Greater impulsivity is associated with a reduced propensity to cash out of bets
Bibliographic record
Abstract
A common feature of contemporary sports-betting apps is ‘instant cash-out’, which allows users to settle a bet early in exchange for a discounted immediate payout. Despite high prevalence and links with gambling-related harm, relatively little is known about how personality traits associated with gambling, such as impulsivity, predict instant cash-out usage. To address this question, we recruited 145 general-population adult participants (69 men, 66 women, 10 non-binary or undisclosed; Mage = 36.3, SD = 10.7; participants resided in Australia, Canada, Ireland, New Zealand, the UK, or the USA) to complete five self-report questionnaires related to impulsivity, as well as the Problem Gambling Severity Index (PGSI), and a validated cognitive task measuring individual differences in cash-out frequency. We then assessed how cash-out frequency in the behavioral task was associated with both self-reported impulsivity and PGSI. We found that cash-out frequency was negatively correlated both with PGSI scores and with a number of impulsivity-related traits including Dysfunctional Impulsivity, Lack of Premeditation, Positive Urgency, Sensation Seeking, and Fun Seeking. An exploratory factor analysis revealed that higher scores on a latent ‘Dysfunctional Impulsivity’ factor were negatively associated with cash-out frequency overall, whereas higher scores on an ‘Inhibition and Inflexibility’ factor predicted higher cash-out frequency specifically for bets with a low win probability. Taken together, results suggest that instant cash-out may primarily appeal to less impulsive people and those with lower PGSI scores. This raises the possibility that instant cash-out may specifically facilitate increased gambling behaviors among people with less prior experience of 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 | 0.000 | 0.003 |
| 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".