A longitudinal study on the effects of social support on self-stigma, psychiatric symptoms, and personal and social functioning in community patients with severe mental illnesses in China.

BACKGROUND: Few studies have examined whether social support contributes to better consequences among chronic patients with severe mental illnesses (SMI) in their community recovery stage and whether self-stigma would be a mechanism through which social support impacts psychiatric symptoms and personal and social functioning. AIMS: This study aimed to examine prospective associations of social support with long-term self-stigma, psychiatric symptoms, and personal and social functioning, and to investigate whether self-stigma would mediate the associations of social support with psychiatric symptoms and personal and social functioning among patients with SMI. METHODS: A total of 312 persons with SMI (schizophrenia and bipolar disorder) in their community recovery stage participated in the study. Social support, self-stigma, psychiatric symptoms, and personal and social functioning were evaluated at baseline. The follow-up assessment was conducted at 6 months with the baseline measures except for social support. Hierarchical linear regression and mediation analysis were performed. RESULTS: The results showed that baseline social support predicted decreases in stigma (ß = -.115, p = .029) and psychiatric symptoms (ß = -.193, p < .001), and increases in personal and social functioning (ß = .134, p = .008) over 6 months, after adjusting for relevant covariates. Stigma at 6 months partially mediated the association between baseline social support and 6-month psychiatric symptoms (indirect effect: ß = -.043, CI [-0.074, -0.018]). Stigma and psychiatric symptoms at 6 months together mediated the association between baseline social support and 6-month personal and social functioning (indirect effect: ß = .084, 95% CI [0.029, 0.143]). CONCLUSION: It is necessary to provide comprehensive social support services and stigma reduction interventions at the community level to improve the prognosis of SMI.

A Multiscale Clustering Approach for Non-IID Nominal Data.

Multiscale brings great benefits for people to observe objects or problems from different perspectives. Multiscale clustering has been widely studied in various disciplines. However, most of the research studies are only for the numerical dataset, which is a lack of research on the clustering of nominal dataset, especially the data are nonindependent and identically distributed (Non-IID). Aiming at the current research situation, this paper proposes a multiscale clustering framework based on Non-IID nominal data. Firstly, the benchmark-scale dataset is clustered based on coupled metric similarity measure. Secondly, it is proposed to transform the clustering results from benchmark scale to target scale that the two algorithms are named upscaling based on single chain and downscaling based on Lanczos kernel, respectively. Finally, experiments are performed using five public datasets and one real dataset of the Hebei province of China. The results showed that the method can provide us not only competitive performance but also reduce computational cost.