Investigating gaming structural features associated with gaming disorder and proposing a revised taxonomical model: A scoping review
Bibliographic record
Abstract
Background and aims: Gaming disorder (GD) is a mental health concern that has been heavily contested by experts. This scoping review synthesizes the literature to identify the structural features of video game design that can contribute to GD. Furthermore, a taxonomy of the structural features implicated with GD is proposed, revised from earlier work. Methods: Seven databases, in addition to Google Scholar, were searched. Peer-reviewed studies were included if they assessed a link between gaming structural characteristics and GD or a proxy. The final pool included 105 articles. Results: Avatar creation and customizability, multiplayer characteristics, and reward and punishment features were highly represented in the literature. There was no evidence for three categories in the original taxonomy: support network features, sexual content, and explicit language. Furthermore, structural feature sub-categories emerged that were absent from the previous taxonomy, such as general socialization features, type of virtual world, and in-game currency. Manipulation and control features and presentation features were less represented than social features, narrative and identity features, and reward and punishment features. The reviewers propose two broad classes of addictive gaming structural features: 'features enhancing in-game immersion and realism' and 'gambling-like features'. Discussion and conclusions: Numerous studies found a relationship between social, narrative and identity, and reward and punishment structural characteristics with GD. Two broad classes of gaming structural features were associated with addiction. The first, 'features enhancing in-game immersion and realism,' including social gameplay, avatar creation, storytelling, and graphics/sound. The second, 'gambling-like features,' included different mechanisms of rewards-and-punishment.
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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 | 0.031 | 0.112 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.059 | 0.034 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".