Original Research A National Survey of Gambling Problems in Canada
Bibliographic record
Abstract
In several Canadian provinces, the 1990s saw widespreadexpansion in the number and types of legalized gambling (1,2). Among other factors, increased availability of legalized gambling has been associated with increased rates of gam-bling problems in the general population (3,4). Although Canadian policy-makers recognize that a social cost accom-panies the financial revenues of gambling expansion, there has never been a nationwide, interprovincial survey of gam-bling problems. From a public health perspective, we need ac-curate data from national community surveys to plan Can J Psychiatry, Vol 50, No 4, March 2005 213 Objective: The 1990s saw widespread expansion of new forms of legalized gambling involving video lottery terminals (VLTs) in community settings (that is, in bars and restaurant lounges) and permanent casinos in several Canadian provinces. To date, there has never been a national survey of gambling problems with representative interprovincial data. Using a new survey, we sought to compare prevalence figures across the 10 Canadian
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".