Video game playing frequency, social cognition, and social behavior in childhood.
Bibliographic record
Abstract
Socialization is the basis of human behavior and thus an individual’s social competence is key to creating and maintaining satisfying relationships, communicating with others, and functioning adaptively in society. With video games increasingly omnipresent in the leisure activities of young people, concerns have been raised as to their potential harmfulness, or conversely, their utility with respect to developing cognitive, affective, and social skills. However, reported links between video game playing frequency (VGPF) and markers of social competence are equivocal, with some studies reporting adverse associations and others highlighting measurable benefits. Much of the previous work in this area has focused on adolescents, yet video game use is also common in elementary school children. This study aimed to explore associations between VGPF and three components of social competence: social cognitive skills, social adaptive skills, and social behavior. Children (n = 57, 6–12 years) completed measures of these components, and their parents completed a video game habits questionnaire. Weekly VGPF was positively associated with executive and social behavior difficulties and negatively associated with social adaptive skills and prosocial behavior. Social adaptive skills, empathy, and lower VGPF were independently associated with prosocial behavior, while poorer empathy and executive difficulties were associated with social behavior problems. In conclusion, elementary school children who played video games less frequently in this study displayed better social competence in terms of prosocial behavior, but there was no association between VGPF and social behavior problems. Limiting gaming frequency could possibly increase opportunities for real-life social interaction, promoting prosocial behavior.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".