The dishonest gambler: Low HEXACO honesty–humility and gambling severity in a community sample of gamblers
Bibliographic record
Abstract
Personality dimensions have been found to be important in understanding the aetiology of disordered gambling. While the majority of research has focused on the Five-Factor Model of personality, recent empirical evidence also indicated that the honesty-humility factor of the HEXACO personality model may be a key personality correlate of gambling behaviour. In the present research, we extend the understanding between personality and gambling severity by further assessing whether HEXACO dimensions are associated with both current gambling status and gambling severity in a community-recruited sample of gamblers (N = 427). In addition, we examined whether motivations to engage in gambling (enhancement, coping, social and financial) mediated the relationship between personality and gambling severity on the Problem Gambling Severity Index. Demographic covariates were controlled for in our analyses. The results indicate that honesty-humility was the only personality dimension that significantly predicted gambling status (non-gamblers vs. current gamblers). In addition, lower scores on honesty-humility, conscientiousness and openness were significantly associated with gambling severity. Lastly, coping motives were the only significant mediator in the relationship between honesty-humility and increased gambling severity. The results offer further support to the notion that honesty-humility may be an especially pertinent personality dimension in understanding the aetiology of disordered gambling. © 2018 John Wiley & Sons, Ltd.
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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 | 0.002 |
| 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 | 0.000 |
| 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".