Relationship between Online Game Addiction with Depression in Adolescents from 6 High Schools in Indonesia
Bibliographic record
Abstract
The World Health Organization (WHO) has defined online game addiction into one of the medical conditions in 2018 under the category of Gaming disorder. Playing online games excessively may be related to both physical and psychological health. The problem of online game addiction and its impact on adolescent’s mental health is still not sufficiently documented. This study aimed to analyze the relationship between online game addiction and depression in adolescents from 6 High Schools in 4 districts/cities of Indonesia. This study is a quantitative research with cross-sectional design as a further analysis of the 2019 School-Based Mental Health Intervention Study. This study involved students of grade 10 and 11 at 6 public high schools in Bogor Regency, Bogor City, City of Central and South Jakarta. The statistical analysis used was multiple logistic regression test. Out of 746 students, 12.9% were depressed and 10.3% were addicted to online games. There was a significant relationship between online game addiction towards depression with OR = 2.44 (95% CI: 1.259-4.735) after being controlled for sex, learning difficulties, age, father's education, and mother's education. Schools are the best institutions that can be used for adolescent depression screening. It is expected that provision of mental health service facilities in school as one of the strategies to overcome online gaming addictions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".