Psychological Characteristics of Problem Gamblers with and without Mood Disorder
Bibliographic record
Abstract
OBJECTIVE: Problem and pathological gamblers are significantly more likely to experience mood disorders, compared with the general population. Our study examined the relation of psychological characteristics (personality, trait impulsiveness, and gambling motives) to current co-occurring mood disorder (major depression and dysthymia) status among problem and pathological gamblers. METHOD: Problem and pathological gamblers (N = 150) underwent a clinical interview to assess current co-occurring mood disorders; participants completed measures of problem gambling severity, personality, impulsiveness, and gambling motives. RESULTS: Problem and pathological gamblers with a current co-occurring mood disorder were more likely to be female, older, and to report higher lifetime and past-year gambling severity. A co-occurring mood disorder was associated with higher personality scores for alienation and stress reaction, lower scores for well-being, social closeness, and control, as well as higher impulsiveness scores for urgency and lack of premeditation, and lower sensation seeking scores. Participants with a co-occurring mood disorder also reported higher coping motives for gambling. Multivariate logistic regression analyses demonstrated that personality factors (lower social closeness and higher alienation) contributed to the greatest likelihood of being diagnosed with a co-occurring mood disorder. CONCLUSIONS: Mood disorders frequently co-occur with problem and pathological gambling, and they are associated with greater gambling severity. These findings highlight that interpersonal facets of personality contribute substantially to co-occurring mood disorder status. Implications for treatment will be discussed.
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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) | 0.003 | 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".