A Network Approach to Parental Burnout

Abstract

Background: The use of network analyses in psychology has increasingly gained traction in the last few years. A network perspective views psychological constructs as dynamic systems of interacting elements. Objective: We present the first study to apply network analyses to examine how the hallmark features of parental burnout — i.e., exhaustion related to the parental role, emotional distancing from children, and a sense of ineffectiveness in the parental role — interact with one another and with maladaptive behaviors related to the partner and the child(ren), when these variables are conceptualized as a network system. Participants and setting: In a preregistered fashion, we reanalyzed the data from a French-speaking sample (n = 1551; previously published in Mikolajczak, Brianda, Avalosse, & Roskam, 2018), focusing on seven specific variables: the three hallmark parental burnout features, partner conflict, partner estrangement, neglectful behavior toward children, and violent behavior toward children. Methods: We computed two types of network models, a graphical Gaussian model to examine network structure, potential communities, and influential nodes, and a directed acyclic graph to examine the probabilistic dependencies among the different variables. Results: Both network models pointed to emotional distance as an especially potent mechanism in activating all other nodes. Conclusions: These results suggest emotional distance as critical to the maintenance of the parental burnout network and a prime candidate for future interventions, while affirming that network analysis can successfully expose the structure and relationship of variables related to parental burnout and its consequences related to the partner and the child(ren).

Bridging maladaptive social self-beliefs and social anxiety: a network perspective

“…This involves an iterative process of randomly restarting this process with different possible edges linking different node pairs, perturbing the system, and using 50 different random restarts to avoid local maxima. Following previous studies (e.g., Bernstein et al, 2017, Blanchard et al, 2020McNally et al, 2017), we performed 100 perturbations (i.e., attempts to insert, delete, or SOCIAL SELF-BELIEFS & SOCIAL ANXIETY 13 reverse an edge) for each restart. As this iterative procedure unfolds, the function returns the best fitting network based on this random restart/perturbation procedure.…”

Why we should move from reductionism and embrace a network approach to parental burnout

“…To give an example relevant to parental burnout, if future research confirms that emotional distance is central to parental burnout networks, then clinicians could aim to specifically reduce parents' emotional distance A NETWORK APPROACH TO PARENTAL BURNOUT 10 toward their children, potentially through mental imagery exercises such as imagery rescripting (Blackwell, 2019;Holmes & Mathews, 2010). Since we found that emotional distance was strongly connected to the other parental burnout features as well as child maltreatment (Blanchard et al, 2020), this targeted intervention should diminish these variables as well.…”

“…Not surprisingly, the only network analysis of parental burnout thus far published (Blanchard et al, 2020) supports this idea: parental burnout's features do interact in different ways with family-related variables. In this large cross-sectional network analysis (n = 1551), the authors (Blanchard et al, 2020) investigated the interactions between the three components of the parental burnout inventory (distance, exhaustion, and parental inefficacy; Roskam et al, 2017), as well as marital problems (specifically conflicts and estrangement) and child maltreatment (precisely neglect and violence). The study found that emotional distance was highly central, meaning it emerged as the most influential node in the network system.…”

A Network Approach to the Five-Facet Model of Mindfulness: Insights from Gaussian Graphical and Directed Acyclic Graph Models

“…Finally, we 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 [35,[38][39][40]. Hence, the resulting network is directed and possesses arrows reflecting the predicted direction of the dependence among nodes [35,[38][39][40][41][42]. In this project, we thus relied on DAGs to examine the probabilistic dependencies between our variables MINDFULNESS AS A NETWORK 7 of interest and generate a data-driven, potentially causal, model of the interplay among the five facets of mindfulness.…”

“…We investigated the GGM's community structure-that is, whether nodes form a unitary network structure or whether they cluster into distinct communities-by implementing the Spinglass modularity-based community detection algorithm. As in previous studies [e.g., 34,38,41]), we chose this algorithm given its suitability for revealing the community structure of signed networks [60], that is networks composed of both positive and negative edge weight values. We implemented this algorithm via the spinglass.community function of the R package igraph.…”