Factors For Cessations of Exclusive Breast Feeding at End Of 6 Weeks in Healthy Term and Late Preterm Neonates Born in A Hospital Set Up in North India
Bibliographic record
Abstract
Background: Millions of deaths can be averted by scaling up exclusive breast feeding, EBF rate. By end of 2 months only 69% of mothers and by 4-6 months only one quarter of mothers are giving EBF to their babies. Factors influencing non-initiation or early cessation of exclusive breastfeeding had already been evaluated under NFHS data but only for babies born in community settings, these factors need to be investigated for the neonates born out of community settings. Objectives: To identify risk factors for early cessation of exclusive breast-feeding. Setting: Teaching Hospital in north India. Subjects: Mothers giving birth to term or late pre-term healthy neonates were enrolled within 48 h after delivery and followed up till 6th postnatal week. Results: A total of 350 mothers were enrolled. At the end of 1st, 2nd and 6th week the proportion of EBF decreased to 95%, 93.3% and 75.8% respectively. Risk factors associated with early cessation of exclusive breastfeeding were cesarean section at end of six weeks was primiparous mothers (RR: 1.95, 95%CI: 1.01-3.79). Conclusion: First time mothers need more counseling on exclusive breast-feeding.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".