Not Too Much, Not Too Often, and Not Too Many: the Results of the First Large-Scale, International Project to Develop Lower-Risk Gambling Guidelines
Bibliographic record
Abstract
Abstract Until now, there has been no evidence-based, specific advice for people who gamble who want to reduce their risk of experiencing gambling harms. This paper presents the results from the first large-scale, comprehensive, international project to develop lower-risk gambling guidelines. Specifically, we calculated relative risk estimates to determine risk of harm across the range of possible limits for gambling frequency, expenditure, and number of types of gambling engaged in; conducted an online survey (n = 4583) of people who gamble to assess whether they understood and found credible the proposed quantitative limits; conducted a series of interviews and focus groups with people who gamble to assess self-control strategies and reactions to proposed quantitative limits; conducted a meta-analysis of problem gambling risk factors in the general population; and consulted with a pan-Canadian, multi-sectoral committee of stakeholders. Project outcomes were examined and deliberated by a working group of scientists who decided upon a set of recommendations for lower-risk gambling. This paper presents these recommendations.
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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 | 0.095 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".