Problem gambling: the mediating role of impulsivity and cognitive bias
Bibliographic record
Abstract
Previous research has suggested that endorsement of erroneous gambling beliefs is positively associated with gambling intensity and severity (Xian et al., 2008). Likewise, higher levels of impulsivity have also been associated with increasingly severe problem gambling (Steel & Blaszczynski, 1998). This study examined whether impulsivity and cognitive bias were associated with pathological gambling, and if so, which best explained the relationship between gambling risk status and gambling behaviors. A sample of 80 undergraduate students from the University of Manitoba completed a number of measures assessing impulsivity, cognitive bias, gambling behavior, and gambling play. Results showed that probable pathological gamblers (N=40) scored higher in impulsivity (F (5, 74), p < .005) and cognitive bias (F (4, 75) = 11.94, p < .001) than non-pathological gamblers (N=40). A series of mediation models suggested that the effects of gambling group on some EGM play variables are mediated by cognitive bias, but not impulsivity. Moderated mediation models found that impulsivity moderates the mediating effect of cognitive bias on the relationship between gambling group and EGM play. These results support the treatment of erroneous gambling cognitions with pathological gamblers while it also gives support to the recent reclassification of Pathological Gambling as an "addiction and related disorder" in the DSM-V.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".