Canadian Problem Gambling Index (CPGI) prevalence studies [Canada]: Consolidated dataset
Bibliographic record
Abstract
The Canadian Problem Gambling Index (CPGI) originated in 2000 as a Canadian interprovincial research initiative to develop and validate a new measure to identify problem gamblers in population health surveys. Since the CPGI was introduced, all Canadian provinces and many jurisdictions in other countries have relied on this measure to estimate the prevalence of problem gambling in general and special populations. In 2007, the OPGRC carried out the process of soliciting researchers for data that were collected using the CPGI instrument in order to compile and harmonize data into one large dataset. This is a cross-national and cross sectional dataset (n=21,374) compiled from seven major prevalence studies of Canadian adults (18 or older) residing in the Canadian provinces. It includes 2191 variables with information on gambling activities, gambling behaviours, adverse consequences related to gambling, and problem gambling correlates. Selected variables were harmonized to facilitate cross national comparisons. Included in this concatenated dataset are the following individual problem gambling prevalence studies: National Validation Study 2001, Alberta 2002, British Columbia 2003, Manitoba 2002, Ontario 2001, Ontario 2005, and Newfoundland and Labrador 2005. Please see the Data Source section below for the citations and direct links to the individual datasets comprising this consolidated dataset.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.026 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".