Infants admitted to neonatal units – interventions to improve breastfeeding outcomes: a systematic review 1990–2007
Bibliographic record
Abstract
This review aimed to identify interventions to promote breastfeeding or breast milk feeding for infants admitted to the neonatal unit. The medical electronic databases were searched for papers listed between 1990 and June 2005 which had breastfeeding or breast milk as an outcome and which targeted infants who had been admitted to a neonatal unit, thus including the infant and/or their parents and/or neonatal unit staff. Only papers culturally relevant to the UK were included resulting in studies from the USA, Canada, Europe, Australia and New Zealand. This search was updated in December 2007 to include publications up to this date. We assessed 86 papers in full, of which 27 ultimately fulfilled the inclusion criteria. The studies employed a range of methods and targeted different aspects of breastfeeding in the neonatal unit. Variations in study type and outcomes meant that there was no clear message of what works best but skin-to-skin contact and additional postnatal support seemed to offer greater advantage for the infant in terms of breastfeeding outcome. Galactogogues for mothers who are unable to meet their infants' needs may also help to increase milk supply. Evidence of an effect from other practices, such as cup-feeding on breastfeeding was limited; mainly because of a lack of research but also because few studies followed up the population beyond discharge from the unit. Further research is required to explore the barriers to breastfeeding in this vulnerable population and to identify appropriate interventions to improve breastfeeding outcomes.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".