A Network Approach to Exploring Effects of Self-Stigma Intervention in Serious Mental Illness

Self-stigma is a pervasive problem among individuals with serious mental illness (SMI) that can negatively affect their well-being and quality of life. Although several interventions have been developed to reduce self-stigma, there is little research into which components of these treatments relate to improvement. In this study, we used network analysis as a novel exploratory method for identifying processes contributing to change in self-stigma and related variables.

We compared networks of an active stigma intervention called Ending Self-Stigma (ESS) to a non-stigma intervention, and an enhanced treatment-as-usual stigma intervention. We included clinical symptoms, signs of recovery orientation, and expressions of self-stigma as nodes in the network. We examined the changes in the overall network structure of self-stigma-related variables before and after each intervention, as well as the role of individual nodes in the networks.

Our results suggest that the intervention did not change the structure of any networks. However, we found that psychoticism, withdrawal, alienation, and self-efficacy were important in explaining variance in stigma-related variables across interventions.

Our findings may inspire future dismantling studies investigating the role of key factors in self-stigma interventions and supports examining changes in self-stigma-related variables as a network of interconnected features.