Individuals from communities characterized by high crime rates, violence, poverty, and social disadvantages are of greater risk to develop PTSD. Due to high comorbidity rates between PTSD and...Show moreIndividuals from communities characterized by high crime rates, violence, poverty, and social disadvantages are of greater risk to develop PTSD. Due to high comorbidity rates between PTSD and borderline personality disorder (BPD), recently, complex PTSD was introduced. Complex PTSD contains, next to PTSD symptoms, the additional symptoms as disturbances in self-organization (DSO) and negative alterations in cognitions and mood (NACM). However, it remains unclear how symptoms of complex PTSD, PTSD, and BPD are related in an at-risk, urban sample. The present study explored the relations between PTSD, BPD, DSO symptoms and NACMs using a network approach. The symptoms were assessed using semi-structured clinical interviews. Participants (N = 470; 98.1% female; 97.7% African-American) were recruited by Powers et al. (2022) from medical clinics within urban areas in the USA. Two network analysis were estimated using EBICglasso model to create regularized partial correlation networks. The first to explore the overall structure of PTSD, BPD, and complex PTSD, the second to investigate the relatedness of the NACM and DSO symptoms. The results were in line with previous studies and indicated that the NACM symptoms play a crucial role in the PTSD structure, in connecting PTSD with the DSOs. BPD and DSO symptoms were related via emotional dysregulation. Of the specific NACM symptoms, trauma-related amnesia was more related to BPD than to PTSD, and DSO symptom. This suggests BPD and PTSD to be distinct, complex PTSD to be phenomenologically related to both and the NACM and DSO symptoms to be associated with each other.Show less
In recent years the use of network analysis has seen an increase across multiple fields. Using features derived from network analysis in a random forest model has already shown promising results in...Show moreIn recent years the use of network analysis has seen an increase across multiple fields. Using features derived from network analysis in a random forest model has already shown promising results in an experiment of the Netherlands Labour Inspection to predict risk scores of violating Dutch labour laws for Dutch companies. In order to see if this is the best way for the NLA to use network analysis this method has been compared with using node classification, a deep learning method that uses sampling and aggregation to label nodes of a network based on its neighbours and the network structure. In this study node classification has shown to perform better at predicting risk scores for Dutch companies than a random forest model with network features does. A simulation study was done on the node classification method to test its robustness and has shown that it is important that there are enough labels in the training set in order for the method to perform well and that the quality of these labels influences the extent to which the model overfits. If the data scientist of the NLA choose to use node classification in future projects it is important that they make an ethical selection of the variables to use for prediction and that they ensure that there are enough labels in the dataset for the node classification model to perform well.Show less
The literature on risk and protective factors for depression focuses on biological, demographic, social-environmental, and psychological factors. Estimating a network model, this thesis project...Show moreThe literature on risk and protective factors for depression focuses on biological, demographic, social-environmental, and psychological factors. Estimating a network model, this thesis project explores how dynamic psychological risk and protective factors for depression interact and determines which factors are more central to a network of these factors (Research Question 1). It also tests if dynamic risk and protective coping factors relate to current depressive symptoms, as prior studies suggest (Research Question 2). Cross-sectional data from 453 students at a Dutch higher education participating in the WARN-D research project were analyzed. Overall, protective factors clustered together, as risk factors did. The strongest positive associations emerged between Seeking Distraction and Ignoring and between Locus of Control and Optimism. The strongest negative relations merged between Seeking Social Support and Ignoring, Resilience and Intolerance of Uncertainty, and Catastrophizing. Self-efficacy, Resilience, and Self-esteem were the most central features of the network. The results did not support the hypothesis that all the included risk and protective factors are related to current depressive symptoms. Only some were, with the strongest positive associations being between current depressive symptoms and Persistent Thinking and Optimism. Despite the limitations of the present work, these findings highlight the importance of further research on risk and protective factors for depression.Show less
