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

Area of Science:

• Psychiatry and Mental Health
• Psychology
• Public Health

Background:

• Internalized stigma significantly impacts individuals with schizophrenia.
• Understanding stigma profiles is crucial for effective mental healthcare.
• Predictors of internalized stigma require further investigation.

Purpose of the Study:

• To analyze latent profiles of internalized stigma in schizophrenia patients.
• To identify covariates, including clinical and sociodemographic factors, predicting these stigma profiles.
• To inform targeted interventions for specific at-risk populations.

Main Methods:

• Latent Class Analysis (LCA) was employed.
• A sample of 227 schizophrenia patients from public mental health centers in Arica, Chile, was analyzed.
• Variables included ethnicity, gender, premorbid adjustment, Duration of Untreated Psychosis, and symptom severity.

Main Results:

• Three distinct internalized stigma profiles were identified.
• Premorbid adjustment emerged as a significant predictor of latent stigma profile belonging.
• Ethnicity was not found to be a significant predictor of internalized stigma profiles, contrary to hypotheses.

Conclusions:

• Latent classification models effectively assess stigma profiles in schizophrenia.
• Findings support the development of targeted interventions for specific populations.
• Understanding premorbid adjustment is key to addressing internalized stigma.