The association of at-risk, problem, and pathological gambling with substance use, depression, and arrest history
Bibliographic record
Abstract
We examined at-risk, problem, or pathological gambling co-occurrence with frequency of past-year alcohol, tobacco, and marijuana use; depressive symptoms; and arrest history. Data included the responses of over 3,000 individuals who participated in a 2006 telephone survey designed to understand the extent of at-risk, problem, and pathological gambling; comorbidity levels with substance use; mental health; and social problems among Southwestern U.S. residents. Data were analyzed with multinomial and bivariate logistic regression. Respondents at risk for problem gambling were more likely to use alcohol, tobacco, and marijuana than those respondents not at risk. Pathological gamblers were no more or less likely to consume alcohol or tobacco than were non-gamblers or those not at risk. A dose-response relationship existed between degree of gambling problems and depressive symptoms and arrest history. Interventions for at-risk or problem gamblers need to include substance use treatment, and the phenomenon of low levels of substance use among pathological gamblers needs further exploration.
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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) | 0.000 | 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 | 0.001 |
| 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".