Integrating gaming disorder into early intervention in first-episode psychosis – current knowledge and future directions
Bibliographic record
Abstract
Gaming disorder (GD) is increasingly recognized as a clinically significant condition, yet its implications in first-episode psychosis (FEP) remain largely unexplored. This perspective article focuses on the intersection of GD and FEP, highlighting key diagnostic and treatment challenges, including symptom overlap that complicates differential diagnosis, the absence of validated screening tools, and difficulties in sustained patient engagement. Drawing insights from substance use disorder management in FEP, we propose a preliminary clinical framework for integrating GD assessment and intervention into early intervention in psychosis programs. This approach prioritizes comprehensive evaluation, patient-centered care, and a harm-reduction model that supports digital well-being. Addressing GD inFEP populations is crucial for optimizing functional recovery and promoting a holistic, recovery-oriented approach to psychiatric care. Further research is needed to refine screening tools and validate tailored interventions in this population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".