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Author(s): Mohammed A. Mamun (corresponding author) [1,2,3]; Firoj Al-Mamun [1,2,3]; Md Emran Hasan [3,4]; Nitai Roy [5]; Moneerah Mohammad ALmerab [6]; Mohammad Muhit [2]; Mst. Sabrina Moonajilin [1] 1. Introduction [...], Background: Diabetes management poses significant challenges worldwide, with individuals often facing increased risks of mental health issues, including suicidal behaviors. While traditional statistical methods have been used to assess risk factors, this study introduces machine learning techniques to predict suicidal behaviors among diabetic patients. Therefore, this study aims to investigate the prevalence and risk factors of suicidal behaviors among individuals with diabetes in Bangladesh using both traditional statistical methods and machine learning models. Methods: A cross-sectional study was carried out with 390 diabetic patients, assessing demographic characteristics, diabetes-related variables, mental health indicators, and suicidal behaviors. Statistical analyses including chi-square tests and logistic regression, and various machine learning models, were employed to examine associations and predict risk factors. Results: This study found that 13.8% of the individuals with diabetes reported lifetime suicidal behavior and 9.0% reported past-year suicidal behavior. Significant risk factors associated with higher rates of suicidal behaviors included younger age, marital status, lower monthly income, higher educational attainment, being insulin users, not having a family history of diabetes, and suffering from anxiety. Among the machine learning models tested, the CatBoost model outperformed others, obtaining low log loss values of 0.45 and 0.32 for lifetime and past-year suicidal behaviors, respectively. CatBoost demonstrated excellent accuracy rates of 83.97% and 88.46%, significantly higher than those of traditional logistic regression models. The most significant predictors identified by machine learning were treatment type, followed by occupation, anxiety, age group, and having chronic diseases. Conclusion: The findings suggest the urgent need for comprehensive interventions and support systems for individuals managing diabetes, particularly those at higher risk of suicidal behaviors. Machine learning offers improved predictive power and the ability to identify complex patterns in risk factors. Collaborative efforts between healthcare providers, mental health professionals, and public health initiatives are essential for prioritizing mental well-being in diabetes management.

While literature maintained a strong association between emotion regulation skills and suicidal behavior, relatively less is known as to what extent this relationship goes through different dimensions of perceived social support. The purpose of the present study is to examine the possible mediator role of divergent aspects of perceived social support (family, friends, and significant other) in the relationship between difficulties in emotion regulation and suicide tendency. Conveniently selected 641 college students (430 female, 211 male) living in the different regions of Turkey constituted the sample of the present study. The findings indicated that perceived family, friends, and significant other support mediated the relationship between difficulties in emotion regulation and suicide tendency, with perceived family support being the strongest mediator. The entire model explained 51% of the variance in suicide tendency among college students. Regarding suicide tendency, males reported significantly higher scores than females. Our findings underscore that although difficulties in emotion regulation posed a significant risk factor for suicide tendency, perceived social support from family, friends, and significant other remained resilience factors that highlight the need to promote university students' social support systems., Author(s): Berkan Demir [sup.1] , Zeynep Hatipoglu Sümer [sup.2] Author Affiliations: (1) grid.449350.f, 0000 0004 0369 647X, Department of Educational Sciences, Bartin University, , Kutlubeyyazicilar, 74100, Bartin, Turkey (2) grid.6935.9, [...]

Suicidal thoughts and behaviors (STB) and emergency department (ED) utilization are prevalent in autistic youth. The current study surveyed clinicians in a pediatric psychiatric ED to examine differences in attitudes on suicide-related care for autistic and non-autistic patient populations. While clinicians rated addressing STB in ASD as important and adaptations to care as necessary, less than half identified ASD as a suicide risk factor and confidence ratings were significantly lower for autistic patients. Previous ASD training predicted confidence and accounted for approximately 25% of the variance in confidence scores. Findings highlight the urgency to develop and disseminate ED clinician training, and address the lack of validated assessment tools, adapted suicide prevention practices, and evidence-based treatments for STB in autistic youth., Author(s): Paige E. Cervantes [sup.1] , Annie Li [sup.1] [sup.2] , Katherine A. Sullivan [sup.1] , Dana E. M. Seag [sup.1] , Argelinda Baroni [sup.1] [sup.2] , Sarah M. Horwitz [...]

Neuropsychiatric disorders regularly occur following brain injury and are often diagnosed within a year of the injury. (1,2) Mood disorders, particularly major depressive disorder (MDD), are the most frequently diagnosed [...]

PURPOSE Over 95% of patients who screen positive on the Patient Health Questionnaire-9 (PHQ-9) suicide risk item do not attempt or die by suicide, which could lead to unnecessary treatment and/or misallocation of limited resources. The present study seeks to determine if suicide risk screening can be meaningfully improved to identify the highest-risk patients. METHODS Patients eligible to receive medical treatment from the US Department of Defense medical system were recruited from 6 military primary care clinics located at 5 military installations around the United States. Patients completed self-report measures including the PHQ-9 and 16 items from the Suicide Cognitions Scale (SCS) during routine primary care clinic visits. Postbaseline suicidal behaviors (suicide attempts, interrupted attempts, and aborted attempts) were assessed by evaluators who were blind to screening results using the Self-Injurious Thoughts and Behaviors Interview. RESULTS Among 2,744 patients, 13 (0.5%) engaged in suicidal behavior in the 30 days after screening and 28 (1.0%) displayed suicidal behavior in the 90 days after screening. Multiple SCS items differentiated patients with suicidal behavior less than 30 days after screening positive for suicide risk. Augmenting the PHQ-9 suicide risk item with SCS items improved the identification of patients who were most likely to have suicidal behavior within a month of screening positive without sacrificing sensitivity. CONCLUSION Among primary care patients who screen positive for suicide risk on the PHQ-9, SCS items improved screening efficiency by identifying those patients who are most likely to engage in suicidal behavior within the next 30 days. Key words: primary health care; mass screening, suicide, INTRODUCTION From 1999 to 2017, the US suicide rate increased by more than 33%. (1) Suicides have increased at an even faster rate among military personnel and veterans. (2) Nearly [...]