Game Faces: How Digital Play Affects the Psychological Landscape of Youth
Bibliographic record
Abstract
The use of video games in children and adolescents has been growing since the invention of home interactive entertainment. With that growth, many parents and mental health professionals alike have questioned the impact on the mental well-being of their children and patients. Using current literature, we shall investigate the impact of video gaming on children's and teenagers' mental health in this systemic review. We will investigate time spent playing video games and the development of behavioral disorders, obsessive-compulsive disorder (OCD), depression, anxiety, and general psychological well-being. A search of PubMed and EBSCO discovery host was done, looking for primary peer-reviewed articles on the mental health outcomes of video gaming in the pediatric population (2-18 years old) of North America with no prior mental health diagnosis. The search returned 713 articles. After screening and selection, nine articles on six distinct studies were included. Overall, increased time spent playing video games was linked to increased depression and OCD symptoms, behavioral disorders, and suicidal ideation. This is a multifactorial issue that lacks substantial research in the current literature, leaving an opportunity for expansion on this topic in the future.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".