ARQ National Psychotrauma Centre

Objective This study aimed to examine the severity of posttraumatic stress disorder (PTSD) symptoms in children of parents with cancer and to identify individual and family factors associated with these symptoms.

Background:
Text mining and machine learning are increasingly used in mental health care practice and research, potentially saving time and effort in the diagnosis and monitoring of patients. Previous studies showed that mental disorders can be detected based on text, but they focused on screening for a single predefined disorder instead of multiple disorders simultaneously.

Posttraumatic stress disorder (PTSD) is a prevalent disorder worldwide and often co-occurs in dementia. Both have a major impact on disease burden and quality of life. PTSD may be difficult to recognize in dementia and a structured diagnostic method is lacking.

Background Posttraumatic stress disorder (PTSD) is accompanied by disrupted cortical neuroanatomy. We investigated alteration in covariance of structural networks associated with PTSD in regions that demonstrate the case-control differences in cortical thickness (CT) and surface area (SA).

Childhood maltreatment (CM) is linked to impairments in various domains of social functioning. Here, we argue that it is critical to identify factors that underlie impaired social functioning as well as processes that mediate the beneficial health effects of positive relationships in individuals exposed to CM.

Brief eclectic psychotherapy for posttraumatic stress disorder (BEPP), an evidence-based treatment that focuses on working through difficult emotions and grief originating from traumatic events, aims not only to reduce symptoms but also to enable trauma survivors to learn from devastating experiences. This chapter describes BEPP and unique characteristics that distinguish it from other evidence-based treatments that are highly relevant in the treatment of complex posttraumatic stress disorders (CTSDs).

BACKGROUND

A significant increase in the number of individuals suffering from prolonged grief disorder is expected in the aftermath of the COVID-19 pandemic for two main reasons. First, the number of excess deaths has contributed to an immense number of bereaved individuals. Second, recent literature has shown that circumstances associated with COVID-19 deaths may be contributing to increased risk for the development of prolonged grief disorder.

OBJECTIVE

Introduction

The necessary requirement of a traumatic event preceding the development of Posttraumatic Stress Disorder, theoretically allows for administering preventive and early interventions in the early aftermath of such events. Machine learning models including biomedical data to forecast PTSD outcome after trauma are highly promising for detection of individuals most in need of such interventions.

Background:

First responders to disasters are at risk of developing post-traumatic stress disorder (PTSD). The trajectories of post-traumatic stress symptom severity differ among individuals, even ifthey are exposed to similar events. These trajectories have notyet been reported in non-Western first responders.

Aims: