Despite 30 years of research advancements, PTSD treatment remains a trial-and-error process as 22 veterans per day commit suicide to relieve their symptoms. Foa and Kozak's emotional processing theory informed this correlational study which included secondary data consisting of participants' self-rated scale scores to examine whether the independent variables number of deployments, guilt, depression, and anxiety predicted the dependent variable PTSD symptom reduction in a veteran sample with combat deployments and associated PTSD symptoms who completed accelerated resolution therapy (ART). An analysis of whether mean PTSD symptom reduction amounts differed by symptom severity levels was also completed. The study aimed to identify the first predictive treatment-matching model for PTSD symptom reduction by use of ART. A multiple regression analysis to determine whether the predictor variables predicted PTSD symptom reduction by use of ART resulted in nonsignificant findings (p = .517). A Welch ANOVA test to determine if mean PTSD symptom reduction differed among the low, moderate, and high PTSD symptom severity groups showed significant results (p = .002). Games-Howell post hoc analysis showed that mean differences in PTSD symptom reduction from the low to high PTSD symptom severity group was significant (p = .001) with a 26.1 point mean reduction for the high symptom severity group and a greater than 10-point mean PTSD symptom reduction for the low and moderate symptom severity groups. The findings confirmed a need for treatment-matching algorithm studies to predict which PTSD interventions most benefit veterans suffering with PTSD to reduce trial-and-error treatment approaches, associated comorbidities, and high rates of suicides.
| Identifer | oai:union.ndltd.org:waldenu.edu/oai:scholarworks.waldenu.edu:dissertations-7667 |
| Date | 01 January 2019 |
| Creators | Witt, Ann |
| Publisher | ScholarWorks |
| Source Sets | Walden University |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Walden Dissertations and Doctoral Studies |
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