner Support
I received help from my partner with caring for my children.
Partner Conflict
I had some misunderstandings, tension, or arguments with my
partner.
Difficult to Manage (Kids)
My children were difficult to manage.
Positive Moments (Kids)
I shared positive moments with my children.
Angry (Kids)
I got angry with my children.
Resources
I lacked the means (for example, time, energy, material resources)
to take care of my children. (Rev)
Social Support
I received help from friends or family (other than my partner) with
caring for my children.
Time with Kids
Around how many hours did you spend near your children
today (outside of sleeping hours)?
Note. These daily diary items were presented in random order on the same page, with “Today” at
the top. Rev = reverse-scored.
PARENTAL BURNOUT: TEMPORAL NETWORK 33
Figure 1
Network Analyses
Note. Solid blue edges represent positive associations, while dashed red lines represent negative
associations. AngKids = getting angry toward children; DiffKids = finding children difficult to
manage; Dist = emotional distance; Exh = emotional exhaustion; FedUp = feeling fed up;
PartnerConf = partner conflict; PartnerSupp = partner support; PosMoKids = sharing positive
moments with children; Res = resources; SocialSupp = social support. Edge values for all
networks (including p-values, standard errors, etc.) can be found in the supplementary materials
(see Tables S2, S3, and S4).
Exh
Dist
FedUp
PartSup
PartConf
DiffKids
PosMoKids
AngKids
Res
SocSup
Temporal
Exh
Dist
FedUp
PartSup
PartConf
DiffKids
PosMoKids
AngKids
Res
SocSup
Contemporaneous
Exh
Dist
FedUp
PartSup
PartConf
DiffKids
PosMoKids
AngKids
Res
SocSup
Between-subjects
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