Assessing and Treating Problem Gambling: Empirical Status and Promising Trends
Bibliographic record
Abstract
OBJECTIVE: Ways to clinically assess and treat problem gambling evolve as our knowledge about this disorder increases. This paper summarizes current knowledge about treating problem gambling and describes developments in the assessment, psychology, and biology of problem gambling that may be important for treatment. METHODS: We reviewed recent published literature reporting advances in the assessment, psychology, and biology of problem gambling. We retained for review only controlled clinical trials in which subjects were randomized to either psychological or pharmacologic treatment. RESULTS: Although several gambling treatments were found to be efficacious, support for any specific treatment modality is still limited. Cognitive-behavioural treatments were most effective. Although diagnostic assessment has improved, there are still very few measures of gambling-related variables. The contribution to gambling of sex, concurrent psychiatric disorders, cognitive distortions, and impulsivity has been described. Evidence implicating decision-making areas of the cortex and disturbances in serotonin and dopamine functioning has been reviewed. Available evidence for a genetic contribution to problem gambling is weak. CONCLUSIONS: Improvements in the methodology of gambling-treatment research were discussed to advance the clinical approach to this disorder. Developments in the area of assessment, psychology, and biology of gambling should inform clinical approaches to a greater degree than they currently do. We identified the need to study different types of gambling separately, rather than combining them, as an important goal.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".