Physical card pack and especially video game loot box spending are both positively correlated with problem gambling but not linked to negative mental health: An international survey.
Bibliographic record
Abstract
OBJECTIVE: Card packs are physical products providing random content that companies rely on to monetize trading or collectible card games. Loot boxes are equivalent digital products inside video games that can similarly be bought to obtain randomized rewards. Both products are psychologically similar to gambling because the player can "win" by obtaining rare and valuable rewards or alternatively "lose" by obtaining nonvaluable rewards. Loot box spending has been repeatedly and reliably linked to problem gambling. However, the link between card pack spending and gambling has been little studied. METHOD: = 1,961) to assess the links between card pack and loot box spending on one hand and problem gambling and mental health outcomes on the other. RESULTS: = 0.22) were all linked to problem gambling but at markedly different strengths. Spending money on all these gambling-like products were not associated with negative mental health. Spending money on certain subcategories of loot boxes differs from overall spending. CONCLUSIONS: The current legal definitions of "gambling" in many countries should be modernized using scientific evidence: Presently, the law (if properly enforced) would regulate products that are less strongly correlated with problem gambling and therefore arguably less potentially harmful (e.g., physical card packs), but fails to regulate arguably more harmful products that are more strongly correlated with problem gambling (e.g., loot boxes). (PsycInfo Database Record (c) 2026 APA, all rights reserved).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".