Lower-risk gambling limits: linked analyses across eight countries
Bibliographic record
Abstract
A common public health initiative in many jurisdictions is provision of advice to people to limit gambling to reduce the risk of gambling-related harm. The purpose of this study is to use consistent methodology with existing population-based prevalence surveys of gambling and related harms from different countries to identify quantitative limits for lower risk gambling. Risk curve analyses were conducted with eleven high quality data sets from eight Western countries. Gambling indicators were monthly expenditure, percentage of income spent on gambling, monthly frequency, and number of different types of gambling. Harm indicators included financial, emotional, health, and relationship impacts. Contributing data sets produced limit ranges for each gambling indicator and each harm indicator, which were compared. Gender differences in limit ranges were minor. Modal analysis, an assessment of the mean of the upper and lower range limits, indicated that the risk of harm increases if an individual gambles at these levels or greater: $60 to $120 CAD monthly, five to eight times monthly, spends more than 1 to 3% of gross monthly income or plays three to four different gambling types. This study provides further evidence that lower-risk gambling guidelines can be based upon empirically derived limits.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".