Early Initiation of Breastfeeding and Exclusive Breastfeeding: A Case Study of Breastfeeding Mothers in Takalar District
Bibliographic record
Abstract
The coverage of exclusive breastfeeding in the Takalar District in 2011 was around 57.3% to 75.4% which means that people in Takalar District still needs exclusive nutrition and breastfeeding handling. In addition, Early initiation of breastfeeding (EIB) and exclusive breast milk from birth to the age of six months are two important breastfeeding practices in reducing infant mortality rate and increasing exclusive breastfeeding success. This study aims to look at the condition of early initiation of breastfeeding (EIB) and exclusive breastfeeding among breastfeeding mothers in Takalar District. This study was a descriptive study investigating some variables such as socio-demographics, pregnancy history, and early initiation breastfeeding practice. The population consisted of all breastfeeding mothers and samples were chosen randomly (56 subjects). Study findings indicate that the majority of the mothers had been successful in providing exclusive breast milk (92.9%). This indicates a very good situation. When viewed from the EIB implementation factor, the proportion of mothers giving birth who had practiced EIB was still low (26.8%). This indicated that there were respondents who were not successful in practicing EIB but successful in exclusive breastfeeding.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".