Prevalence of gambling-related harm provides evidence for the prevention paradox
Bibliographic record
Abstract
Background The prevention paradox (PP) describes a situation in which a greater number of cases of a disease-state come from low-risk members of a population, because they are more prevalent than high-risk members. Past research has provided only tangential and disputed evidence to support the application of the PP to gambling-related harm. Aims To assess whether the PP applies to gambling, the prevalence of a large set (72) of diverse harmful consequences from gambling was examined across four risk categories for problem gambling, including no-risk, low-risk, moderate-risk, and problem-gambling. Methods Respondents who had gambled on non-lottery forms in the past 6 months completed an online survey (N = 1,524, 49.4% male). The data were weighted to the known prevalence of gambling problems in the Victorian community. Results The prevalence of gambling harms, including severe harms, was generally higher in the combined categories of lower risk categories compared to the high-risk problem-gambling category. There were some notable exceptions, however, for some severe and rare harms. Nevertheless, the majority of harms in the 72-item list, including serious harms such as needing temporary accommodation, emergency welfare assistance, experiencing separation or end of a relationship, loss of a job, needing to sell personal items, and experiencing domestic violence from gambling, were more commonly associated with lower risk gamblers. Conclusion Many significant harms are concentrated outside the ranks of gamblers with a severe mental health condition, which supports a public-health approach to ameliorating gambling-related harm.
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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 | 0.018 | 0.103 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".