A preliminary examination of the effects of pretreatment relationship satisfaction on treatment outcomes in cognitive-behavioral conjoint therapy for PTSD.
Bibliographic record
Abstract
OBJECTIVE: The primary goal of the present study was to investigate whether pre-treatment relationship satisfaction predicted treatment drop-out and posttraumatic stress disorder (PTSD) symptom outcomes within a trial of cognitive-behavioral conjoint therapy (CBCT) for PTSD (Monson & Fredman, 2012). Additionally, we examined the influence of pre-treatment relationship distress on relationship outcomes. METHOD: Thirty-seven patients and their intimate partners who participated in a course of CBCT for PTSD were assessed for PTSD symptoms with the Clinician-Administered PTSD Scale and PTSD Checklist, and for intimate relationship functioning with the Dyadic Adjustment Scale. CBCT for PTSD is a conjoint therapy designed to improve PTSD symptoms and enhance relationship functioning. Patients had to meet diagnostic criteria for PTSD to be included in the study; however, couples were not required to be in distressed relationships to receive treatment. RESULTS: = 1.02). CONCLUSIONS: Among patients receiving CBCT for PTSD, treatment drop-out and improvements in PTSD symptoms may be independent of pre-treatment relationship functioning, whereas improvements in relational functioning may be greater among those distressed prior to treatment.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".