Are There Cognitive and Behavioural Approaches Specific to the Treatment of Pathological Gambling?
Bibliographic record
Abstract
OBJECTIVE: Treatment approaches for pathological gambling have been modelled after preexisting substance addiction models. This paper reviews cognitive-behavioural models in a search for original insights that are specific to gambling treatment. METHOD: A computerized search of major health care databases (Medline and PsycINFO) was performed. RESULTS: New cognitive-behavioural approaches to the treatment of pathological gambling provide 3 original additions to the traditional multimodal treatment of addictions: cognitive restructuring, in vivo exposure, and imaginal desensitization. Other cognitive-behavioural techniques, such as relapse prevention, problem solving, and social skill training, are shared by gambling treatment and addictions treatment. CONCLUSIONS: When treating pathological gamblers, clinicians must consider introducing techniques to address cognitive distortions related to gambling. Also, cue exposure--whether in vivo or imaginal--may help deal with urges prompted by such cues. The blending of these new techniques into a multimodal addiction treatment potentially balances the rational and external orientation of the cognitive-behavioural approach with interpersonal and introspective components of the traditional addiction treatment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".