Expressing Human Milk in the NICU
Bibliographic record
Abstract
BACKGROUND: Human milk has multiple benefits for human health; however, rates of infants receiving human milk at discharge in Canadian neonatal intensive care units (NICUs) are far below recommendations of the Baby Friendly Hospital Initiative supported by the Canadian Pediatric Association. Mothers of infants requiring NICU care usually need to express their milk, especially mothers of premature infants, since for some time their infant is unable to feed directly at the breast. Expressing human milk for an extended period can be challenging for mothers. PURPOSE: To document maternal experiences expressing human milk for their infant in the NICU as a closeness or separation experience, and to discover what factors gave rise to these perceptions. METHODS: In this descriptive qualitative cross-sectional study, 15 mothers whose infants were hospitalized in a level III NICU and who were expressing human milk audio recorded their thoughts and feelings with a smartphone application for 48 hours while they were expressing milk. A thematic content analysis was used to analyze data. FINDINGS: Expressing human milk for their hospitalized infant was a difficult experience for all mothers, and most described both closeness and separation feelings while expressing milk. Their feelings fluctuated depending on their coping mechanisms, perceived challenges, as well as their location and environment. Mothers were all pursuing their goal of continued milk expression with various levels of motivation. IMPLICATIONS FOR PRACTICE: NICUs should adopt a family-centered approach to care whereby mothers' care needs related to milk expression are addressed. Nurses can ask open-ended questions to explore how mothers are coping, and they can assist mothers to find effective coping strategies to minimize the challenges related to milk expression to promote mother-infant closeness. IMPLICATIONS FOR RESEARCH: The impact of environment and location on mothers with hospitalized infants should be explored, as unit designs could be altered to foster closeness.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".