Internet Addiction and Online Gaming Disorder in Children and Adolescents During COVID-19 Pandemic: A Systematic Review
Bibliographic record
Abstract
The Indonesian government has enforced several social restrictions to prevent the spread of the coronavirus disease-2019 (COVID-19) virus, such as closures of in-person schools, public areas, and playgrounds as well as reduced outdoor activities. These restrictions will affect mental health of school-age children and adolescents. The internet is chosen as one of the media to keep academic activities running, but excessive internet use will increase internet addiction and online gaming disorder. This study aimed to understand the prevalence and psychological impacts of internet addiction and online gaming disorder on children and adolescents globally during the pandemic. Systematic searches were carried out on the PubMed, ProQuest, and Google Scholar search engines. All studies were assessed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 criteria and the Newcastle Ottawa Scale. Five studies met the criteria for assessing internet addiction and online gaming disorder cases in children and adolescents. Four studies discussed internet addiction, and one study addressed the negative impacts of online gaming on children and adolescents during the COVID-19 pandemic. There has been an increase in internet use and online gaming disruption in children and adolescents in almost all parts of Asian and Australian countries during the COVID-19 pandemic period.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".