Problem and Probable Pathological Gambling: Considerations from a Community Survey
Bibliographic record
Abstract
OBJECTIVE: To investigate the nature and extent of gambling problems in a region of Canada in which legalized gambling activities were expanded during the 1990s. METHOD: A standardized telephone interview was conducted with a random sample of 738 community-dwelling adults (response rate 74%) in Winnipeg, Manitoba. RESULTS: According to traditional classification criteria, the lifetime prevalence of "probable pathological gambling" was 2.6%. A further 3.0% of the sample met criteria for traditionally defined "problem gambling," and evidence suggests that both types of gamblers share several characteristics. Social or recreational gamblers significantly differed on several variables from individuals who reported gambling problems. CONCLUSIONS: The 2.6% prevalence figure is the highest yet reported in a Canadian epidemiological survey and was obtained in a region that developed a more liberal attitude toward gambling in the 1990s. Further, a continuum of severity was demonstrated by scores on the South Oaks Gambling Screen (SOGS), and a clear and consistent distinction between problem and probable pathological gambling was not apparent. Frequenting casinos and using video poker and slot machines, rather than buying lottery tickets, distinguishes problem or pathological gamblers from recreational gamblers.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".