Traditional Practices of Mothers’ About Child Health
Bibliographic record
Abstract
Purpose: This study was conducted to determine the traditional practices by mother regarding child health and their traditional practices to be effective socio- demographic factors. Method: The sampling of the descriptive and cross-sectional study was constituted by120 mothers, who applied to policlinics and services of Ege University Child Hospital between February-April 2008; has a child with 0-6 years of age. As data collection tool, contained 12 items of “Questionnaire Form for Traditional Practices Interest in Child Health” constituted by reviewing the relevant literature was used. Obtained data were evaulated in SPSS program. Results: It was detected that mothers most commonly use the methods of Muslim call to the ear of newborn (81.7%), having someone to pray for devil-eye (75%), putting blue bead on the baby to avert the devil eye (58.3%), salting the baby to prevent the baby from smelling bad (49.2%), having someone pray for the baby crying continuously (46.7%), putting amulets and pray papers to avert the devil eye (45.8%), and using yellow cloths and covers in an effort to prevent jaundice (38.3%). Conclusion: As a result; it was detected that traditional practices are still widely used in child health but these traditional practices in general is not harmful to child health was seen to be practices. It was seen that educational level, economic status and properties of their living area of mothers affected the traditional practices in child care.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".