The Relationship between giving formula milk and the incidence of diarrhea in babies 0-6 months in the Work Area of Batangtoru Public Health Center in 2023
Bibliographic record
Abstract
ABSTRACT Giving formula milk too early will also reduce breast milk consumption, and if it is too late it will cause the baby to be malnourished and feeding at an early age will result in the baby's digestive ability not being ready to accept additional food. The problem of giving formula milk is greatly influenced by the baby's health behavior such as diarrhea. The mother's knowledge about giving formula milk and having a good attitude in giving formula milk can determine the best development for her child. The aim of this research is to determine the relationship between breastfeeding mothers regarding giving formula milk to babies 0-6 months with the incidence of diarrhea in the Batangtoru Community Health Center Work Area in 2023. This type of research is quantitative with a cross sectional approach method. The population in this study were all mothers who had babies aged 0-6 months, totaling 49 mothers. Because the population is less than 50 people, the sampling technique uses a total sampling technique. Chi Square Test results obtained p=0.000 (<0.05). So the conclusion is that there is a relationship between giving formula milk and the incidence of diarrhea in babies 0-6 months in the Batangtoru Health Center Working Area. 21 people were given formula milk, 21 people had diarrhea. It is recommended that the results of this study can provide information to respondents regarding knowledge of giving formula milk to babies 0-6 months with the incidence of diarrhea. Keywords: Formula feeding, incidence of diarrhea, babies 0-6 months
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".