Risk Tolerance, Impulsivity, and Self-esteem: Differences and Similarities between Gamblers and Non-Gamblers in a Pilot Study
Bibliographic record
Abstract
Risk-taking ranges from socially beneficial entrepreneurship through games of chance as entertainment to problem gambling that can be both individually and socially destructive. There are many conflicting theories about what leads individuals to become gamblers, although some consensus suggests a link to personality traits. Links between gambling and impulsivity, risk tolerance and self-esteem remain unclear, and childhood experiences may be pertinent. To explore these issues, we studied 41 non-gamblers and compared them to 16 individuals identified as frequent gamblers in which both groups completed a psychological battery. The study goal was to try and determine which measures best relate to high propensities towards gambling, particularly with regards to different domains of risk tolerance. In this small sample, the results show the gamblers to have statistically significantly greater financial, recreational and social risk tolerance, as well as higher impulsivity and more favorable attitudes towards gambling overall. In contrast, there were no statistically significant differences in self-esteem and adverse childhood experiences. Multivariate models reveal three measures of risk tolerance to significantly contribute to gambling propensity. While differences in impulsivity exist to some degree, those for self-esteem and adverse childhood experiences were less important. This preliminary research suggests that risk tolerance may be a key psychological determinant in gamblers, but this relatively small study does not support previous suggestions that impulsivity, low self-esteem, or adverse childhood experiences are as important. Repeated studies with larger samples may help further clarify these findings
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".