Assessing the Relationship between Disordered Gamblers with Psychosis and Increased Gambling Severity: The Mediating Role of Impulsivity
Bibliographic record
Abstract
OBJECTIVE: Recent research suggests that disordered gambling and psychosis co-occur at higher rates than expected in the general population. Gamblers with psychosis also report greater psychological distress and increased gambling severity. However, the mechanism by which psychosis leads to greater gambling symptomology remains unknown. The objective of the present research was to test whether impulsivity mediated the relationship between comorbid psychosis and gambling severity. METHOD: The sample consisted of 394 disordered gamblers voluntarily seeking treatment at a large university hospital in São Paulo, Brazil. A semistructured clinical interview (Mini-International Neuropsychiatric Interview) was used to diagnosis the presence of psychosis by registered psychiatrists. Severity of gambling symptoms was assessed using the Gambling Symptom Assessment Scale, and the Barratt Impulsiveness Scale-11 provided a measure of impulsivity. RESULTS: Of the sample, 7.2% met diagnostic criteria for psychosis. Individuals with a dual diagnosis of psychosis did not report greater gambling severity. Conversely, dual diagnoses of psychosis were associated with greater levels of impulsivity. Higher levels of impulsivity were also associated with greater gambling severity. Importantly, support for our hypothesised mediation model was found such that impulsivity mediated the association between disordered gambling and psychosis and gambling severity. CONCLUSION: Impulsivity appears to be a transdiagnostic process that may be targeted in treatment among disordered gamblers with a dual diagnosis of psychosis to reduce problematic gambling behaviours.
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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 | 0.002 | 0.007 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".