Risk of harm among gamblers in the general population as a function of level of participation in gambling activities
Bibliographic record
Abstract
AIMS: To examine the relationship between gambling behaviours and risk of gambling-related harm in a nationally representative population sample. DESIGN: Risk curves of gambling frequency and expenditure (total amount and percentage of income) were plotted against harm from gambling. SETTING: Data derived from 19, 012 individuals participating in the Canadian Community Health Survey-Mental Health and Well-being cycle, a comprehensive interview-based survey conducted by Statistics Canada in 2002. MEASUREMENT: Gambling behaviours and related harms were assessed with the Canadian Problem Gambling Index. FINDINGS: Risk curves indicated the chances of experiencing gambling-related harm increased steadily the more often one gambles and the more money one invests in gambling. Receiver operating characteristic analysis identified the optimal limits for low-risk participation as gambling no more than two to three times per month, spending no more than 501-1,000 CAN dollars per year on gambling and investing no more than 1% of gross family income on gambling activities. Logistic regression modelling confirmed a significant increase in the risk of gambling-related harm (odds ratios ranging from 2.0 to 7.7) when these limits were exceeded. CONCLUSIONS: Risk curves are a promising methodology for examining the relationship between gambling participation and risk of harm. The development of low-risk gambling limits based on risk curve analysis appears to be feasible.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".