Effect of Using Smart Mobile Device on Child Prosocial and Difficult Behaviors in School Age; Parents’ Perception
Bibliographic record
Abstract
Background: The time that children spend using digital devices is increasing rapidly with the developmentof new portable and instantly accessible technology. Mobile devices are embedded in and dominate the dailylives of young children. Research Aims: assess the effect of using smart mobile device on child Prosocialand difficult behaviors in school age. Methodology: A cross sectional research design was utilized at August2019 - January 2020. Convenience sample include the 400 school children. An online survey by usingGoogle form, which contains three parts (characteristics of parents, children and Strengths and DifficultiesQuestionnaire). Results: revealed that47.2% and 47.7% of studied children had abnormal emotionalsymptoms and conduct problems. In addition (34.5%) of studied children was normal related peer problemsdomain. Also, (51.2%) of them was abnormal related hyperactivity. While, (30%) of studied children hadnormal Prosocial behavior. Conclusions: the current study concluded that about half of studied children hadabnormal Prosocial and difficult behaviors and less than one quarter of them had borderline Prosocial anddifficult behaviors. While, less than one third of them had normal Prosocial and difficult behaviors.
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".