Self-stigma profiles in schizophrenia: a Latent Class Analysis approach

Objective: this study aimed at analyzing the internalized stigma latent profiles and the covariates that predict variations in their levels considering antecedent variables such as ethnicity, gender and some relevant clinical characteristics like premorbid adjustment, Duration of Untreated Psychosis and symptoms. Method: Latent Class Analysis (LCA) was used to devise a solution with three internalized stigma profiles in a sample comprised by 227 patients diagnosed with schizophrenia from the Public Mental Health Centers of the city of Arica, Chile. Results: the results showed that premorbid adjustment is a significant predictor of class belonging for the latent stigma profiles. When analyzing the sociodemographic characteristics and contrary to what was hypothesized, ethnicity was not a relevant predictor of internalized stigma profiles. Conclusion: the latent classification model is suitable for assessing stigma profiles in order to target future interventions in specific foci and at-risk populations.