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Record W4410135617 · doi:10.1007/978-3-031-82583-5_21

Couple Treatment for Addiction

2025· book-chapter· en· W4410135617 on OpenAlexaff
Marianne Saint-Jacques, Joël Tremblay, Myriam Beaulieu, Mélissa Côté, Magali Dufour, Karine Bertrand, Francine Ferland, Nadine Blanchette-Martin, Paul S. Greenman, Louise Nadeau

Bibliographic record

VenueSustainable development goals series · 2025
Typebook-chapter
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsUniversité du Québec en OutaouaisCentre Intégré de Santé et Services Sociaux de Chaudière-AppalacheUniversité de SherbrookeUniversité du Québec à MontréalUniversité de MontréalUniversité LavalUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsAddictionPsychologyMedicinePsychiatry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.929
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.019
GPT teacher head0.332
Teacher spread0.313 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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