The joint role of impulsivity and distorted cognitions in recreational and problem gambling: A cluster analytic approach
Bibliographic record
Abstract
BACKGROUND AND AIMS: The Pathways Model (Blaszczynski & Nower, 2002) posits that problem gambling is a heterogeneous disorder with distinct subgroups (behaviorally conditioned gamblers, emotionally vulnerable gamblers, and antisocial-impulsivist gamblers). Impulsivity traits and gambling-related cognitions are recognized as two key psychological factors in the onset and maintenance of problem gambling. To date, these constructs have been explored separately, and their joint role in determining problem gambling subtypes has received little attention. The goal of our study was to identify subgroups of gamblers based on impulsivity traits and gambling-related cognitions, and to determine whether this approach is consistent with the Pathways model. METHODS: Gamblers from the community (N = 709) and treatment-seeking pathological gamblers (N = 122) completed questionnaires measuring gambling habits, disordered gambling symptoms, gambling-related cognitions, and impulsivity traits. RESULTS: Cluster analyses revealed that three clusters globally aligned with the pathways proposed by Blaszczynski & Nower (2002). Two other clusters emerged: (1) impulsive gamblers without cognitive-related cognitions; and (2) gamblers without impulsivity or gambling-related cognitions. Gamblers with both heightened impulsive traits and gambling-related cognitions had more severe problem gambling symptoms. CONCLUSION: We successfully identified, based on an a priori theoretical framework, different subtypes of gamblers that varied in terms of problem gambling symptoms and clinical status. The diversity of the cluster profiles supports the development of personalized prevention strategies and psychological interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".