Associations between the HEXACO model of personality and gambling involvement, motivations to gamble, and gambling severity in young adult gamblers
Bibliographic record
Abstract
Background and aims Substantial research has examined the role of personality in disordered gambling. The predominant model in this work has been the five-factor model (FFM) of personality. In this study, we examined the personality correlates of gambling engagement and gambling severity using a six-dimensional framework known as the HEXACO model of personality, which incorporates FFM characteristics with the addition of honesty-humility. In addition, the potential mediating role of gambling motives in the personality and gambling severity relationship was explored. Methods A sample of undergraduate gamblers (n = 183) and non-gamblers (n = 143) completed self-report measures of the Problem Gambling Severity Index (PGSI) and the Gambling Motives Questionnaire-Financial, as well as self- and observer report forms of the HEXACO-100. Results Logistic regression results revealed that scores on honesty-humility were positively associated with non-gambling over gambling status. Furthermore, it was also found that honesty-humility, agreeableness, and conscientiousness were each uniquely associated with PGSI severity scores. The results of the mediational analyses suggest that each personality factor has different gambling motivational paths leading to PGSI gambling severity. Discussion and conclusions The findings of this study contribute to the literature on behavioral addictions by providing an increased understanding of individual personality factors associated with likelihood of gambling, overall gambling severity, and gambling motives. Ultimately, these findings suggest that the honesty-humility dimension may be a target for the prevention efforts against problematic gambling outcomes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".