Between two worlds: Exploring esports betting in relation to problem gambling, gaming, and mental health problems
Bibliographic record
Abstract
BACKGROUND AND AIMS: Esports betting is an emerging gambling activity where individuals place bets on an organized video gaming competition. It represents only one of several gambling activities commonly endorsed by adolescents. To date, limited research has explored the relationship between esports betting and mental health among adolescents and its convergence with both problem gambling (PG) and problem video gaming (PVG). The present study examined the relation between esports betting, PG and PVG, and both externalizing and internalizing problems among adolescents while accounting for adolescents' video gaming intensity (i.e., how often they play 2 h or more in a day) and engagement in other gambling activities. METHODS: Data was collected from 6,810 adolescents in Wood County, Ohio schools. A subset of 1,348 adolescents (M age = 14.67 years, SD = 1.73, 64% male) who had gambled and played video games during the past year were included in the analyses. RESULTS: Approximately 20% (n = 263) of the included sample had bet on esports during the past year. Esports betting was positively correlated with other forms of gambling, both PG and PVG, and externalizing behaviors. Mediation analyses revealed esports betting was associated to both internalizing and externalizing problems through PVG and not PG. CONCLUSIONS: Esports betting may be particularly appealing to adolescents who are enthusiastic video gamers. As such, regulators must be vigilant to ensure codes of best practices are applied to esports betting operators specifically for underaged individuals.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".