Hubungan Perawatan Botol Susu Dan Perilaku Mencuci Tangan Dengan Kejadian Diare Pada Batita Di Wilayah Kerja Puskesmas Delanggu
Bibliographic record
Abstract
Diarrhea was still one of the major causes of morbidity and mortality in almost all geographic regions of the world and all age groups can be attacked by diarrhea. Diarrhea was a risk factor for behavioral factors such as the use of milk bottles were not clean. The rest of the milk in the bottle will be exposed to bacteria from saliva and mouth of a child less than three years. The aim of this study treatment relationship bottles and wash your hands and healthy behaviors in family structure with diarrhea in child under three years in Puskesmas Delanggu. This study was a retrospective descriptive correlative approach. Sample research was the mother and the child under three years were recorded in health centers Delanggu in September 2015 that as many as 142 respondents with a proportional random sampling technique. Collecting data using questionnaires were analyzed using Chi Square test. Conclusion of the study are (1) behavioral treatment bottle of milk in child under three years are mostly bad (45%), (2) hand washing mothers with child under three years majority were sufficient (51%), (3) there was a relationship behavioral treatments milk bottle with the incidence of diarrhea in child under three years in Puskesmas Delanggu where the better behavioral treatments milk bottle, the incidence of diarrhea was getting low, and (4) there was a relationship hand washing with diarrhea in child under three years in Puskesmas Delanggu where the better hand washing, the incidence of diarrhea lower. \n \nKeywords: nursing bottles, handwashing, diarrhea, child under three years
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".