Disordered gambling among higher-frequency gamblers: who is at risk?
Bibliographic record
Abstract
BACKGROUND: When gambling opportunities are made available to the public in a given jurisdiction, some individuals participate occasionally and others more frequently. Among frequent gamblers, some individuals develop problematic involvement and some do not. This study addresses the association among demographic and social risk factors, frequency of gambling and gambling disorders. METHOD: Data from an adult community sample (n=1372) were used to identify risk factors for higher-frequency gambling and disordered gambling involvement. RESULTS: Individuals with higher intelligence, older individuals and more religious individuals were less frequent gamblers. Males, single individuals and those exposed to gambling environments (friends and family who gamble) and those who started to gamble at a younger age were more frequent gamblers. Excitement-seeking personality traits were also higher among more frequent gamblers. A different set of risk factors was associated with the likelihood of gambling disorder among these higher-frequency gamblers. These variables included mental health indicators, childhood maltreatment and parental gambling involvement. Among higher-frequency gamblers, individuals who smoke cigarettes, those with a diagnosis of alcohol or drug dependence or obsessive-compulsive disorder, those with higher anxiety or depression and those with higher impulsivity and antisocial personality traits were more likely to report gambling-related problems. These individuals were also more likely to report gambling on electronic gambling machines (e.g. slot machines). CONCLUSIONS: These data suggest a model in which higher-frequency gambling, particularly with electronic gambling machines, when combined with any type of emotional vulnerability increased the likelihood of gambling disorder.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.003 |
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; both teacher heads agree on what is shown here.
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".