A factor mixture analysis of social stigma toward people with mental illness

Abstract

Background: Social stigma toward people living with mental illness and the associated treatment avoidance are acute in the Chinese communities. There is an urgent need to identify ways to eradicate stigma and promote the understanding of people living with mental illness (PLMI). The present study explored the cluster patterns and attributing factors of social stigma in Hong Kong using a factor mixture analysis. Methods: Participants were a university sample of 218 Chinese adults (mean age = 22.4 years, SD = 6.1). They filled in a self-report questionnaire which measured the attribution factors of social stigma, social distance, and interpersonal reactivity toward PLMI. The Attribution Questionnaire was used to assess nine stigmatizing attitudes toward PLMI: pity, danger, fear, blame, segregation, anger, help, avoidance, and coercion, on a 9-point rating scale. Latent profile analysis and factor mixture analysis were carried out using Mplus 7 and the identified classes were validated by comparing their demographics and attributing factors using a stepwise distal outcome approach. Results: Two latent classes were identified in the factor mixture models with good classification accuracy. The majority of the participants (N = 175, 80.2%) belonged to the low-stigmatizing class with low to moderate degrees of expression of stigmatizing attitudes toward PLMI. The high-stigmatizing class (N = 43, 19.8%) displayed moderate to high degrees of expression of stigmatizing attitudes toward PLMI. Compared to the low-stigmatizing class, participants in the high-stigmatizing class was more likely to be male, younger, and reported significantly higher social distance, personal distress, and empathetic concern. Discussions: The different group profiles elucidated the complex interactions among emotions, thoughts, and behavior of social stigma toward PLMI. An appreciation of the complexity in stigma patterns enhances psychiatric services through tailored education and promotion initiatives. Acknowledgement: This study was supported by the Public Policy Research Scheme, Research Grants Council (HKU 7006-PPR-11).