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Record W4378953276 · doi:10.1556/2006.2023.00019

Investigating gaming structural features associated with gaming disorder and proposing a revised taxonomical model: A scoping review

2023· review· en· W4378953276 on OpenAlex
Nirav Saini, David C. Hodgins

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Behavioral Addictions · 2023
Typereview
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsAvatarPsychologyNarrativeTaxonomy (biology)Social psychologyCognitive psychologyComputer scienceHuman–computer interaction

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.961
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.116
GPT teacher head0.435
Teacher spread0.319 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it