Exploring Relationships Between Problem Gambling, Scratch Card Gambling, and Individual Differences in Thinking Style
Bibliographic record
Abstract
Background and aims: Scratch cards are a popular form of lottery gambling available in many jurisdictions. However, there is a paucity of research that examines associations between individual differences in thinking style, participation in scratch card gambling, and problem gambling severity. Methods: In three studies, we sought to examine the relationships among these variables in large, online samples of participants. Participants completed the Cognitive \nReflection Test (CRT), the Problem Gambling Severity Index (PGSI), the Actively Open-Minded Thinking Scale, \nand self-reported their frequency of scratch card gambling. Results: Throughout all three studies, specific associations were reliably established. Specifically, negative associations were observed between participants’ CRT and PGSI scores, as well as between participants’ CRT scores and scratch card gambling frequency. In addition, we found a positive association between problem gambling severity and scratch card gambling frequency. Finally, problem \ngambling severity was shown to correlate positively with participants’ willingness to pay for irrelevant information in a scratch card gambling scenario. Discussion and conclusions: Overall, we observed that problem gambling severity is associated with an individuals’ thinking style and scratch card gambling behavior. This study adds to the existing literature examining problem gambling, and highlights the role of thinking style in understanding gambling behavior \nand problematic gambling.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".