A Decision Tree Approach To The Assessment Of Posttraumatic Stress Disorder

Structured diagnostic interviews are widely considered to be the optimal method of assessing symptoms of posttraumatic stress; however few clinicians report using structured assessments to guide clinical practice. One key impediment to the use of structured assessments in clinical practice is the amount of time required for test administration and interpretation. Thus, the present research conducted an initial feasibility study using a normative sample of college-aged adults (n = 88) to develop an assessment protocol based on the clinician administered PTSD scale (caps). Decision tree analysis was utilized to identify a subset of predictor variables within the 17 caps symptom criteria variables that were most predictive of a diagnosis of posttraumatic stress disorder (PTSD). The algorithm-driven sequence of questions reduced the number of items administered by more than 75% and classified the validation sample at 100.0% accuracy for those without a diagnosis of PTSD and 85.7% accuracy for those with a diagnosis of PTSD. The present study also demonstrated the feasibility of computer administration of the algorithm-based sequence in a normative sample of college-aged adults (n = 197). The algorithm-based, computer-administered sequence had high sensitivity and specificity and excellent diagnostic agreement with the computer-administered full caps sequence. These results demonstrated the feasibility of developing a protocol to assess PTSD in a way that imposes little assessment burden while still providing a reliable diagnosis.

Name thesis:degree_name

Ph.D. in Psychology

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Psychology

Year dc:date.available

2015

Author dc:creator

• Stewart, Regan

Contributors dc:contributor

• John Young
• Kathy Knight
• Danielle J. Maack

• Clinical Psychology

Repository record dc:identifier

https://egrove.olemiss.edu/etd/1132

OAI identifier oai:identifier

oai:egrove.olemiss.edu:etd-2131