A Profile of Canadian Adults Seeking Treatment for Gambling Problems and Comparisons with Adults Entering an Alcohol Treatment Program
Bibliographic record
Abstract
OBJECTIVE: To empirically describe problem gambling in the Canadian context, using a large sample of treatment-seeking adults. METHOD: We assessed 1376 Winnipeg adults seen in the problem gambling program at the Addictions Foundation of Manitoba (AFM) and compared them with 11,661 alcohol-program clients seen over the same 4-year time period. RESULTS: Sociodemographic comparisons revealed a higher-functioning profile for individuals with gambling problems compared with those having alcohol problems (for example, higher education and income levels). Most gambling clients did not report symptoms of substance abuse, but almost 70% smoked cigarettes. The most frequent gambling activity involved the use of video lottery terminals (VLTs) in local bars and restaurant lounges. Lottery tickets, bingo, and even casinos were infrequently used by problem gamblers. CONCLUSIONS: In several ways, gambling problems in the Canadian context represent a relatively novel form of addiction that many clinicians have not previously encountered. VLTs were only recently introduced in many parts of Canada, and they appear to play a large role in the expression of problem gambling. One potential reason for the popularity of neighbourhood bars over casinos or US venues is the increased availability of legalized gambling in the community.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".