Loot boxes, gambling-related risk factors, and mental health in Mainland China: A large-scale survey
Bibliographic record
Abstract
Loot boxes can be bought with real-world money to obtain random content inside video games. Loot boxes are viewed by many as gambling-like and are prevalently implemented globally. Previous Western and international studies have consistently found loot box spending to be positively correlated with problem gambling. Previous Western studies presented mixed results as to the correlations between loot box purchasing and gambling-related risk factors, mental wellbeing, and psychological distress. A large-scale survey of adult video game players from the People's Republic of China (PRC) (N = 2601) was conducted through Tencent Survey. The positive correlations between loot box spending and problem gambling, and between loot box spending and problem videogaming, were successfully replicated. However, other potential risk factors (i.e., impulsivity/impulsiveness; binary past-year gambling participation status; and sensation-seeking tendencies) either did not positively correlate with loot box spending or only did so weakly. Contrary to expectations, high impulsivity was negatively associated with loot box engagement. The Risky Loot Box Index (RLI) most strongly positively correlated with, and was the best predictor in multiple linear regression models for, loot box spending. The RLI may be effective at measuring loot box harms cross-culturally. A surprising weak positive correlation was found between loot box engagement and PRC players' mental wellbeing, and high psychological distress unexpectedly negatively predicted loot box purchasing. Although gambling-like, the risk and protective factors of loot boxes are seemingly different, meaning they should rightfully be treated as novel products. Cross-cultural research can contribute to a better understanding of loot box harms.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".