A Perspective on Age Restrictions and Other Harm Reduction Approaches Targeting Youth Online Gambling, Considering Convergences of Gambling and Videogaming
Bibliographic record
Abstract
Internet gambling has become a popular activity among some youth. Vulnerable youth may be particularly at risk due to limited harm reduction and enforcement measures. This article explores age restrictions and other harm reduction measures relating to youth and young adult online gambling. A systematic rapid review was conducted by searching eight databases. Additional articles on online gambling (e.g., from references) were later included. To place this perspective into context, articles on adult gambling, land-based gambling, and substance use and other problematic behaviors were also considered. Several studies show promising findings for legally restricting youth from gambling in that such restrictions may reduce the amount of youth gambling and gambling-related harms. However, simply labeling an activity as "age-restricted" may not deter youth from gambling; in some instances, it may generate increased appeal for gambling. Therefore, advertising and warning labels should be examined in conjunction with age restrictions. Recommendations for age enforcement strategies, advertising, education, and warning labels are made to help multiple stakeholders including policymakers and public health officials internationally. Age restrictions in online gambling should consider multiple populations including youth and young adults. Prevention and harm reduction in gambling should examine how age-restriction strategies may affect problem gambling and how they may be best enforced across gambling platforms. More research is needed to protect youth with respect to online gambling.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".