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Record W2022176671 · doi:10.1037/0893-164x.17.4.284

Treatment of Pathological Gambling: A Critical Review of the Literature.

2003· review· en· W2022176671 on OpenAlexaff
Tony Toneatto, Robert Ladoceur

Bibliographic record

VenuePsychology of Addictive Behaviors · 2003
Typereview
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsCentre for Addiction and Mental Health
Fundersnot available
KeywordsPsychologyPathologicalClinical psychologyLegalizationCognitionInclusion (mineral)Gambling disorderPsychiatryAddictionSocial psychologyMedicine

Abstract

fetched live from OpenAlex

The legalization and availability of new forms of gambling are increasing in most Western countries. This trend has contributed to the fact that more individuals are developing gambling problems. As a result, there is a need for effective treatments. Although gambling treatment dates several decades, few empirically supported treatments for pathological gambling have been developed. This critical review includes only controlled treatment studies. The primary inclusion criterion was randomization of participants to an experimental group and to at least 1 control group. Eleven studies were identified and evaluated. Key findings showed that cognitive-behavioral studies received the best empirical support. Recommendations to improve gambling treatment research include better validated psychometric measures, inclusion of process measures, better definition of outcomes, and more precise definition of treatments.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.004
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.268
GPT teacher head0.523
Teacher spread0.255 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations251
Published2003
Admission routes1
Has abstractyes

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