An Exploratory Study in Gambling Recovery Communities: A Comparison Between ‘‘Pure’’ and Substance-Abusing Gamblers
Bibliographic record
Abstract
Most of the available literature has shown that gambling disorder (GD) is often associated with several psychiatric conditions. Comorbidities with mood disorders, impulsiveness, personality traits, and impairments in cognitive function have also been frequently investigated. However, it is currently uncommon to study this disorder in individuals without comorbid substance abuse; therefore, the primary aim of our study was to compare the psychological profile of individuals with GD with and without substance use disorder. A total of 60 participants (100% male), including 20 individuals with GD, 20 substance-dependent gamblers (SDGs), and 20 healthy controls (HCs), were assessed with several clinical measures to investigate impulsivity, hostility, mood, and personality traits, as well as with cognitive tasks (i.e., decision-making tasks). Our results showed differences in both experimental groups compared with the HC group in mood disorders, impulsivity, and hostility traits. The ‘‘pure’’ GD group differed from the SDG group only in characteristics related to mood disorders (e.g., State-Trait Anxiety Inventory-Y2, Beck Depression Inventory-II, and assault dimension), whereas greater impairment in decision making processes related to risky choices was shown in the SDG group. This study suggests the importance of studying pure GD to clarify the underlying mechanisms without the neurotoxic effects of the substances. This could provide an important contribution to the treatment and understanding of this complex disorder.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".