Gambling Harms: A Dominance Analysis of Cognitions, Motivation and Impulsivity
Bibliographic record
Abstract
Explanatory models of substance and behavioral addictions typically emphasize the contributions of three predictor domains: distorted cognitions related to control; motivations related to rewards and stress-reduction; and, failure to regulate emotions. In the present study, 271 (161 females) patrons at a racetrack-slots facility completed a survey comprising standardized measures of gambling-related cognitions, motivations for gambling, trait impulsivity, and problem gambling severity. The purpose was to explore dominance analysis as a statistical procedure to identify the relative importance of the three domains as predictors of the experience of gambling harms. The first step of the analysis isolated the dominant facet within each of the three multi-dimensional domains. The final step computed relative dominance among those facets. The results indicated that the most dominant predictor was the cognition of an inability to stop gambling. Motivation to avoid life stressors was the second most dominant predictor followed by the tendency to act rashly in the presence of negative emotion (negative urgency). The relative dominance of the predictors of gambling harm may provide a framework for scaffolding interventions directed at mitigating gambling harms.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".