A Comparison of Gambling Behavior, Problem Gambling Indices, and Reasons for Gambling Among Smokers and Nonsmokers Who Gamble: Evidence from a Provincial Gambling Prevalence Study
Bibliographic record
Abstract
INTRODUCTION: Numerous epidemiological and clinical studies have found that tobacco use and gambling frequently cooccur. Despite high rates of smoking among regular gamblers, the extent to which tobacco potentially influences gambling behavior and vice versa is poorly understood. The current study aimed to provide more insight into this relationship by directly comparing nonsmoking and smoking gamblers on gambling behavior, problem gambling indices, and reasons for gambling. METHODS: The data for this study came from the 2005 Newfoundland and Labrador Gambling Prevalence Study. Gamblers identified as nonsmokers (N = 997) were compared with gamblers who smoke (N = 622) on numerous gambling-related variables. Chi-square analyses were used to compare groups on demographic variables. Associations between smoking status and gambling criteria were assessed with a series of binary logistic regressions. RESULTS: The regression analyses revealed several significant associations between smoking status and past 12-month gambling. Higher problem gambling severity scores, use of alcohol/drugs while gambling, amount of money spent gambling, use of video lottery terminals, and reasons for gambling which focused on positive reinforcement/reward and negative reinforcement/relief were all associated with smoking. CONCLUSIONS: The findings suggest an association between smoking and potentially problematic gambling in a population-based sample. More research focused on the potential reinforcing properties of tobacco on the development and treatment of problematic gambling is needed.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".