Statistical Study on the Impact of Computer-use on Child-health in the Arab-community
Bibliographic record
Abstract
United States of America (USA), Australia, and Canada recommending children computer-use should be imperfect. It obligated a strong influence on children’s lives. The problem was for stuck in the computer-use for a long time per day. The goal was to follow child-health in the Arab-community to notice its influence on the child-health. The method was through a arithmetical study by sending Internet questionnaire and receiving the parents' advice. The results were in the 500 parent answers and 14 comments. The first was "You have a child up to 13 years old?"; 83.6%. The second was "Your child used a computer?"; 80.8%. The third was "Your child used the computer for a long time?"; 67.9%. The fourth was "The computer caused a health problem for your child?"; 70.3%. The fifth was "The computer caused your child mental disorder?"; 68.5%. The sixth was "The computer caused your child trouble seeing?"; 77.2%. The seventh was "The computer caused your child trouble sleeping?"; 72.0%. The eighth 8 was "The computer caused your child feeding problem?"; 69.5%. The ninth was "The computer caused your child family relationship problem?", 72.0%. The tenth was "Prevent your child from using the computer to protect his health?"; 79.0%. It was concluded that the extent of the harms of child computer-use from a health and psychological point. They have young and sensitive tissues. The computer-use affected them, child-health. It was recommended the parents instructions will protect children from the computer-use for long periods. That will reduce child-health and psychological problems.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".