A research plan to define Canada’s first low-risk gambling guidelines
Bibliographic record
Abstract
From a public health perspective, gambling shares many of the same characteristics as alcohol. Notably, excessive gambling is associated with many physical and emotional health harms, including depression, suicidal ideation, substance use and addiction and greater utilization of health care resources. Gambling also demonstrates a similar 'dose-response' relationship as alcohol-the more one gambles, the greater the likelihood of harm. Using the same collaborative, evidence-informed approach that produced Canada's Low-Risk Alcohol Drinking and Lower Risk Cannabis Use Guidelines, a research team is leading the development of the first national Low-Risk Gambling Guidelines (LRGGs) that will include quantitative thresholds for safe gambling. This paper describes the research methodology and the decision-making process for the project. The guidelines will be derived through secondary analyses of several large population datasets from Canada and other countries, including both cross-sectional and longitudinal data on over 50 000 adults. A scientific committee will pool the results and put forward recommendations for LRGGs to a nationally representative, multi-agency advisory committee for endorsement. To our knowledge, this is the first systematic attempt to generate a workable set of LRGGs from population data. Once validated, the guidelines inform public health policy and prevention initiatives and will be disseminated to addiction professionals, policy makers, regulators, communication experts and the gambling industry. The availability of the LRGGs will help the general public make well-informed decisions about their gambling activities and reduce the harms associated with gambling.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; both teacher heads agree on what is shown here.
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".