A network approach to parental burnout

Blanchard, M. Annelise; Roskam, Isabelle; Mikolajczak, Moïra; Heeren, Alexandre

doi:10.1016/j.chiabu.2020.104826

Cited by 68 publications

(84 citation statements)

References 57 publications

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“…Finally, we followed recent publications (e.g., Bernstein et al, 2017;Blanchard et al, 2021;Heeren et al, 2020;McNally et al, 2017, Moffa et al, 2017 and relied on Bayesian network methods to estimate a directed acyclic graph (DAG), which encodes the conditional independence relationships between the variables of interest and characterizes their joint probability distribution (McNally, 2016;Pearl et al, 2016;Scutari, 2010). Hence, the resulting network is directed and possesses arrows reflecting the predicted direction of the probabilistic dependence between nodes (McNally, 2016;Moffa et al, 2017).…”

Section: Anxiety During the Covid-19 Lockdownmentioning

confidence: 99%

“…Following previous psychological research (e.g., Bernstein et al, 2017;Blanchard et al, 2021;Heeren et al, 2020;McNally et al, 2017), we estimated the DAGs via the implementation of a Bayesian hill-climbing algorithm (Scutari, 2010;Scutari & Denis, 2015). To do so, we relied on the R package bnlearn (Scutari, 2010;Scutari & Denis, 2015).…”

Section: Directed Acyclic Graph (Dag)mentioning

confidence: 99%

“…This bootstrap function requires an iterative procedure of randomly restarting this process with various possible edges connecting various node pairs, disturbing the network system, and applying 50 different random restarts to circumvent local maxima. As in recent implementations of this algorithm (e.g., Bernstein et al, 2017;Blanchard et al, 2021;Heeren et al, 2020;McNally et al, 2017), we introduced, for each restart, 100 perturbations (i.e., ANXIETY DURING THE COVID-19 LOCKDOWN 13 attempts to insert, delete, or reverse an edge). As this iterative process of restart/perturbations unfolds, the algorithm returns the model with the optimal BIC value.…”

Section: Directed Acyclic Graph (Dag)mentioning

confidence: 99%

“…As in recent publications (e.g., Bernstein et al, 2017;Blanchard et al, 2021;, we then ensured the stability of the resulting DAG as follows. We bootstrapped 10,000 samples (with replacement), estimated a network for each of the bootstrapped 10,000 samples, and ultimately averaged the resulting 10,000 networks to generate a final network structure via a two-step method.…”

Section: Directed Acyclic Graph (Dag)mentioning

confidence: 99%

“…For ease of interpretation, we followed prior research (e.g., Bernstein et al, 2017;Blanchard et al, 2021;Heeren et al, 2020;McNally et al, 2017) and produced two visualizations of the resulting outputs. In the first one, the thickness of the arrow represents the change in the BIC values when that arrow is removed from the network.…”

Section: Directed Acyclic Graph (Dag)mentioning

confidence: 99%

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Excessive Worrying as a Central Feature of Anxiety during the First COVID-19 Lockdown-Phase in Belgium: Insights from a Network Approach

Heeren¹,

Hanseeuw²,

Cougnon³

et al. 2021

Preprint

Self Cite

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Since the WHO declared the COVID-19 pandemic on March 11, 2020, the novel coronavirus, SARS-CoV-2, has profoundly impacted public health and the economy worldwide. But there are not the only ones to be hit. The COVID-19 pandemic has also substantially altered mental health, with anxiety symptoms being one of the most frequently reported problems. Especially, the number of people reporting anxiety symptoms increased significantly during the first lockdown-phase compared to similar data collected before the pandemic. Yet, most of these studies relied on a unitary approach to anxiety, wherein its different constitutive features (i.e., symptoms) were tallied into one sum-score, thus ignoring any possibility of interactions between them. Therefore, in this study, we seek to map the associations between the core features of anxiety during the first weeks of the first Belgian COVID-19 lockdown-phase (n = 2,829). To do so, we implemented, in a preregistered fashion, two distinct computational network approaches: a Gaussian graphical model (GGM) and a directed acyclic graph (DAG). Despite their varying assumptions, constraints, and computational methods to determine nodes (i.e., the variables) and edges (i.e., the relations between them), both GGM and DAG pointed to excessive worrying as a node playing an especially influential role in the network system of the anxiety features. Altogether, our findings offer novel data-driven clues for the ongoing field's larger quest to elucidate, and eventually alleviate, the mental health consequences of the COVID-19 pandemic.

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Section: Anxiety During the Covid-19 Lockdownmentioning

confidence: 99%

Section: Directed Acyclic Graph (Dag)mentioning

confidence: 99%

Section: Directed Acyclic Graph (Dag)mentioning

confidence: 99%

Section: Directed Acyclic Graph (Dag)mentioning

confidence: 99%

Section: Directed Acyclic Graph (Dag)mentioning

confidence: 99%

See 3 more Smart Citations

Excessive Worrying as a Central Feature of Anxiety during the First COVID-19 Lockdown-Phase in Belgium: Insights from a Network Approach

Heeren¹,

Hanseeuw²,

Cougnon³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

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The Dialectical Relationship Between Burnout and Work Engagement: A Network Approach

Hafstad,

Ebrahimi,

Fostervold

2024

Stress and Health

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Based on previous empirical evidence, scholars have argued for a dialectical relationship between burnout and work engagement. However, these conclusions largely rely on latent variable models, which have inherent limitations due to strong hierarchical assumptions about data. As a result, previous findings should be considered preliminary indications rather than conclusive evidence. In this study, we introduce network analysis to investigate the relationship between burnout and work engagement. We demonstrate its utility by comparing the fit indices of the network model and three factor models in a cross‐national sample with 2469 employees from Norway and 879 employees from the US and UK. Based on theory and fit indices, we conclude that the network model was preferred in both samples. Using this model, we uncovered positive relationships between two work engagement variables and the exhaustion component of burnout. Theoretically, this study broadens our understanding and show support for the dialectical relationship between burnout and work engagement. Furthermore, by introducing the network model to empirical research, we provide a novel approach that contribute to nuance and ideas for research on burnout and work engagement. Practically, our results offer insightful data on possible points for intervention between burnout and work engagement variables.

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