Utilizing Retrospective Accounts of Primary Symptom-Clusters to Predict PTSD over Time in Women Survivors of Domestic or Sexual Assault

dc.contributor.authorSullivan, Connor Patricken
dc.contributor.committeechairJones, Russell T.en
dc.contributor.committeememberAxsom, Danny K.en
dc.contributor.committeememberHarrison, David W.en
dc.contributor.committeememberWhite, Bradley A.en
dc.contributor.departmentPsychologyen
dc.date.accessioned2019-09-17T08:02:06Zen
dc.date.available2019-09-17T08:02:06Zen
dc.date.issued2019-09-16en
dc.description.abstractThe extant theories in PTSD describe significant initial symptom reactions, and these reactions may provide opportunities for clearer early identification and treatment of PTSD. There are empirically identified trajectories of PTSD, which indicates there is a critical starting point to those trajectories. Generally, theories and results suggest that the re-experiencing (Cluster B) and hyperarousal (Cluster E) symptoms are common reactions after traumatic events, while hyperarousal and negative cognitions and mood (Cluster D) clusters are generally identified as the most important and/or predictive. Thus, this dissertation utilized retrospective reports in order to identify initial symptom reactions and then subsequently predict PTSD severity over time. Participants included college women who experienced sexual and relationship violence within the past 2 years. Two primary hypotheses were investigated within the dissertation: 1) Cluster B and E symptoms were expected to be the most prevalent initial reactions reported, and 2) Clusters E and D were expected to significantly predict PTSD severity over time. The results indicated partial support for each hypothesis, such that Cluster B symptoms were among the most prevalent initial reactions and Cluster D was a significant predictor of PTSD severity over time. Specifically, earlier Cluster D ordering interacted with the presence of negative beliefs and loss of positive emotions to predict PTSD severity over time.en
dc.description.abstractgeneralThere are ideas and theories about how posttraumatic stress disorder (PTSD) starts and gets worse. People develop PTSD in different ways; some develop it very quickly and it is very bad, while others develop it slowly and it may not affect them much at all. The first signs and symptoms may be the best place to look, much like when you first get a cough or a sore throat with a cold or the flu. Generally, research suggests that common reactions are re-living the trauma and having reactions like being on guard all the time. Being on guard all the time also may be one of those important symptoms that will help predict if someone will get PTSD, as well as experiencing things such as thinking harsh things about oneself. This dissertation included reports from people after they had experienced trauma in order to figure out those first symptoms. Then, it used those first symptoms to predict how bad their PTSD was in the weeks and months later. Participants included college women who experienced sexual assault and domestic violence within the past 2 years. The results showed that people often re-live the trauma, but it may not be the most important when predicting whether they will get PTSD or not. Negative thoughts and beliefs about oneself were the most important set of reactions when predicting who will get PTSD and how badly. More importantly, the earlier they had those negative thoughts, the worse their PTSD was in the coming weeks and months.en
dc.description.degreeDoctor of Philosophyen
dc.format.mediumETDen
dc.identifier.othervt_gsexam:19899en
dc.identifier.urihttp://hdl.handle.net/10919/93727en
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectPTSDen
dc.subjecttraumaen
dc.subjectsexual violenceen
dc.titleUtilizing Retrospective Accounts of Primary Symptom-Clusters to Predict PTSD over Time in Women Survivors of Domestic or Sexual Assaulten
dc.typeDissertationen
thesis.degree.disciplinePsychologyen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.leveldoctoralen
thesis.degree.nameDoctor of Philosophyen

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