Couple Treatment for Addiction
Bibliographic record
Abstract
Abstract Problematic substance/gambling use has major harmful effects on both members of a couple and on their relationship. It is also exacerbated by couple distress. However, for a person with an addiction (PA), the desire to maintain the relationship and the partner’s support are strong sources of motivation and reinforcement for change and recovery. Although research has shown for decades that couple therapy is an effective way to treat addiction, services are focused largely on individual treatments (Ariss and Fairbairn, Consult Clin Psychol 88:526–40, 2020). The first part of this chapter reviews the literature to provide a brief description of the interactions between problematic use and couple relationships and of effective models of couple treatment for addiction, along with their limitations. The second part of the chapter delves more deeply into Integrative Couple Treatment for Addiction, an approach developed by the authors of this chapter. It focuses on assessment and intervention strategies designed for integrating partners of persons with an addiction (PPA) into couple treatment. Specific issues of addiction treatment with couples and observations on the obstacles to disseminating and implementing couple treatments in real-life settings are briefly addressed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.003 |
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".