Gaming Disorder in First Episode Psychosis: Prevalence and Impact on Symptomatology and Functioning in a Prospective Cohort Study
Bibliographic record
Abstract
BACKGROUND AND HYPOTHESIS: First-episode psychosis (FEP) often coincides with comorbidities that hinder recovery. Gaming disorder (GD) has emerged as a potential concern; however, data on its prevalence and clinical impact in FEP populations remain limited. We hypothesized that GD would be more prevalent among individuals with FEP than in the general population and associated with more severe psychotic symptoms and poorer functional outcomes. STUDY DESIGN: In this prospective cohort study, 284 consecutive patients aged 18-35 years admitted to an Early Intervention Service in Quebec, Canada, were assessed for GD using International Classification of Diseases-11 criteria, with up to 24 months follow-up. Psychotic symptoms and functioning were measured at baseline and at 6-month intervals using the Positive and Negative Syndrome Scale-6 items (PANSS-6) and the Social and Occupational Functioning Assessment Scale (SOFAS). STUDY RESULTS: Gaming disorder was present in 7.04% (95% CI: [4.35%, 10.67%]) of the sample, exceeding the 3.3% general population estimates. Among the 105 patients who reported gaming at least monthly, those with GD (n = 20) more often played Massively Multiplayer Online Role-Playing Games, while non-GD gamers (n = 85) preferred action-adventure games. GD was associated with significantly higher PANSS-6 scores (P = .04), driven by elevated negative symptoms (P = .04). SOFAS scores were consistently lower among individuals with GD, with effect sizes increasing over time. CONCLUSIONS: Gaming disorder is a prevalent and clinically meaningful comorbidity in FEP, linked to greater negative symptom burden and functional impairment. These findings support the need for systematic GD screening in early psychosis services and highlight the importance of developing tailored interventions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".