Gambling and Problem Gambling in Canada in 2018: Prevalence and Changes Since 2002
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to provide an updated profile of gambling and problem gambling in Canada and to examine how the rates and pattern of participation compare to 2002. METHOD: An assessment of gambling and problem gambling was included in the 2018 Canadian Community Health Survey and administered to 24,982 individuals aged 15 and older. The present analyses selected for adults (18+). RESULTS: A total of 66.2% of people reported engaging in some type of gambling in 2018, primarily lottery and/or raffle tickets, the only type in which the majority of Canadians participate. There are some significant interprovincial differences, with perhaps the most important one being the higher rate of electronic gambling machine (EGM) participation in Manitoba and Saskatchewan. The overall pattern of gambling in 2018 is very similar to 2002, although participation is generally much lower in 2018, particularly for EGMs and bingo. Only 0.6% of the population were identified as problem gamblers in 2018, with an additional 2.7% being at-risk gamblers. There is no significant interprovincial variation in problem gambling rates. The interprovincial pattern of problem gambling in 2018 is also very similar to what was found in 2002 with the main difference being a 45% decrease in the overall prevalence of problem gambling. CONCLUSIONS: Gambling and problem gambling have both decreased in Canada from 2002 to 2018 although the provincial patterns are quite similar between the 2 time periods. Several mechanisms have likely collectively contributed to these declines. Decreases have also been reported in several other Western countries in recent years and have occurred despite the expansion of legal gambling opportunities, suggesting a degree of inoculation or adaptation in large parts of the population.
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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 | 0.000 | 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 | 0.000 |
| 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".