Gambling and Problem Gambling among Canadian Urban Aboriginals
Bibliographic record
Abstract
OBJECTIVE: To assess the prevalence of gambling and problem gambling in urban Aboriginals in the Canadian Prairie provinces and to determine the predictors of problem gambling. METHOD: In total, 1114 Aboriginals living in 15 cities in Alberta, Saskatchewan, and Manitoba were recruited via posters and direct solicitation at Native Friendship Centres, shopping malls, and other locations where Aboriginals congregated. They each completed a self-administered 5- to 10-minute survey. RESULTS: Urban Aboriginals in the present sample were found to have a much higher level of gambling participation than the general Canadian public, especially for electronic gambling machines, instant lotteries, and bingo. Their intensity of participation in terms of number of formats, frequency of play, and gambling expenditure was also very high. This, in turn, is an important contributing factor to their very high rate of problem gambling, which was found to be 27.2%. Problem gambling was higher in males, unemployed people, and cities having the highest proportion of their population consisting of urban Aboriginals. CONCLUSIONS: Urban Aboriginal people appear to have some of the highest known rates of problem gambling of any group in Canada. This is attributable to having many more risk factors for problem gambling, such as a greater level of participation in gambling, greater participation in continuous forms of gambling (e.g., electronic gambling machines), younger average age, higher rates of substance abuse and mental health problems, and a range of disadvantageous social conditions (e.g., poverty, unemployment, poor education, cultural stress) that are conducive to the development of addictive behaviour.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".