Therapeutic Alliance with Perpetrators of Intimate Partner Violence: The Roles of Childhood Interpersonal Trauma and Attachment Insecurity
Bibliographic record
Abstract
Intimate partner violence (IPV) represents a major global health concern with devastating consequences. Many perpetrators seek treatment to stop their violent behaviors, where a strong therapeutic alliance (TA) could support their progress. This study’s first aim was to examine how individual or cumulative childhood interpersonal trauma (e.g. sexual abuse, neglect) and attachment insecurity (anxiety, avoidance) reported by IPV perpetrators predict the quality of TA reported by their therapist. The second aim was to assess whether childhood interpersonal trauma interacts with attachment insecurity in predicting TA. The third aim was to explore whether the number of therapy sessions moderates these associations. A sample of 349 men entering treatment for IPV completed admission questionnaires and their therapists assessed the quality of TA at the end of treatment. Results revealed that childhood interpersonal trauma and attachment insecurity did not predict TA directly, but that TA was worse when men reported childhood physical neglect with high attachment anxiety. The number of sessions moderated the links between TA and physical abuse, exposure to parental psychological IPV, and attachment (anxiety and avoidance). By documenting predictors of TA among IPV perpetrators, this study highlights the importance of trauma history, attachment insecurity, and of promoting longer treatment duration.
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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 | 0.012 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| 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".