The Experience of Gambling and its Role in Problem Gambling
Bibliographic record
Abstract
This paper reports on the results of a psychological study conducted in Ontario, Canada, that attempted to answer the question of why some people develop gambling problems while others do not. A group of social gamblers (n = 38), sub-clinical problem gamblers (n = 33) and pathological gamblers (n = 34) completed a battery of questionnaires. Compared to non-problem gamblers, pathological gamblers were more likely to report experiencing big wins early in their gambling career, stressful life events, impulsivity, depression, using escape to cope with stress and a poorer understanding of random events. We grouped these variables into three risk factors: cognitive/experiential, emotional and impulsive and tested the extent to which each risk factor could differentiate non-problem and pathological gamblers. Each risk factor correctly identified about three-quarters of the pathological gamblers. More than half (53%) of the pathological gamblers had elevated scores on all three risk factors. Interestingly, 60% of the sub-clinical cases had elevated scores on only one risk factor. The results are interpreted in terms of a bio-psycho-social model of gambling addiction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".