Comorbid addictive behaviors in disordered gamblers with psychosis
Bibliographic record
Abstract
OBJECTIVE: While it has been shown that disordered gamblers with psychosis are at increased risk for comorbid psychopathology, it is unclear whether this dual-diagnosis population is also at greater risk of problematic engagement with comorbid addictive behaviors. METHODS: We tested for association between disordered gambling with psychosis and comorbid addictive behaviors in a sample of 349 treatment-seeking disordered gamblers. RESULTS: Twenty-five (7.2%) disordered gamblers met criteria for psychosis. Disordered gamblers with psychosis were no more likely to meet diagnostic criteria for current alcohol/substance use disorder than disordered gamblers without psychosis. However, this dual-disorder population reported greater misuse of shopping, food bingeing, caffeine, and prescription drugs. When controlling for multiple comparisons, binge eating was the only addictive behavior to remain significant. CONCLUSION: Given these findings, a comprehensive assessment of addictive behaviors - specifically food bingeing - in this population may be warranted.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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 teacher head, 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".