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Cognitive Treatment of Pathological Gambling

2001· article· en· W1968770177 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Journal of Nervous and Mental Disease · 2001
Typearticle
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsPathologicalPsychologyCognitionPsychological interventionClinical psychologyPerceptionPsychiatryMedicine

Abstract

fetched live from OpenAlex

This study evaluated the efficacy of a cognitive treatment package for pathological gambling. Sixty-six gamblers, meeting DSM-IV criteria for pathological gambling, were randomly assigned to treatment or wait-list control conditions. Cognitive correction techniques were used first to target gamblers' erroneous perceptions about randomness and then to address issues of relapse prevention. The dependent measures used were the South Oaks Gambling Screen, the number of DSM-IV criteria for pathological gambling met by participants, as well as gamblers' perception of control, frequency of gambling, perceived self-efficacy, and desire to gamble. Posttest results indicated highly significant changes in the treatment group on all outcome measures, and analysis of data from 6- and 12-month follow-ups revealed maintenance of therapeutic gains. Recommendations for clinical interventions are discussed, focusing on the cognitive correction of erroneous perceptions toward the notion of randomness.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.217
Threshold uncertainty score0.229

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.147
GPT teacher head0.417
Teacher spread0.270 · 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