Adolescent Screen Use: Problematic Internet Use and the Impact of Gender
Bibliographic record
Abstract
OBJECTIVE: The relationship between screen use and problematic internet use (PIU; i.e., internet addiction) amongst adolescents has not been sufficiently explored. Further, there is even less research on how gender is associated with this relationship. The goal of the study was to examine adolescent screen use, PIU, and its impact on day-to-day routines of adolescents. METHODS: Participants were recruited from an outpatient pediatric clinic in São Paulo, Brazil. A total of 180 adolescents completed questionnaires related to their screen use, extracurricular activities, and symptoms of PIU. Univariate and multivariate statistics were used to determine correlates and predictors of PIU, and to explore gender differences. RESULTS: A total of 26.1% of adolescents met the criteria for PIU. There were no significant differences between boys and girls in PIU severity. However, there were significant gender differences in preferred use of the Internet, with boys being more likely to access the Internet to play video games (odds ratio [OR]=27.1) and girls being more likely to socialize with friends (OR=4.51). PIU severity increased proportionally to the number of hours of use of all screen devices with moderate-to-large effect sizes (η2=0.060-0.157). Using screens during meals and missing extracurricular activities were both associated with PIU. CONCLUSION: Though gender was not associated with PIU, both excessive use of screen devices during meals and neglect of offline extracurriculars activities were identified as risk factors for PIU. Prevention measures should consider the impact of gender and associated patterns of motivation and Internet use.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".