Familiar but neglected: identification of gaps and recommendations to close them on exclusive breastfeeding support in health facilities in Malawi
Bibliographic record
Abstract
BACKGROUND: Exclusive breastfeeding is widely accepted as a key intervention with proven efficacy for improving newborn survival. Despite international commitments and targets to support and promote breastfeeding, there are still gaps in meeting and maintain coverage in many sub-Saharan African countries. This paper aimed to triangulate the perspectives of health workers, mothers, and their family members with facility assessments to identify gaps to improve breastfeeding support in in Malawi. METHODS: The study on breastfeeding barriers and facilitators was conducted in 2019 at one tertiary hospital and three secondary-level hospitals in Malawi. We conducted 61 semi-structured interviews with health workers, postnatal mothers, grandmothers, aunts, and fathers. In 2017, we carried out a neonatal care facility assessment using the World Health Organization (WHO) Integrated Maternal, Neonatal, and Child Quality of Care Assessment and Improvement Tool. Qualitative data were analysed using a thematic analysis approach within the Systems Framework for Health Policy. RESULTS: The district-level hospitals rated high with an average score of 4.8 out of 5 across the three facilities indicating that only minor improvements are needed to meet standards of care for early and exclusive breastfeeding. However, the score fell to an average of 3.5 out of 5 for feeding needs with sick neonates indicating that several improvements are needed in this area. The qualitative data demonstrated that breastfeeding was normalized as part of routine newborn care. However, the focus on routine practice and reliance on breastfeeding knowledge from prenatal counselling highlights inequities and neglect in specialized care and counselling among vulnerable mothers and newborns. Revitalisation of breastfeeding in Malawian facilities will require a systems approach that reinforces policies and guidelines; contextualises knowledge; engagement and empowerment of other relatives to the baby and task-sharing among health workers. CONCLUSIONS: Breastfeeding is accepted as a social norm among health workers, mothers, grandmothers, aunts, and fathers in Malawi, yet vulnerable groups are underserved. Neglect in breastfeeding support among vulnerable populations exacerbates health inequities. Health systems strengthening related to breastfeeding requires a concerted effort among health workers, mothers, grandmothers, aunts, and fathers while remaining grounded in contexts to support family-centered hospital care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".