The impact of internet gambling on gambling problems: A comparison of moderate-risk and problem Internet and non-Internet gamblers.
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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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".