Bibliographic record
Abstract
BACKGROUND: Neonatal abstinence syndrome (NAS) is a growing problem in the United States, affecting 32,000 infants annually. Although breastfeeding would benefit infants with NAS, rates among these mothers are low. PURPOSE: The purpose of this quality improvement project was to increase breastfeeding rates and decrease hospital length of stay (LOS) for infants with NAS through prenatal breastfeeding initiatives. METHOD: A pre-/postquality improvement design was used to assess the relationship between breastfeeding initiatives on breastfeeding rates and LOS in infants with NAS. A 3-class curriculum was offered to pregnant women at risk for delivering an infant with NAS. Chart review was completed for all infants evaluated for NAS in a hospital at baseline (n = 56), after Baby Friendly Status (BFS) (n = 75), and after BFS plus breastfeeding education (n = 69). RESULTS: Although not statistically significant, the BFS plus breastfeeding education cohort had the largest percentage of exclusively breastfed infants during hospitalization (24.6%) and at discharge (31.9%). There was a statistically significant decrease in LOS (P < .001) between cohorts. IMPLICATIONS FOR PRACTICE: The small sample made it not possible to infer direct impact of the intervention. However, results suggest that prenatal education may contribute to an increase in the numbers of infants with NAS who receive human milk and a decrease in hospital LOS. IMPLICATION FOR RESEARCH: Refinement of best practices around breastfeeding education and support for mothers at risk of delivering an infant with NAS is recommended so that breastfeeding may have the greatest impact for this subgroup of women and their infants.
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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 | 0.006 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.003 |
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".