A public health approach for Asian people with problem gambling in foreign countries
Bibliographic record
Abstract
There has been a rapid increase in Asian immigration to English-speaking countries such as New Zealand, Australia, Canada, and the United States. Anecdotal accounts and research suggest high levels of participation in gambling by people from Asian countries. Asian problem gambling is seen as being a social rather than an individual problem compounded by difficulties with post-migration adjustment. Contemporary public health perspectives are not limited to the biological and behavioural dimensions, but can also address socioeconomic determinants such as income, employment, poverty, and access to social and healthcare services related to gambling and health. This paper discusses how a public health viewpoint can lead to effective strategies against problem gambling. The five principles proposed in this paper are: (1) acknowledging similarities and differences within Asian populations, (2) ensuring that strategies are evidence-based, (3) treating Asian problem gambling in an acculturation framework, (4) addressing the issue of shame associated with problem gambling among Asian people, and (5) targeting at-risk sub-groups.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 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".