Epidemiology of Youth Gambling Problems in Canada: A National Prevalence Study
Bibliographic record
Abstract
OBJECTIVES: To describe the epidemiology of gambling problems among youth aged 15 to 24 years in Canada and to examine whether these gambling prevalence patterns differ by sex and (or) by geographic region. METHOD: We used data from The Canadian Community Health Survey: Mental Health and Well-Being. Gambling problems were determined according to the Canadian Problem Gambling Index. All prevalence estimates used appropriate sampling weights and bootstrap variance estimation procedures developed by Statistics Canada. Multivariate logistic regression modelling was also employed to supplement the above prevalence comparisons by age, sex, and region. RESULTS: Among Canadian youth aged 15 to 24 years (n=5666), 61.35% gambled in the past 12 months and the national prevalence of moderate-risk or problem gambling was 2.22% (3.30% in male respondents and 1.10% in female respondents). Male respondents had significantly higher prevalence of gambling problems than female respondents. Regional prevalence estimates of youth moderate-risk or problem gambling were 1.37% in British Columbia, 2.17% in the Prairie provinces, 2.75% in Ontario, 2.12% in Quebec, and 1.71% in the Atlantic provinces. CONCLUSIONS: Youth, particularly young men, are at greater risk for gambling problems than adults. More prevention and research efforts are also needed to address the observed sex differences and interregional variability in the prevalence of gambling problems among youth. The national prevalence estimates from this study provide important baseline data against which future cohorts of Canadians can be monitored and measured.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".