Problem gambling among women in Canada
Bibliographic record
Abstract
Background: Large-scale growth of the gambling industry was most notable in Canada in the 1990s.The expansion of gambling has been identified as an important public health "or.."rn.'-3Clinical data has indicated that large proportions of gambling help- seekers in Canada are women.t-6To fuither our knowledge of women and problem gambling, a conceptual framework was developed for this research using a population health model.T Methods: Data used for the analysis were from the nationally representative Canadian Community Health Survey Cycle 1.2 (CCHS L2; n: 20,211 women aged 15 years and older; data collected in 2002).The statistical analysis included logistic regression, multinomial regression, and linear regression models.Results: The l2-month plevalence of at-risk gambling and problern gambling among women was 1 1.01% and I.35o/o, respectively.Being aged 40 to 49 years, a household income of less than $50,000, a high school education or less, being never-married, leportirig life stress, and using negative coping skills were signifìcantly associated with increased odds of problem garnbling among women.Endorsement of higher levels of social support was associated with decreased odds of problem gambling.Tlie types of gambling associated with the highest odds of problem garnbling were VLTs outside a casino, VLTs inside a casino, and other casino games.In unadjusted models, problern gambling was associated with a significantly higher probability of lower self-perceived general health, suicidal ideation and attempts, decreased psychological well-being, distress, depression, mania, panic attacks, social phobia, agoraphobia, alcohol dependence, any psychiatric disorder, psychiatric comorbidity, chronic blonchitis, fibromyalgia, migraine headaches, help-seeking from a professional, attending a self-help 111 grolrp, and calling a telephone help line.In models adjusting for covariates, only the relationships between problem gambling and distress, panic attacks, agoraphobi a, any psychiatric disorder, fibromyalgia, and calling a telephone help line remained statistically significant.Cottclusions.'Frequent VLT gambling outside and inside casinos is associated with the largest odds of problern garnbling, which highlights an area of gambling in Canad a that needs to be reassessed if problern gambling is to be prevented or reduced.Findings fiom the current research have important research and policy irnplications that could inform a whole population approach.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".