How risky is Internet gambling? A comparison of subgroups of Internet gamblers based on problem gambling status
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it