Video Game Playing and Gambling in Adolescents: Common Risk Factors
Bibliographic record
Abstract
Video games and gambling often contain very similar elements with both providing intermittent rewards and elements of randomness. Furthermore, at a psychological and behavioral level, slot machine gambling, video lottery terminal (VLT) gambling and video game playing share many of the same features. Despite the similarities between video game playing and gambling there have been very few studies that have specifically examined video game playing in relation to gambling behavior. This study inquired about the nature of adolescent video game playing, gambling activities, and associated factors. A questionnaire was completed by 996 (549 females, 441 males, 6 unspecified) participants from grades 7–11, who ranged in age from 10–17 years. Overall, the results of the study found a clear relationship between video game playing and gambling in adolescents, with problem gamblers being significantly more likely than non-problem gamblers or non-gamblers to spend excessive amounts of time playing video games. Problem gamblers were also significantly more likely than non-problem gamblers or non-gamblers to rate themselves as very good or excellent video game players. Furthermore, problem gamblers were more likely to report that they found video games, similar to electronic machine gambling, to promote dissociation and to be arousing and/or relaxing.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".