Adolescent Problem Gaming and Loot Box Purchasing in Video Games: Cross-sectional Observational Study Using Population-Based Cohort Data
Bibliographic record
Abstract
BACKGROUND: Video game loot boxes, which can typically be purchased by players or are given as reward, contain random virtual items, or loot, ranging from simple customization options for a player's avatar or character, to game-changing equipment such as weapons and armor. Loot boxes have drawn concern, as purchasing loot boxes might lead to the development of problematic gambling for adolescents. Although parental problem gambling is associated with adolescent problem gambling, no studies have evaluated the prevalence of loot box purchases in adolescents' parents. OBJECTIVE: This study investigated the association between loot box purchasing among adolescents and parents, and problem online gaming in population-based samples. METHODS: In total, 1615 adolescent (aged 14 years) gamers from Japan responded to a questionnaire regarding their loot box purchasing and problem online gaming behaviors. Problem online gaming was defined as four or more of the nine addictive behaviors from the Diagnostic and Statistical Manual of Mental Disorders. The adolescents' primary caregivers were asked about their loot box purchasing. RESULTS: Of the 1615 participants, 57 (3.5%) reported loot box purchasing. This prevalence did not differ according to primary caregivers' loot box purchasing, but adolescents who purchased loot boxes were significantly more likely to exhibit problem online gaming (odds ratio 3.75, 95% CI 2.17-6.48). CONCLUSIONS: Adolescent loot box purchasing is linked to problem online gaming, but not with parents' loot box purchasing. Measures to reduce these behaviors should target reducing addictive symptoms in young video gamers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".