Exploring the association between loot boxes and problem gambling: Are video gamers referring to loot boxes when they complete gambling screening tools?
Bibliographic record
Abstract
Concerns regarding the similarities between video game 'loot boxes' and gambling have been supported by correlations in survey studies between loot box engagement and problem gambling scores. It is generally noted that this correlation could reflect loot box users migrating to conventional gambling, and/or people with gambling problems being attracted to loot boxes when they play video games. We describe a third possibility, that when gamers complete problem gambling screens they may be referring to harms incurred from their loot box use. Using three secondary datasets from cross-sectional online surveys, we explore this account in two ways. First, in participants who do not endorse any participation in conventional forms of gambling, we compare rates of positive (i.e. non-zero) scores on the Problem Gambling Severity Index (PGSI) in participants with and without loot box use. Second, noting that some PGSI items have less relevance to loot box use versus gambling, we compare endorsement rates of individual PGSI items, in gamers versus gamblers, and loot box users vs non-loot box users (focusing on item 3 "going back another day to win back the money you lost"). In analysis 1, positive PGSI scorers among non-gamblers were significantly elevated in loot box users vs non-loot box users, although absolute numbers were low overall. In analysis 2, there were no reliable differences (gamers vs gamblers, loot box users vs non-loot box users) in PGSI item 3 endorsement rates. We conclude that these results provide partial support for this third option, and highlight a need for future studies to consider this possibility more directly.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".