Objective: This study aimed to translate the Leahy Emotional Schemas Scale-II (LESS-II) into Arabic and validate its psychometric properties among Iraqi university students. The hypothesis was that the Arabic version would retain the original factor structure and demonstrate robustness and validity.

Method: The process involved translation, cultural adaptation, backward translation, and the bilingual method to ensure linguistic and cultural relevance. The sample consisted of 280 Iraqi university students (64% female, 36% male). Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) were conducted to assess the factor structure. Reliability was tested using Cronbach’s alpha and test-retest methods.

Results: EFA identified a 14-factor structure aligned with Leahy’s model, accounting for 91.83% of the total variance. CFA confirmed a good model fit (RMSEA = 0.08, CFI = 0.93, TLI = 0.90). The total scale’s reliability (Cronbach’s alpha) was 0.82, with test-retest reliability at 0.87. Pearson correlations indicated significant relationships between LESS-II factors, anxiety, and depression, supporting construct validity.

Conclusion: The Arabic LESS-II was established as a valid and reliable tool for assessing emotional schemas. However, the study's reliance on a nonclinical sample limits generalizability. Future research should validate the scale in diverse and clinical populations, highlighting its potential utility in Arabic-speaking contexts for both research and clinical practice.

Objective: Automatic negative thoughts have an important role in development of a persistent negative cognitive bias, which may ultimately result in suicidal ideation. The primary objective of the present study was to examine the relationship between automatic negative thoughts and experiential avoidance in relation to psychological distress.

Method: The study sample comprised 441 individuals who had attempted suicide. Participants underwent interviews utilizing standardized questionnaires including Automatic Thoughts Questionnaire, Kessler Psychological Distress Scale, Acceptance and Action Questionnaire–II, and Cognitive Emotion Regulation Questionnaire along with its nine subscales. After internal relationships assessment among the research variables, outlier detection was done using the boxplot analysis and standard deviation distance metrics. To analyze the direct and indirect associations between the input and output variables, Structural Equation Modeling (SEM) was employed. In addition, SPSS-28 and Amos 29 software were used to analyzed the data.

Results: The final model showed that automatic negative thoughts were significantly inversely associated with adaptive cognitive emotion regulation (β = -0.42, P ≤ 0.01) and significantly positively related to both maladaptive cognitive emotion regulation (β = 0.49, P ≤ 0.01) and psychological distress (β = 0.53, P < 0.01). Additionally, experiential avoidance showed a significant positive relationship with maladaptive cognitive emotion regulation (β = 0.22, P < 0.01).

Conclusion: This research demonstrated that automatic negative thoughts could worsen psychological distress through the regulation of cognitive emotion in those who had a history of suicide. By the clinical management of automatic negative thoughts and shifting individuals’ cognitive emotion regulation toward adaptive strategies, there is potential for a substantial reduction in suicidal ideation and attempts which can be evaluated in future clinical trials.

Objective: The Personality Inventory for DSM-5 (PID-5) is a widely used scale to evaluate the dimensional constructs of two trait models proposed by the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) and the International Classification of Diseases (ICD-11). The present meta-analysis first aimed to examine the factor structure, reliability, and congruence coefficients of the Persian version of the PID-5 to assess both trait models. The second aim was to evaluate the factor structure and reliability of the Persian version of the Personality Inventory for DSM-5-Brief Form (PID-5-BF).

Method: A systematic search was conducted in PubMed, Magiran, and SID to find records in English and Farsi from January 2013 to December 2023. According to the PRISMA, data from nine medium- to high-quality reports including 7,608 participants were analyzed using the random-effects method. Quality of studies, heterogeneity, and publication bias were reported.

Results: The five-factor structure of the PID-5 to measure both trait models was supported by the pooled estimates of factor loadings. The alpha coefficient median for the DSM-5 model was 0.83 (range: 0.82-0.90), and the congruence coefficient median was .91 (range: 0.80-0.97). The ICD-11 alpha median was .78 (range: 0.68-0.91), and congruency median was 0.90 (range: 0.71-0.96). The factor loadings for negative affectivity, detachment, antagonism, disinhibition, and psychoticism on the PID-5-BF were 0.44-0.69, 0.38-0.67, 0.46-0.72, 0.42-0.70, and 0.44-0.76, respectively, and the alpha median was 0.73 (range: 0.65-0.76).

Conclusion: Since both the original and brief versions of the PID-5 are valid and strongly similar to international structures, the clinical and research applications of these questionnaires are recommended to mental health professionals in Iran.

Objective: The integration of behavioral economics and neuroeconomics into mental health offers innovative perspectives on understanding and addressing psychological disorders. This overview aims to synthesize current knowledge and explore the implications of these interdisciplinary approaches in the context of mental health.

Method: In this narrative review, we summarized the current evidence regarding the applications of behavioral economics and neuroeconomics approaches in the field of mental health.

Results: Behavioral economics and neuroeconomics provide valuable insights into the cognitive and emotional processes underlying mental health disorders, such as irrational decision-making, impulsivity, and self-control issues. Concepts such as loss aversion, temporal discounting, and framing effects inform the development of innovative interventions and policy initiatives. Behavioral economic interventions, including nudges, incentives, and commitment devices, show promise in promoting treatment adherence, reducing risky behaviors, and enhancing mental well-being. Neuroeconomics contributes by identifying neural markers predictive of treatment response and relapse risk, paving the way for personalized treatment approaches.

Conclusion: The integration of behavioral economics and neuroeconomics into mental health research and practice holds significant potential for improving the understanding of psychological disorders and developing more effective, personalized interventions. Further research is needed to elucidate the mechanisms of action, optimize intervention strategies, and address ethical considerations associated with these approaches in mental health settings.