Charting a path towards a public health approach for gambling harm prevention
Bibliographic record
Abstract
Abstract Aim Gambling harm is a serious public health issue affecting the health, financial security, and social well-being of millions of people and their close relations around the world. Despite its population health implications, gambling harm is not typically viewed and treated as a public health policy issue. This paper critically reviews the evolution of the public health perspective on gambling harm. It also considers how gambling harm can be operationalized within a public health model. Methods A critical historical review of the emerging public health perspective on gambling harm was conducted. Key documents covering three decades of development were reviewed and appraised through a process of deliberation and debate over source impact in the fields of research, policy, and programming internationally. Results The first decade mainly focused on identifying gambling harm and framing the public health issue. The second decade featured the expansion of health assessment and emerging areas of policy and program development. The third decade saw an increased focus on public health frameworks that advanced understanding of harm mechanics and impact. As reflected by the essential functions of a general public health model, gambling harm prevention efforts emphasize health promotion over other key functions like health assessment and surveillance. Conclusion Gambling harm is a public health issue requiring greater attention to health assessment and surveillance data development.
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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 | 0.017 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".