Epidemiology of Problem Gambling in Prince Edward Island: A Canadian Microcosm?
Bibliographic record
Abstract
OBJECTIVES: To gather information that describes the extent of gambling and problem gambling in Prince Edward Island (PEI), to rigorously analyze the relation between gambling activities and problem gambling, to document cognitive and emotional correlates of problem gambling, and to identify an at-risk gambling group. METHOD: We selected a random, stratified sample (n = 809) to represent the adult population of PEI. We administered both the South Oaks Gambling Screen (SOGS) and an early version of the Canadian Problem Gambling Index (CPGI) to participants who had gambled. RESULTS: The current rate of problem gambling was 3.1%. Problem gamblers were likely to be under age 30 years, to be single, and to report cognitive, emotional, and substance abuse correlates. Multiple-regression analysis identified a unique and substantial relation between problem gambling and video lottery terminal (VLT) use. We identified a group of at-risk gamblers (scoring 1 or 2 on the SOGS), comprising 14% of the sample. CONCLUSIONS: Gambling and problem gambling patterns in PEI resemble those in most other provinces. The relation found between problem gambling, VLT use, and cognitive, emotional, and substance use correlates should apply to the greater population as well.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".