Gambling Harm and the Prevention Paradox in Massachusetts
Bibliographic record
Abstract
The term “prevention paradox” focuses on the notion that more aggregate harm is experienced by low-risk individuals even though high-risk individuals experience the greatest amount of harm per individual. This paper examines whether the prevention paradox in relation to gambling harms exists in Massachusetts. The analysis is drawn from two population surveys and the distribution of harms across four gambling severity groups is examined. The results show that because of the larger size of the three lower severity groups, even the much smaller average number of harms endorsed by members of these groups accounts for nearly three-quarters (72.9%) of the aggregate number of harms across all groups. While almost all individuals in the highest severity group report one or more harms, any individual reporting one or more harms is more likely to be in a lower severity group. Financial, health, and emotional/psychological harms are more common and more broadly distributed across the gambling severity groups compared to relationship, work/school, and illegal harms. In contrast to a similar study in Finland, which found that the most severe group accounted for over 50% of the harms in the health, relationship, and illegal harm domains, the prevention paradox is supported across all harm domains in Massachusetts.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".