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Record W4282842211 · doi:10.1111/jmft.12596

Integrative couple treatment for pathological gamblers with an emphasis on forgiveness processes: A case study with three couples

2022· article· en· W4282842211 on OpenAlexafffund
Mélissa Côté, Magali Dufour, Joël Tremblay

Bibliographic record

VenueJournal of Marital and Family Therapy · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicMarriage and Sexual Relationships
Canadian institutionsUniversité du Québec à MontréalConcordia UniversityUniversité du Québec à Trois-RivièresCentre intégré de santé et de services sociaux de Chaudière-Appalaches
FundersFonds de Recherche du Québec-Société et Culture
KeywordsForgivenessRelevance (law)PsychologyElement (criminal law)PathologicalSocial psychologyPsychotherapistMedicinePolitical science

Abstract

fetched live from OpenAlex

Gambling's impact on a couple's relationship is an essential element in the gambling disorder (GD). Gamblers tend to lie to their partner to conceal the extent of their gambling problems and debts, which can lead to a serious relational transgression for the couple. One promising avenue is a couple treatment focusing on forgiveness processes. The objective of this study was to determine whether the Integrative Couple Treatment for Pathological Gamblers (ICT-PG) with an emphasis on forgiveness processes helped couples to enhance these processes. A Single-Case Research Design (SCRD) was used with three couples in which one of the members had a GD. The results highlight the importance of jointly analyzing the forgiveness processes between the gamblers and their partner, which constantly influenced each other. These promising results illustrate the relevance of integrating forgiveness processes in treatment for couples where one of the members has a GD.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.546
Threshold uncertainty score0.740

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.118
GPT teacher head0.361
Teacher spread0.243 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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

Citations6
Published2022
Admission routes2
Has abstractyes

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