A scoping review of the association between loot boxes, esports, skin betting, and token wagering with gambling and video gaming behaviors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background and aims: Many new digital gambling-like activities such as loot boxes, esports betting, skin betting, and token wagering have recently emerged and grown in popularity. This scoping review aimed to: (a) synthesize the existing empirical research literature on gambling-like activities and their associations with gambling and video gaming behaviors, including problem gambling and video gaming; (b) identify sociodemographic, psychological, and motivational factors associated with engagement in gambling-like activities; and (c) identify research gaps and areas for further research. Methods: A systematic search of Ovid, Embsco, and ProQuest databases and Google Scholar was conducted in May 2021 and last updated in February 2022. The search yielded a total of 2,437 articles. Articles were included in the review if they were empirical studies that contained quantitative or qualitative results regarding the relationship between gambling-like activities and gambling or gaming. Results: Thirty-eight articles met inclusion criteria and were included in the review. Overall, the review results suggest that all forms of gambling-like activities were positively associated with gambling and gaming with small to medium effects. Gambling-like activity participation was also positively associated with mental distress and impulsivity. Gaps identified included a lack of inquiry into skin betting and token wagering, a lack of diversity in the research methods (i.e., mainly cross-sectional surveys), and a paucity of research that includes more ethnically, culturally, and geographically diverse populations. Discussion: Longitudinal studies with more representative samples are needed to examine the causal link between gambling-like activities and gambling and video gaming.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it