Sports-betting-related gambling disorder: Clinical features and correlates of cognitive behavioral therapy outcomes
Bibliographic record
Abstract
BACKGROUND AND AIMS: The number of patients with gambling disorder (GD) whose gambling preference is sports betting is increasing. However, their clinical profile and their responses to psychological treatments -compared to patients with other forms of gambling- have not been thoroughly studied. Therefore, the aims of this study were: (1) to compare the clinical characteristics of GD patients whose primary gambling activity was sports betting (SB+; n = 113) with GD patients with other primary gambling activities (SB-; n = 1,135); (2) to compare treatment outcomes (dropout and relapses) between SB + and SB- patients; and (3) to explore relationships between specific variables (GD severity, psychological distress and personality features) and treatment outcome in SB + and SB- GD patients, through correlation models and path-analysis. METHODS: The cognitive behavioral treatment consisted of 16 weekly sessions. Personality features, psychopathology, and sociodemographic and clinical factors were assessed. RESULTS: The SB + group included higher proportions of younger patients who were single and had higher educational levels, older ages of GD onset, and greater GD severities. Regarding treatment outcomes, the dropout rate was lower in the SB + group, and no between-group differences were found regarding relapse. Dropout within the SB + group was related to being unemployed, and relapse was related to being unmarried and experiencing more psychological distress. DISCUSSION AND CONCLUSION: The differences between SB + and SB- GD patients suggest that GD patients with sports-betting problems may benefit from tailored therapeutic approaches.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".