The impact of internet gambling on gambling problems: A comparison of moderate-risk and problem Internet and non-Internet gamblers.
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
Numerous studies have reported higher rates of gambling problems among Internet compared with non-Internet gamblers. However, little research has examined those at risk of developing gambling problems or overall gambling involvement. This study aimed to examine differences between problem and moderate-risk gamblers among Internet and non-Internet gamblers to determine the mechanisms for how Internet gambling may contribute to gambling problems. Australian gamblers (N = 6,682) completed an online survey that included measures of gambling participation, problem gambling severity, and help seeking. Compared with non-Internet gamblers, Internet gamblers were younger, engaged in a greater number of gambling activities, and were more likely to bet on sports. These differences were significantly greater for problem than moderate-risk gamblers. Non-Internet gamblers were more likely to gamble on electronic gaming machines, and a significantly higher proportion of problem gamblers participated in this gambling activity. Non-Internet gamblers were more likely to report health and psychological impacts of problem gambling and having sought help for gambling problems. Internet gamblers who experience gambling-related harms appear to represent a somewhat different group from non-Internet problem and moderate-risk gamblers. This has implications for the development of treatment and prevention programs, which are often based on research that does not cater for differences between subgroups of gamblers.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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