Patterns of change in physical functioning and posttraumatic stress disorder with cognitive processing therapy in a randomized controlled implementation trial
Bibliographic record
Abstract
Background: Physical health concerns (e.g. chronic pain, fatigue) are common among clients with posttraumatic stress disorder (PTSD). Prior research has indicated that clients report improved physical functioning and fewer physical health symptoms after receiving Cognitive Processing Therapy (CPT) for PTSD. However, less is known about the impact of physical functioning on the clients’ PTSD symptom improvement in CPT.Objective: The current study examined the patterns of change of and between physical functioning and PTSD symptoms over the course of CPT among a diverse military, veteran, and community sample.Method: We collected clients’ (N = 188) physical functioning and PTSD symptom severity prior to and during CPT using the 12-Item Short Form Health Survey and the PTSD Checklist. We used multilevel modelling to 1) evaluate the impact of baseline physical functioning on the PTSD symptom trajectory, 2) examine the trajectory of physical functioning, and 3) assess the dynamics between physical functioning and PTSD symptoms over the course of CPT.Results: Our multilevel analyses indicated that 1) physical functioning significantly improved for those with low levels of functioning prior to treatment, 2) poorer baseline physical functioning predicted slower improvements in PTSD symptoms, and 3) poorer physical functioning in one session predicted less PTSD symptom improvement by the next session.Conclusions: Our findings demonstrate that while physical functioning can interfere with PTSD symptom improvement, physical functioning can also improve over the course of CPT. In light of the interconnected nature of physical health and PTSD symptoms, clinicians may need to attend to lower levels of physical functioning when providing CPT or other trauma-focused therapies. Future research to determine whether specific treatment adaptations may benefit such clients is needed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".