The Adaptation of the Substance Use Stigma Mechanism Scale (SU-SMS) Into Turkish: A Validity and Reliability Study

Employing Bayesian analysis to establish a cut-off point and assess stigma prevalence in substance use disorder: a comprehensive study of the Chinese version of the Substance Use Stigma Mechanism Scale

PURPOSE

In China, individuals with substance use disorders (SUD) face severe stigma, but reliable stigma assessment tool is lacking. Therefore, this study aimed to validate the Chinese version of the Substance Use Stigma Mechanism Scale (SU-SMS-C) and set its cut-off point.

METHODS

We recruited 1005 individuals with SUDs from Chinese rehabilitation centers. These participants completed a battery of questionnaires that included the SU-SMS-C, The Multidimensional Scale of Perceived Social Support (MSPSS), Center for Epidemiologic Studies Depression Scale (CES-D), General Self-Efficacy Scale (GSES), and Perceived Devaluation and Discrimination (PDD). Confirmatory factor analysis was used to assess the construct validity of the scale. Additionally, the Naive Bayes classifier was used to establish the cut-off point for the SU-SMS-C. We additionally explored the correlation between patient demographic characteristics and stigma.

RESULTS

A confirmatory factor analysis was utilized, revealing a second-order five-factor model. Based on the Naive Bayes classifier, the area under the receiver operating characteristic (AUCROC) of 0.746, the cut-off point for the SU-SMS-C was established at 44.5. The prevalence of stigma observed in the study population was 49.05%. Significant disparities were observed in the distribution of stigma across genders, with males experiencing more pronounced stigma than females. Moreover, patients consuming different primary substances reported diverse levels of stigma. Notably, those primarily using heroin endured a higher degree of stigma than users of other substances.

CONCLUSION

The study is the first to identify a cut-off point for the SU-SMS-C by Naive Bayes classifier, bridging a major gap in stigma measurement research. SU-SMS-C may help treat and manage SUDs by reducing stigma.

Internalization process of stigma of people with mental illness across cultures: A meta-analytic structural equation modeling approach

This meta-analytic study synthesized findings from 108 independent data sets across 22 cultures to investigate whether the stigma internalization model (the internalization of experienced stigma and perceived stigma to self-stigma) is associated with well-being and recovery of people with mental illness. We also examined the moderating role of collectivism in the internalization process. Results of the meta-analytic structural equation modeling suggested that self-stigma is a significant mediator in the relationships between experienced stigma and perceived stigma with well-being and recovery variables (indirect effects = 0.02 to -0.16). Experienced and perceived stigma had significant direct effects on well-being and recovery variables (Bs = 0.07 to -0.21, p < 0.05), suggesting that both external (e.g., public stigma) and internal (i.e., self-stigma) influences of stigma work concurrently to affect recovery and well-being of people with mental illness. The results of the mixed effect three-level meta-analytic models showed that collectivism significantly moderated the relationship between experienced and perceived stigma with self-stigma (Bs = 0.06 to 0.11, p < 0.05). This implied that the more collectivistic a culture is, the stronger the correlation between experienced and perceived stigma with self-stigma. Implications to stigma reduction approaches were discussed.