Fifteen years of problem gambling prevalence research: What do we know? Where do we go?
Bibliographic record
Abstract
This paper charts the rapid growth of problem gambling prevalence research in North America and internationally. Looking beyond the overall prevalence of problem gambling in the general population, the results of these studies support the notion of a link between the expansion of legal gambling opportunities and the prevalence of problem gambling as well as the notion that the characteristics of problem gamblers change in response to changes in the availability of specific types of gambling. The results of these studies also challenge existing concepts and definitions of problem gambling. In the future, it will be important to improve how problem gambling prevalence research is done. Such work is likely to include changes in how we measure gambling problems as well as requiring us to take steps to overcome obstacles in achieving representative samples of the population and obtaining valid and accurate information.
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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 | 0.040 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| 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; 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".