The prevalence and determinants of problem gambling in Australia: Assessing the impact of interactive gambling and new technologies.
Bibliographic record
Abstract
New technology is changing the nature of gambling with interactive modes of gambling becoming putatively associated with higher rates of problem gambling. This paper presents the first nationally representative data on the prevalence and correlates of problem gambling among Australian adults since 1999 and focuses on the impact of interactive gambling. A telephone survey of 15,006 adults was conducted. Of these, 2,010 gamblers (all interactive gamblers and a randomly selected subsample of those reporting land-based gambling in the past 12 months) also completed more detailed measures of problem gambling, substance use, psychological distress, and help-seeking. Problem gambling rates among interactive gamblers were 3 times higher than for noninteractive gamblers. However, problem and moderate risk gamblers were most likely to attribute problems to electronic gaming machines and land-based gambling, suggesting that although interactive forms of gambling are associated with substantial problems, interactive gamblers experience significant harms from land-based gambling. The findings demonstrate that problem gambling remains a significant public health issue that is changing in response to new technologies, and it is important to develop strategies that minimize harms among interactive gamblers.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".