Resilient relationships: Role of partner responsiveness and relationship satisfaction in posttraumatic stress disorder
Bibliographic record
Abstract
Introduction: Research examining relationship quality among patients with posttraumatic stress disorder (PTSD) largely focuses on the negative impact PTSD can have on patients' romantic relationships, with less attention devoted to factors that build resilience in these relationships. Methods: In a sample of patients undergoing treatment for PTSD (N = 89), including 49 Veterans, this study examined 1) two main effects of PTSD symptom severity and perceived partner responsiveness on relationship quality, 2) a moderating model of perceived partner responsiveness, buffering the relationship between posttraumatic stress symptoms and relationship satisfaction, and 3) a mediation model, whereby posttraumatic stress symptoms lead to less perceived partner responsiveness, which in turn leads to poorer relationship satisfaction. Results: A linear regression analysis revealed that posttraumatic stress symptoms were not linked to relationship satisfaction, whereas perceived partner responsiveness showed a positive association with relationship satisfaction. Veterans tended to have lower relationship satisfaction compared with non-Veterans. The moderation and mediation analyses did not show any significant direct or indirect effects. Discussion: Findings suggest that responsive couples can maintain a strong and supportive relationship regardless of PTSD status.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".