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 percent 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 2 to 3 times per month, spending no more than $501 to $1000CAN per year on gambling, and investing no more than 1% of gross family income on gambling activities. Logistic regression modeling confirmed a significant increase in the risk of gambling-related harm (odds ratios ranging from 1.98 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".