Specifying the Effects of an Online, Self-Help Couples' Intervention on PTSD Clusters and the Influence of Improvements in Relationship Satisfaction
Bibliographic record
Abstract
Couple HOPES (Helping Overcome PTSD and Enhance Satisfaction; CH) is an online dyadic intervention for individuals with posttraumatic stress disorder (PTSD) and their partners. Initial analyses provide support for the efficacy of CH in improving general PTSD symptoms and relationship satisfaction, but it is unclear which symptom clusters of PTSD are improving (i.e., intrusions, avoidance, cognitions and mood, and/or arousal). Moreover, there is a potent association between PTSD symptoms and relationship distress, such that improvements in relationship satisfaction are associated with improvements in PTSD symptoms. However, it is unclear whether this is true in CH, and if so, for which clusters. This information is pertinent to identify when relationship satisfaction requires direct targeting to promote recovery from PTSD symptoms. The current study was a secondary data analysis of the CH case series and uncontrolled trial (N = 27 dyads) and had two aims: (1) to identify which clusters of PTSD are impacted by CH, and (2) to examine whether changes in relationship satisfaction was associated with changes in PTSD clusters. Hierarchical multilevel modelling revealed that CH led to improvements in intrusions, cognitions and mood, and arousal symptom clusters, but not in the avoidance cluster. Avoidance symptoms did improve when changes in relationship satisfaction were moderate to high. Changes in relationship satisfaction were not associated with changes in intrusions, cognitions and mood, or arousal. This study suggests that CH effectively targets intrusion, cognition and mood, and arousal symptoms, but changes in avoidance symptoms are dependent on changes in relationship satisfaction.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".