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

Objective: 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.

Method: 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.

Results: 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.

Conclusion: 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.