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Record W40055348

Predicting treatment outcome in chemically dependent women: A test of Marlatt and Gordon's Relapse Model.

2000· article· en· W40055348 on OpenAlexaboutno aff

Bibliographic record

VenueScholarship at UWindsor (University of Windsor) · 2000
Typearticle
Languageen
FieldMathematics
TopicAdvanced Causal Inference Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsOutcome (game theory)Test (biology)Internal medicineComputer scienceOncologyMedicineMathematicsBiologyMathematical economics
DOInot available

Abstract

fetched live from OpenAlex

The purpose of the present study was to test and expand Marlatt and Gordon's (1985) Relapse Model using a sample of substance-abusing women. Marlatt and Gordon hypothesized that coping skills, positive expectancies, and self-efficacy would predict post-treatment substance use. They also hypothesized that individuals who experience the abstinence violation effect following an initial lapse would be at increased risk of further substance use. Variables representing issues relevant to women were added to the original model. Specifically, poly-drug addiction and experiences with physical and sexual abuse were hypothesized to be important in any relapse model applied to women. Questionnaires were administered to 98 chemically dependent women in treatment centres across Ontario one week within their discharge date, and one, two, and three months after leaving treatment. This study found some support for Marlatt and Gordon's Relapse Model. Self-efficacy was the strongest predictor of an initial, post-treatment lapse. Participants with lower self-efficacy were at greater risk for a lapse. In addition self-efficacy mediated the relationship between coping and relapse. Participants with poorer coping skills also had lower self-efficacy which placed them at increased risk of relapse. All but one woman who relapsed reported experiencing multiple lapses, therefore the second part of Marlatt and Gordon's model could not be tested. When a liberal level of significance was used, participants who had been physically abused as children were found to be more likely to relapse, but this relationship was also mediated by self-efficacy. Finally, there was a significant interaction between expectancies and poly-drug use. Poly-drug users with higher expectancies were more likely to relapse whereas single drug users with lower expectancies were more likely to relapse. The present study found that women's confidence in their ability to remain abstinent after treatment (i.e., self-efficacy) is a key predictor of post-treatment substance use. Confidence was affected by factors such as childhood victimization and ability to cope with cravings. Consequently, treatment programs should focus on increasing self-efficacy by teaching clients coping skills and addressing issues relevant to women, such as victimization. In addition, more research is needed to explore how other issues (e.g., family of origin, motivation, etc.) affect the recovery process in chemically dependent women.Dept. of Psychology. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2000 .C67. Source: Dissertation Abstracts International, Volume: 62-10, Section: B, page: 4777. Adviser: Charlene Senn. Thesis (Ph.D.)--University of Windsor (Canada), 2000.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.388
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.067
GPT teacher head0.307
Teacher spread0.240 · 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 designObservational
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

Citations4
Published2000
Admission routes1
Has abstractyes

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