Barriers and facilitators to breastfeeding support practices in a neonatal intensive care unit in Colombia
Bibliographic record
Abstract
OBJECTIVES: To assess breastfeeding support practices and related barriers and facilitators in a large Intensive Care Unit, Neonatal (NICU) in Medellín, Colombia, as part of a broader quality improvement initiative to enhance breastfeeding support. METHODS: A mixed-methods descriptive design was used to collect data on care practices and outcomes related to NICU breastfeeding support. Data sources included the Neo-BFHI's self-assessment questionnaire of breastfeeding policies and practices, clinical observations, and a retrospective review of 51 patient charts. RESULTS: Of the 51 charts reviewed, 98% of the infants received breastmilk during their hospitalization but the majority (84%) also received formula and only 8% of infants were exclusively breastfed at the time of NICU discharge. All NICU staff received education on mother and baby-friendly care, and the unit complied with the International Code of Marketing of Breast-milk substitutes. However, resources to support lactation (e.g., access to breastfeeding specialists, breast pumps, written teaching materials for parents) were limited, and infants were only allowed to consume milk expressed within the hospital. Mother-infant separation, as well as staff beliefs and care routines, also limited important breastfeeding support practices such as skin-to-skin care and early initiation of direct breastfeeding. CONCLUSIONS: The self-assessment questionnaire and observations revealed a high value for breastfeeding and a family-centered approach to care in the NICU. Key challenges to sustaining breastfeeding in the NICU included a lack of facilities for supporting parental presence, barriers to expression and provision of mother's milk, and a high rate of bottle-feeding with formula.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".