The moderating effect of gender on the relationship between coping and suicide risk in a Portuguese community sample of adults

Background and aims(s): The study evaluated the contribution of coping strategies, based on the Toulousiane conceptualization of coping, to the prediction of suicide risk and tested the moderating effect of gender, controlling for depressive symptoms. Method: A two-time data collection design was used. A community sample of 195 adults (91 men and 104 women) ranging in age from 19 to 65 years and living in several Portuguese regions, mostly in Alentejo, participated in this research. Results: Gender, depressive symptoms, control, and withdrawal and conversion significantly predicted suicide risk and gender interacted with control, withdrawal and conversion, and social distraction in the prediction of suicide risk. Coping predicted suicide risk only for women. Conclusions: Results have important implications for assessment and intervention with suicide at-risk individuals. In particular,the evaluation and development of coping skills is indicated as a goal for therapists having suicide at-risk women as clients.

Self-report depressive symptoms do not directly predict suicidality in nonclinical individuals: Contributions toward a more psychosocial approach to suicide risk

Although suicidality is associated with mental illness in general and depression in particular, many depressed individuals do not attempt suicide and some individuals who attempt to or do die by suicide do not present depressive symptoms. This article aims to contribute to a more psychosocial approach to understanding suicide risk in nonclinical populations. In advocating a psychosocial perspective rather than a depression-focused approach, this article presents four diverse studies that demonstrate sampling and measurement invariance in findings across different populations and specific measures. Study 1 tests the mediation effects of 2 interpersonal variables, thwarted belongingness and perceived burdensomeness, in the association between depressive symptoms and recent suicidality. Studies 2 and 3 evaluate the contribution of hopelessness and psychache, beyond depressive symptoms, to suicidality. Study 4 tests the contribution of life events behind depressive symptoms, and other relevant sociodemographic and clinical variables, to the estimation of “future suicidality.” Overall, results demonstrate that depressive symptoms do not directly predict suicidality in nonclinical individuals, but that other psychosocial variables mediate the association between depressive symptoms and suicidality or predict suicidality when statistically controlling for depressive symptoms. The article contributes to understanding some of the nonpsychopathological factors that potentially link depressive symptoms to suicide risk and that might themselves contribute to suicidality, even when controlling for depressive symptoms.

An Integrated Soft Computing Approach to Hughes Syndrome Risk Assessment

The AntiPhospholipid Syndrome (APS) is an acquired autoimmune disorder induced by high levels of antiphospholipid antibodies that cause arterial and veins thrombosis, as well as pregnancy-related complications and morbidity, as clinical manifestations. This autoimmune hypercoagulable state, usually known as Hughes syndrome, has severe consequences for the patients, being one of the main causes of thrombotic disorders and death. Therefore, it is required to be preventive; being aware of how probable is to have that kind of syndrome. Despite the updated of antiphospholipid syndrome classification, the diagnosis remains difficult to establish. Additional research on clinically relevant antibodies and standardization of their quantification are required in order to improve the antiphospholipid syndrome risk assessment. Thus, this work will focus on the development of a diagnosis decision support system in terms of a formal agenda built on a Logic Programming approach to knowledge representation and reasoning, complemented with a computational framework based on Artificial Neural Networks. The proposed model allows for improving the diagnosis, classifying properly the patients that really presented this pathology (sensitivity higher than 85%), as well as classifying the absence of APS (specificity close to 95%).