Problematic video gaming and psychological distress among children and adolescents during the COVID-19 pandemic
Bibliographic record
Abstract
Increased internet usage, particularly in video gaming, has been observed in recent years. This scoping review aims to provide an overview of literature on psychological distress in children during the COVID-19 pandemic. The literature search followed the preferred reporting items for systematic reviews and meta-analyses guidelines. Data extraction and thematic analysis were performed to explore problematic video gaming (PVG) and its association with psychological distress. Findings revealed an increase in time spent on gaming during the pandemic, with higher severity of PVG observed in adolescents. Boys were more likely to exhibit gaming addiction symptoms than girls. A bidirectional relationship between PVG and psychological distress was found. Increased screen usage was amplified during the pandemic and persisted as a lingering concern. Educators and parents play a pivotal role in monitoring children’s screen time by structuring online lessons to prevent psychological distress. Lessons drawn from the pandemic are not just retrospective but instrumental for future societal challenges.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".