More than loot boxes: the role of video game streams and gambling-like elements in the gaming-gambling connection among adolescents
Bibliographic record
Abstract
The intertwining of video games and gambling, known as simulated gambling, has prompted concerns about the potential influence of simulated gambling as a stepping stone towards monetary gambling. Previous studies tend to focus overwhelmingly on loot boxes, which are video game packages where the randomized content is hidden until opening them. The current study broadens this horizon by mapping the relationships between various gambling-like elements within the video gaming ecosystem, and monetary gambling. Applying the Theory of Reasoned Action, the study considered attitude, perceived normative pressure, and intention alongside monetary gambling behavior. In winter 2021 and early 2022, 1472 Flemish adolescents (mean age = 14.02, 47.5% female) took part in a survey on simulated and monetary gambling. Respondents had participated in simulated (75.3%) and monetary gambling (60.4%) in the past year. Bivariate correlations revealed that gambling-like activities were positively correlated (p < .001) with monetary gambling. Hierarchical multiple regression analyses indicated that watching gambling streams, spinning prize wheels, and spending money in social casino games predict monetary gambling (p < .001). Adding loot boxes and other gambling-like elements explained extra variance on top of gender and age (p < .001). Structural equation modelling suggested a pathway model from simulated gambling to monetary gambling attitude, normative pressure, intention, and behavior. This study underscores the importance of considering diverse gambling- like elements in research on the relationship between simulated and monetary gambling, next to the applicability of the Theory of Reasoned Action.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".