Demographic, psychiatric, and personality correlates of adults seeking treatment for disordered gambling with a comorbid binge/purge type eating disorder
Bibliographic record
Abstract
Preliminary evidence suggests that binge/purge type eating disorders and gambling disorder may commonly co-occur. However, this dual-diagnosis population remains understudied. The present research examined the prevalence rates and correlates of binge/purge type eating disorders (i.e., bulimia nervosa, binge-eating disorder, and anorexia nervosa binge/purge type) among adults seeking treatment for their gambling (N = 349). In total, 11.5% of the sample (n = 40) met criteria for a binge/purge type eating disorder, most commonly bulimia nervosa (n = 33). There was a higher preponderance of binge/purge type eating disorders in women. People with a comorbid binge/purge type eating disorder reported more days gambling, gambling-related cognitive distortions, impulsivity, suicidality, and other current psychiatric comorbidities including addictive behaviours. These findings suggest that binge/purge type eating disorders in people seeking treatment for gambling may be more common than previously believed. Furthermore, the increased psychopathology among people with binge/purge type eating disorder and gambling disorder identify vulnerabilities of this dual-diagnosed population that may require clinical attention.
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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".