Problem gambling, anxiety and poverty: an examination of the relationship between poor mental health and gambling problems across socio-economic status
Bibliographic record
Abstract
Socio-economic status has been shown to be significantly related to both problem gambling and mental health problems. Additionally, forms of psychopathology such as mood and anxiety disorders have been shown to correlate with problem gambling across a variety of settings. However, relatively little research has been conducted examining whether the connection between mood and anxiety disorders and problem gambling is consistent across different levels of socio-economic status. This study examines gambling-related problems among a representative sample of Canadian adults using the 2008 Canadian Community Health Survey (N = 28,271). Generalized linear modelling is used to analyze the data. A moderation effect is found that shows the relationship between anxiety disorders and problem gambling severity varies significantly across socio-economic status. This study shows that social setting has an important influence on the assumed relationship between psychopathology and gambling problems that is downplayed in current problem gambling research. A discussion of the need for greater inclusion of socio-economic context when making assumptions about the connections between problem gambling and psychiatric disorders is made in light of the responsibilities of gambling providers and regulators.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".