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Record W2110936444 · doi:10.1080/10550887.2016.1107315

Motivation to quit or reduce gambling: Associations between Self-Determination Theory and the Transtheoretical Model of Change

2015· article· en· W2110936444 on OpenAlexaff
Vladyslav Kushnir, Alexandra Godinho, David C. Hodgins, Christian S. Hendershot, John Cunningham

Bibliographic record

VenueJournal of Addictive Diseases · 2015
Typearticle
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsUniversity of TorontoUniversity of CalgaryCentre for Addiction and Mental Health
Fundersnot available
KeywordsTranstheoretical modelPsychologyAddictionSelf-determination theoryBehavior changeSocial psychologyDevelopmental psychologyPsychiatryAutonomy

Abstract

fetched live from OpenAlex

Motivation for change and recovery from addiction has been commonly assessed using the Transtheoretical Model's stages of change. Analogous to readiness for change, this measure of motivation may not recognize other elements of motivation relevant to successful change. The aim of this study was to examine the relationship between stages of change and reasons for change according to the Self-Determination Theory among problem gamblers motivated to quit. Motivations for change were examined for 200 adult problem gamblers with intent to quit in the next 6 months (contemplation stage) or 30 days (preparation stage). Analyses revealed that higher autonomous motivation for quitting gambling predicted greater likelihood of being in the preparation stage, whereas those with higher external motivation for change were less likely to be farther along the stage of change continuum. The findings suggest that autonomous motivations relate to readiness for quitting gambling, and may predict successful resolution from problem gambling.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.307
GPT teacher head0.442
Teacher spread0.135 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations40
Published2015
Admission routes1
Has abstractyes

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