Gaming Disorder and Psychotic Disorders: A Scoping Review
Bibliographic record
Abstract
BACKGROUND: There is a growing interest in understanding the impact of video games in the clinical field, given that their excessive use could be associated with health issues. Particularly, gaming disorder (GD) is considered as an addictive behavioral disorder. Clinicians widely recognize the comorbidity of gaming and psychotic disorders (PDs). Furthermore, association between addictive (i.e., substance use disorders) and PDs are well recognized by clinicians. It seems of high interest to explore GD among people with PDs. To this day, little is known about the consequences of GD in vulnerable populations. OBJECTIVES: The aim of this scoping review was to summarize the available research on the comorbidity between GD and PD and to identify the knowledge gaps in this field. METHODS: We used Levac's six-stage methodology for scoping review. Two-hundred and forty-two articles from seven databases were identified. Eight articles respected our inclusion and exclusion criteria. RESULTS: No available study has assessed the prevalence or incidence of GD among patients with PDs. The cases reported highlight the possibility that excessive video gameplay or abrupt gaming disruption could trigger psychosis in some patients. CONCLUSION: The results highlight a significant lack of knowledge concerning PDs associated with GD as only a few reported cases and one empirical study exposed the potential association between those conditions.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".