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Predictors of dropout in cognitive processing therapy for PTSD: An examination of in-session treatment processes

Behaviour Research and Therapy, ISSN: 0005-7967, Vol: 171, Page: 104428
2023
  • 3
    Citations
  • 0
    Usage
  • 35
    Captures
  • 1
    Mentions
  • 57
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    3
  • Captures
    35
  • Mentions
    1
    • News Mentions
      1
      • 1
  • Social Media
    57
    • Shares, Likes & Comments
      57
      • Facebook
        57

Most Recent News

Studies in the Area of Post-Traumatic Stress Disorders Reported from University of Delaware (Predictors of Dropout In Cognitive Processing Therapy for Ptsd: an Examination of In-session Treatment Processes)

2023 DEC 28 (NewsRx) -- By a News Reporter-Staff News Editor at Mental Health News Daily -- Research findings on Mental Health Diseases and Conditions

Article Description

Dropout rates for treatments for adult posttraumatic stress disorder (PTSD) are high. Process research can reveal client factors during treatment that predict dropout. An observational coding system was used to code client processes in audio-recorded early sessions of cognitive processing therapy (CPT), a gold-standard treatment for PTSD. Data are from a randomized controlled noninferiority trial of CPT and written exposure therapy (WET), with higher rates of dropout in CPT than WET (39.7% vs. 6.4%). Participants in this study were 53 treatment-seeking adults with PTSD who were in the CPT arm of the trial and completed the CAPS-5 at pretreatment and at least one session. Of these, 15 (28.3%) dropped out of CPT early (completing ≤9 sessions) and 38 (71.7%) completed treatment. Sessions were coded with an observational coding system on a four-point scale (0 =  absent to 3 =  high ) for maladaptive trauma-related responses (overgeneralized beliefs, ruminative processing, avoidance), affective engagement (negative emotions, physiological distress), and adaptive processing (cognitive emotional processing). Binary logistic regressions showed that more physiological distress and cognitive emotional processing predicted lower dropout, whereas more avoidance predicted higher dropout. Negative emotion, ruminative processing, and overgeneralization were not significant predictors. These findings highlight potential early indicators of treatment engagement that could be targeted to reduce dropout and perhaps facilitate further therapeutic change.

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