Demographic and psychiatric correlates of compulsive sexual behaviors in gambling disorder
Bibliographic record
Abstract
Background and aims Gambling disorder (GD) and compulsive sexual behavior (CSB) may commonly co-occur. Yet, the psychiatric correlates of these co-occurring disorders are an untapped area of empirical scrutiny, limiting our understanding of appropriate treatment modalities for this dual-diagnosed population. This study examined the demographic and clinical correlates of CSB in a sample of treatment-seeking individuals with GD (N = 368) in São Paulo, Brazil. Methods Psychiatrists and psychologists conducted semi-structured clinical interviews to identify rates of CSB and other comorbid psychiatric disorders. The Shorter PROMIS Questionnaire was administered to assess additional addictive behaviors. The TCI and BIS-11 were used to assess facets of personality. Demographic and gambling variables were also assessed. Results Of the total sample, 24 (6.5%) met diagnostic criteria for comorbid CSB (GD + CSB). Compared to those without compulsive sexual behaviors (GD − CSB), individuals with GD + CSB were more likely to be younger and male. No differences in gambling involvement emerged. Individuals with GD + CSB tended to have higher rates of psychiatric disorders (depression, post-traumatic stress disorder, and bulimia nervosa) and engage in more addictive behaviors (problematic alcohol use, drug use, and exercise) compared to GD − CSB. Those with GD + CSB evidenced less self-directedness, cooperativeness, self-transcendence, and greater motor impulsivity. Logistic regression showed that the predictors of GD + CSB, which remained in the final model, were being male, a diagnosis of bulimia, greater gambling severity, and less self-transcendence. Discussion and conclusion Given those with GD + CSB evidence greater psychopathology, greater attention should be allocated to this often under studied comorbid condition to ensure adequate treatment opportunities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".