Subtyping pathological gamblers on the basis of affective motivations for gambling: Relations to gambling problems, drinking problems, and affective motivations for drinking.
Bibliographic record
Abstract
Pathological gamblers who drink when gambling (n=158; 77% men; mean age=36.0 years) completed the Inventory of Gambling Situations (IGS) and gambling and drinking criterion measures. Principal components analysis on the IGS subscales revealed negative (e.g., Unpleasant Emotions) and positive (e.g., Pleasant Emotions) gambling situation factors. Subjecting IGS factor scores to cluster analysis revealed three clusters: (a) enhancement gamblers, with low negative and high positive factor scores; (b) coping gamblers, with very high negative and high positive factor scores; and (c) low emotion regulation gamblers, with low negative and positive factor scores (59%, 23%, and 18% of the sample, respectively). Clusters were validated with a direct measure of gambling motives. Additional validity analyses showed that coping gamblers scored higher than the other groups on a variety of different gambling activities, gambling problems, drinking frequency, drinking problems, and coping drinking motives, whereas low emotion regulation gamblers scored lower than the other groups on gambling frequency, gambling problems, drinking quantity, and enhancement drinking motives. The findings validate this empirical approach to subtyping gamblers and suggest consistency of motives across addictive behaviors.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".