Attachment Injury Severity, Injury-related Stress, Forgiveness, and Sexual Satisfaction in Injured Adult Partners
Bibliographic record
Abstract
An attachment injury can occur when one partner violates the assumption that they will provide comfort and caring during a moment of increased need. For injured partners, unresolved attachment injuries can underlie an enduring stress reaction and lower relationship satisfaction. However, no research has examined the associations between the perceived severity of the injury and sexual satisfaction, a central component of relationship well-being. In this cross-sectional study, we examined the direct and indirect associations between the perceived severity of the attachment injury and sexual satisfaction via injury-related stress symptoms and levels of forgiveness, in injured partners. A total of 145 adults who reported having experienced an attachment injury in their current relationship completed self-report questionnaires measuring injury severity, event-related stress, forgiveness, and sexual satisfaction. An indirect association between the perceived severity of the attachment injury and sexual satisfaction through higher injury-related stress and lower forgiveness was found via a path analysis. Results suggest that fostering forgiveness and attending to injury-related stress may be key toward sexual satisfaction in couples where a partner reports an attachment injury. Clinical implications of these results are discussed in light of theory and potential treatment strategies for addressing an attachment injury in couple's therapy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".