How risky is Internet gambling? A comparison of subgroups of Internet gamblers based on problem gambling status
Bibliographic record
Abstract
Internet gambling offers unique features that may facilitate the development or exacerbation of gambling disorders. Higher rates of disordered gambling have been found amongst Internet than with land-based gamblers; however little research has explored whether Internet disordered gamblers are a distinct subgroup. The current study compared problem with non-problem and at-risk Internet gamblers to understand further why some Internet gamblers experience gambling-related harms, using an online survey with a sample of 2799 Australian Internet gamblers. Problem gambling respondents were younger, less educated, had higher household debt, lost more money and gambled on a greater number of activities, and were more likely to use drugs while gambling than non-problem and at-risk gamblers. Problem gamblers had more irrational beliefs about gambling, were more likely to believe the harms of gambling to outweigh the benefits, that gambling is morally wrong and that all types of gambling should be illegal. For problem gamblers, Internet gambling poses unique problems related to electronic payment and constant availability, leading to disrupted sleeping and eating patterns. However, a significant proportion of Internet problem gambling respondents also had problems related to terrestrial gambling, highlighting the importance of considering overall gambling involvement when examining subgroups of gamblers. It is argued that policy makers should consider carefully how features of Internet gambling contribute to gambling disorders requiring the implementation of evidence-based responsible gambling strategies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".