| Last Updated:
Creating DOI. Please wait...
Psychological network analysis has been widely used to model symptom networks of psychiatric disorders with promising results for a better understanding of psychopathology (Borsboom et al., 2017). Only few studies exist which transfer this method to the concept of resilience to stress-related psychopathology. Resilience is the adaptive process that leads to maintenance or quick recovery after stress exposure (Kalisch et al., 2017). Resilience factors, such as general self-efficacy, are psychological (or other) constructs contributing to resilience (Kalisch et al., 2017). So far, research approaches mainly include resilience as a dynamic property of psychopathology symptom networks (PSN) (Borsboom et al., 2017) or networks of resilience factors (RFN) after adversity exposure (Fritz et al., 2018). However, it remains unknown how RFN might potentially differ with respect to resilience itself.
The network structure and connectivity of resilience factors might provide important information about resilience after stress exposure. In dynamic PSN, Cramer et al. (2016) stated that stronger network connectivity is associated with an increase in synchronized activation of symptoms resulting in a stable activation pattern that is less likely to reverse. This leads to mutual reinforcement of psychopathology symptoms. This mechanism might actually be beneficial in the case of resilience. Additionally, connectivity differences of cross-sectional RFN might contribute to an enhancement of resilience factors after childhood adversity (Fritz et al., 2018). Thus, increased connectivity of RFN might potentially play a role in the development and maintenance of resilience.
A promising way to quantify resilience as such has been applied for example by van Harmelen and colleagues (2016). They used principal components analysis to extract latent scores of both childhood adversity and psychosocial functioning and related them to each other using regression analysis. Resilience scores (“resilient functioning scores”) were the regression residuals.
This study aims at investigating properties of a network model of self-efficacy with respect to a novel resilience score indicating high versus low individual levels of resilience. Adapting the idea of connectivity differences in dynamic PSN (Cramer et al., 2016) we expect the self-efficacy network to differ in high resilient compared to low resilient individuals. We hypothesize that high resilient individuals show stronger positive connectivity between self-efficacy items than low resilient individuals.
To address this, we firstly employ a novel methodology to compute resilience scores in a large cross-sectional sample of approximately 850 adults. These resilience scores indicate whether one’s mental health is better or worse or exactly as expected given individual stress exposure. We use partial-least squares (PLS) regression (Seidlitz, et al, 2018; Vértes et al. 2016; Whitaker et al., 2016) to predict an expected level of mental health by stress exposure and extract resilience scores as regression residuals. An established measure of resilient functioning (van Harmelen et al., 2017) will be used to validate our PLS regression approach. All data are self-report measures.
As a second step, we split our sample and explore the structure of self-efficacy network models of high vs. low resilient subjects. For the network analysis of both networks, we focus on global connectivity indices - e.g. degree, strength, expected influence, shortest path length, global efficiency - and additionally explore the network structure.
Results might provide a useful way to quantify resilience and help to better understand the nature of self-efficacy with respect to resilience itself.