PCN Rep. 2025 Apr 25;4(2):e70096. doi: 10.1002/pcn5.70096. eCollection 2025 Jun.
ABSTRACT
AIM: Identifying the underlying factors that trigger suicidal ideation and understanding their interactive effects is essential for predicting suicidal thoughts. This study seeks to explore the psychopathological, socioeconomic, and demographic determinants of suicidal ideation in outpatients referred to a specialized psychiatric clinic.
METHODS: A cross-sectional study was conducted at the psychiatric clinic of the 22nd Bahman Hospital in Qazvin, Iran, from 2020 to 2021. The study comprised 288 participants (78 with suicidal ideation and 210 without). All participants completed the Symptom Checklist-90-Revised (SCL-90-R) and Kuppuswamy’s Socioeconomic Status Scale. Demographic and clinical data were collected and analyzed using appropriate statistical methods.
RESULTS: Individuals with suicidal ideation demonstrated significantly higher SCL-90-R scores compared to those without (150.84 ± 37.87 vs. 119.13 ± 33.81, respectively, p < 0.001). Among the SCL-90-R subscales, Obsessive-Compulsive Disorder, Psychoticism, Depression, and Somatization exhibited the strongest correlations with suicidal ideation (p < 0.001). Significant risk factors for suicidal ideation included elevated SCL-90-R scores (odds ratio [OR] = 1.04, 95% confidence interval [CI]: 1.02-1.05), marital status (being married vs single) (OR = 0.13, 95% CI: 0.05-0.29), education level below diploma (OR = 2.95, 95% CI: 1.17-7.57), low socioeconomic status (OR = 5.80, 95% CI: 1.68-20.44), presence of personality disorder (OR = 3.86, 95% CI: 1.03-14.63), and major depression (OR = 6.40, 95% CI: 1.89-22.42) (p < 0.05).
CONCLUSION: The results indicate that psychopathological symptoms, educational attainment, marital status, and socioeconomic challenges may contribute significantly to the development of suicidal ideation. It is recommended that specialized care clinics prioritize the assessment of suicidal thoughts to facilitate the implementation of appropriate preventive measures.
PMID:40291165 | PMC:PMC12022497 | DOI:10.1002/pcn5.70096
AI-assisted Evidence Research
Share Evidence Blueprint
Search Google Scholar