Bibliographic record
Abstract
Public health has a tradition of addressing emerging and complex health matters that affect the whole population as well as specific groups. AIDS, environmental tobacco smoke and violence are examples of contemporary health concerns that have benefited from public health analysis and involvement. This article encourages the adoption of a public health perspective on gambling issues. Gambling has been studied from a number of perspectives, including economic, moral, addiction and mental health. The value of a public health viewpoint is that it examines the broad impact of gambling rather than focusing solely on problem and pathological gambling behavior in individuals. It takes into consideration the wider health, social and economic costs and benefits; it gives priority to the needs of vulnerable and disadvantaged people; and it emphasizes prevention and harm reduction. This paper looks at the public health foundations of epidemiology, disease control and healthy public policy, and applies them to gambling. Major public health issues are analyzed within a North American context, including problem gambling trends amongst the general adult population and youth, and their impact on other specific populations. There is significant opportunity for public health to contribute its skills, methodologies and experience to the range of gambling issues. By understanding gambling and its potential impacts on the public's health, policy makers, health practitioners and community leaders can minimize gambling's negative impacts and optimize its benefits.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".