Gambling Severity, Impulsivity, and Psychopathology: Comparison of Treatment‐ and Community‐Recruited Pathological Gamblers
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Because most studies of pathological gambling gather data from participants recruited from treatment, this study compared community and treatment-enrolled pathological gamblers (PGs) with respect to demographics, gambling severity, impulsivity, and psychopathology. METHODS: One hundred six PGs were recruited as part of two larger studies in Farmington, Connecticut (n= 61) and Windsor, Ontario (n= 45) using radio advertising, word of mouth, and/or newspaper ads, as well as a gambling treatment program at each location. RESULTS: Community (n= 49) and treatment-enrolled (n= 57) PGs did not differ on age, education, gender, race, employment, or marital status. Treatment-enrolled PGs were more likely to report past year illegal behaviors, preoccupation with gambling, and higher scores on the Barratt Impulsiveness Scale (BIS) Attention Impulsivity subscale. Assessment of psychopathology in the Ontario study indicated that treatment-enrolled PGs were more likely to present with Major Depressive and Dysthymic Disorders. Community-recruited PGs in the Connecticut study were overall more likely to present with any substance use disorder relative to their treatment-enrolled counterparts. CONCLUSIONS AND SCIENTIFIC SIGNIFICANCE: Our findings inform intervention and research within the field of pathological gambling. Specifically, the distressing aspects of pathological gambling, such as legal issues, preoccupation with gambling, and depression, may be present more in treatment-enrolled PGs than in those recruited from the community. Such emotional disturbances should be further explored to increase motivation and treatment adherence in PGs. In addition, due to relative absence of overall differences between the groups, research findings utilizing treatment-enrolled PGs may be a good representation of both groups.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".