Assessment of Mothers Education and their Knowledge about Home-Accident among Early Childhood Age Group
Bibliographic record
Abstract
Objectives: The study was carried assessment of mothers education and their knowledge about home accidents prevention among early childhood age group. Methodology: A descriptive study design was achieved in 10-30 October 2015. A non-probability accidental sample of 113 Mothers who agreed to participate in the study & have a child or more live together at the same house and the sample was selected of health centers when visiting for certain follow-up to their children in the Babylon city, middle of Iraq, Through using questionnaire that constructed by researchers to collect data available for the purpose of the study through using descriptive and inferential statistics according to the aims of the study. Results: After completion of 113 questionnaire, the results were revealed that majority of mother's age were 44 (38.9%) aged between 22-28 years old while their education were 58 (51.3) in primary level, for this reason the majority of the sample 96 (85%) were unemployed. The study reveals more than quarter of the sample 39.8% as 45 within group of 61-70 score of moderate knowledge with optimum level contrasted. Conclusions: The present study reveals quarter of the sample had moderate knowledge with optimum level contrasted and few of them have poor knowledge regarding to accidents avoidance of kids under five as well as no correlation between mother's knowledge with their age and education. Recommendations: Health education program about explanations for home accidents, medical aid managements and technique for avoidance into the educational modules at various levels were recommended.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".