Relationship between Addiction to Online Video Games and Cognitive Function in Adolescents
Bibliographic record
Abstract
Introduction: Adolescent is a particular transition phase especially in their development of cognitive function. Cognitive interpreted as an activity of brain which has a function to its external environment. In Pasific Asia, the prevalence of people with decreased cognitive function is 6,7%. One of the factors contributes to this problem is playing online video game. Many previous researches have analyzed the correlation between online video games and cognitive function but there is still pros and cons about this. The aim of this study is to assess the relationship between addiction to online video games and cognitive function in adolescents. Method: The method used in this study is a cross-sectional design of 56 adolescents who were selected using consecutive non-random sampling and met the inclusion criteria. Online video game addiction data is obtained from game addiction scale (GAS) questionnaire and Montreal Cognitive Assessment (MOCA-INA) is used to assess cognitive function. The relationship between two variables were analyzed using the Fisher's Exact. Results: From 56 respondents, 44,6% experienced a decrease in their cognitive function. Data shows the lowest score of domain are attention (64.3%) and memory (55.4%). The respondents who are addicted to online video games are 16.1%. Conclusion: In this study, there is no relationship between addiction to online video games and cognitive function in adolescents.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".