Assessing Randomised Clinical Trials of Cognitive and Exposure Therapies for Gambling Disorders: A Systematic Review
Bibliographic record
Abstract
Aims: Problem or pathological gambling is associated with significant disruption to the individual, family and community with a range of adverse outcomes, including legal, financial and mental health impairment. It occurs more frequently in younger populations, and comorbid conditions are common. Cognitive–behaviour therapy (CBT) is the most empirically established class of treatments for problematic gambling. This article reports on a systematic review and evaluation of randomised clinical trials (RCTs) concerning two core techniques of CBT: cognitive and behavioural (exposure-based) therapies. Methods: PsycINFO, MEDLINE and the Cochrane library were searched from database inception to December 2012. The CONsolidated Standards Of Reporting Trials (CONSORT) for non-pharmacological treatments was used to evaluate each study. Results: The initial search identified 104 references. After two screening phases, seven RCTs evaluating either cognitive ( n = 3), exposure ( n = 3) or both ( n = 1) interventions remained. The studies were published between 1983 and 2003 and conducted across Australia, Canada, and Spain. On average, approximately 31% of CONSORT items were rated as ‘absent’ for each study and more than 52% rated as ‘present with some limitations’. For all studies, 70.83% of items rated as ‘absent’ were in the methods section. Conclusions: The findings from this review of randomised clinical trials involving cognitive and exposure-based treatments for gambling disorders show that the current evidence base is limited. Trials with low risk of bias are needed to be reported before recommendations are given on their effectiveness and clinicians can appraise their potential utility with confidence.
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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 | 0.084 | 0.225 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.025 | 0.018 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".