Betting to deal: coping motives mediate the relationship between urgency and Problem gambling severity
Bibliographic record
Abstract
Background and aims: Elevated impulsivity traits, particularly negative and positive urgency, have been identified as robust predictors of problem gambling. However, the mechanisms that link urgency to problem gambling remain unknown. The present research examined whether self-reported gambling motives (social, coping, enhancement, financial) mediated the association between urgency and problem gambling severity.Methods: The sample consisted of 564 community gamblers (52.1% female; M age =36.10, SD = 11.25) including 156 (27.7%) who were classified as moderate risk gamblers and 141 (25%) as problem gamblers.Results: The mediation analyzes revealed that both negative and positive urgency were associated with problem gambling severity. Coping was the only motive that mediated these associations. The pattern of results remained the same when the analyzes were restricted to problem gamblers and when controlling for days gambled and money spent in the past 30 days.Conclusions: The desire to alleviate strong emotional states (negative or positive) maybe an important determinant of problem gambling. Furthermore, results indicate that treatment initiatives for problem gambling may benefit from including training for clients in alternative and more adaptive coping strategies for the effective management of intense affective states.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".