Psychophysical and cognitive adverse effects of smart phones overuse on children and adolescent
Bibliographic record
Abstract
Abstract Background: The excessive use of smartphones is seen as a source of child and adolescent violence and probably impacts cognitive function. As the world of mobile phones are continuously evolving, the violence factor is an endless argument. Smart mobile can cause problems other than violence which might be physical, cognitive, or psychological. Objectives: The study aims to clarify the dangers of smartphones overuse on psychophysical and cognitive function. Materials and Methods: A prospective follow-up study was conducted that included 100 children. A convenient sample of participants and their parents were interviewed directly and were asked about their children’s smart phones habit, causes of cellular phone overuse, school performance. Participants behavior was assessed by Modified overt aggression scale, whereas cognitive function by Montreal Cognitive Assessment test was done in 1, 6, and 12 months interval. Results: 50% of participants used smartphones for more than 3 hours per day, as well as most of the cases who used smartphones for a longer duration were children (25%) as compared to other age groups, children used smartphones mainly for violent and nonviolent games than in social applications, excessive mobile phone usage for more than 3 hours daily associated with significant health problems like neck pain (68%), headache (52.1%), eye tearing (51%), sleep problems (47.6%), and backache (47.1%). Children and adolescents who used smartphones for less than 1 hour daily show an increment in their scores, whereas those who used smartphones for more than 1 hour daily gradually decreased their scores with the time used. Conclusion: Smartphone overuse had a significant impact on behavior, school performance, and cognitive function.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".