Treating Attachment Injured Couples With Emotionally Focused Therapy: A Case Study Approach
Bibliographic record
Abstract
This paper compared the attachment injury resolution process in two distressed couples undergoing ten sessions of Emotional Focused Therapy (EFT), a short-term empirically validated treatment for relational distress. An attachment injury is a newly coined clinical construct that denotes a specific type of betrayal within the couple's relationship. The incident is so potent that it calls into question assumptions about the safety of the relationship. The task analytic method was used to examine the pathways of change as related to attachment injury of each couple. Several outcome and process measures were employed in order to differentiate the therapeutic process between the resolved versus non-resolved couple. Results indicated that the couple who resolved their identified attachment injury at the outset of therapy adhered to the attachment injury resolution model, while the non-resolved couple showed marked deviations from the expected pathways of change. Findings suggest that the resolved couple tended to show more differentiation of interactional positions and greater levels of experiencing throughout the therapeutic process in relation to the non-resolved couple. It is recommended that further research is necessary to examine the clinical utility of the attachment injury resolution model in the context of a larger number of case studies.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| 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".