期刊名称：Proceedings of the Latvian Academy of Sciences, Section B. Natural, exact, and applied sciences, B dala. Dabaszinatnes
印刷版ISSN：1407-009X
电子版ISSN：1407-009X
出版年度：2019
卷号：73
期号：4
页码：356-363
DOI：10.2478/prolas-2019-0055
语种：English
出版社：The Academy
摘要：The aim of this study was to evaluate the utility of the pathological personality traits in predicting suicidal ideation, especially in combination with other risk factors, such as the level of depression, prior attempts of suicide, low self-esteem, low level of perceived social support and self-esteem-by-social support interaction, both in general and in psychiatric inpatient samples. Data were analysed within two samples: non-clinical general sample (n = 461) and psychiatric inpatient sample (n = 131). Latvian Clinical Personality Inventory (LCPI) was used as the instrument for data collection. LCPI is a comprehensive multi-scale multi-item inventory, which consists of nine clinical scales (including Depression Symptom Scale), 33 pathological personality scales, five functioning scales, and five additional scales, including a Suicidal Ideation Scale, Low Self-esteem Scale and Lack of Perceived Social Support Scale. Results of the hierarchical regression analysis showed that several facet-level pathological personality traits (depressivity, self-harm, dissociation proneness, submissiveness, and suspiciousness) added significant incremental variance to the prediction of suicidal ideation above and beyond the well-known main risk factors of suicidal ideation, such as depression and prior suicide attempt. This effect remained stable even after taking into account additional interpersonal risk factors, such as low self-esteem, low level of perceived social support and self-esteem-by-perceived social support interaction. The incremental effect of personality traits was medium in the psychiatric inpatient sample and small in the general sample. Findings of the study may assist in early screening for persons with suicide risk and for developing prevention programmes in different settings.
