Breastfeeding Barriers for Preterm Infants in Neonatal Intensive Care Unit Environments: A Systematic Assessment and Meta-Analysis
Bibliographic record
Abstract
Background: Breast milk is vital for the growth and development of preterm infants. However, in Neonatal Intensive Care Units (NICUs), mothers often encounter significant challenges in breastfeeding. Objective: This study aims to systematically evaluate the barriers to breastfeeding in NICUs, thereby providing evidence-based support for clinical practices. Methods: A comprehensive search was conducted in the Cochrane Library, PubMed, Web of Science, Embase, and Scopus databases, up to September 2023. Meta-analysis was performed using Stata 15.0, applying fixed or random effects models to calculate odds ratios (OR) and their 95% confidence intervals (CI). Study quality was assessed using the Newcastle–Ottawa Scale for cases and cohorts and the Agency for Healthcare Research and Quality standards for cross-sectional studies. Heterogeneity was evaluated using Cochran’s chi-squared test (Cochran’s Q) and I 2 statistics, and publication bias was assessed through funnel plots and symmetry tests. Results: A total of 32 studies were included, encompassing 96,053 preterm infants. The main barriers to breastfeeding in preterm infants included: low gestational age (OR = 1.36, 95% CI: 1.06–1.75), lower maternal education (OR = 1.64, 95% CI: 1.39–1.93), insufficient breast milk (OR = 2.09, 95% CI: 1.39–1.93), multiple births (OR = 1.615, 95% CI: 1.18–2.210), smoking (OR = 2.906, 95% CI: 2.239–3.771), and single motherhood (OR = 1.439, 95% CI: 1.251–1.654). Conclusion: This study underscores the need for individualized breastfeeding support strategies in NICUs, taking into account the diverse backgrounds of mothers. Future research should focus on unraveling the underlying mechanisms affecting breastfeeding in preterm infants, with the goal of enhancing breastfeeding rates and improving developmental 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 | 0.017 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.053 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".