Changes in Social Adjustment With Cognitive Processing Therapy: Effects of Treatment and Association With PTSD Symptom Change
Bibliographic record
Abstract
The current study sought to determine if different spheres of social adjustment, social and leisure, family, and work and income improved immediately following a course of cognitive processing therapy (CPT) when compared with those on a waiting list in a sample of 46 U.S. veterans diagnosed with posttraumatic stress disorder (PTSD). We also sought to determine whether changes in different PTSD symptom clusters were associated with changes in these spheres of social adjustment. Overall social adjustment, extended family relationships, and housework completion significantly improved in the CPT versus waiting-list condition, η(2) = .08 to .11. Hierarchical multiple regression analyses revealed that improvements in total clinician-rated PTSD symptoms were associated with improvements in overall social and housework adjustment. When changes in reexperiencing, avoidance, emotional numbing, and hyperarousal were all in the model accounting for changes in total social adjustment, improvements in emotional numbing symptoms were associated with improvements in overall social, extended family, and housework adjustment (β = .38 to .55). In addition, improvements in avoidance symptoms were associated with improvements in housework adjustment (β = .30), but associated with declines in extended family adjustment (β = -.34). Results suggest that it is important to consider the extent to which PTSD treatments effectively reduce specific types of symptoms, particularly emotional numbing and avoidance, to generally improve social adjustment.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".