Personality Disorders and Pathological Gambling: A Review and Re-Examination of Prevalence Rates
Bibliographic record
Abstract
The current study reviews and reexamines the association between pathological gambling and personality disorders (PDs). To date, the majority of investigations have examined the prevalence of PDs in a single group of treatment-seeking pathological gamblers (PGs); very few of these studies included a comparison group, and even fewer compared PGs to nonpathological gamblers who, in contrast to nongamblers, resemble PGs in their attraction to and engagement in gambling behavior. The current study included a sample composed of nontreatment-seeking pathological gamblers and a comparison group of nonpathological gamblers (NPGs); these participants completed a self-report instrument (SCID-II/PQ) and were administered a structured clinical interview SCID-II) designed to assess PDs. Compared to the SCID-II, the SCIDII/PQ produced significantly higher PD prevalence rate estimates and symptom endorsements. Although the pattern of specific PD prevalence and symptom endorsement varied somewhat across the instruments, PGs consistently displayed significantly higher levels of borderline PD than NPGs; this pattern endured even after controlling for Axis I disorders and overlap among Axis II PDs.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".