Carer and staff perspectives on supplementary suckling for treating infant malnutrition: qualitative findings from <scp>M</scp>alawi
Bibliographic record
Abstract
Severe acute malnutrition (SAM) in infants aged <6 months is a major global health problem. Supplementary suckling (SS) is widely recommended as an inpatient treatment technique for infant <6 months SAM. Its aim is to re-establish effective exclusive breastfeeding. Despite widespread support in guidelines, research suggests that field use of SS is limited in many settings. In this study, we aimed therefore to describe and understand the barriers and facilitating factors to SS as a treatment technique for infant SAM. We conducted qualitative interviews and focus group discussions in a hospital setting in Blantyre, Malawi, with ward staff and caregivers of infants <2 years. We created a conceptual framework based on five major themes identified from the data: (1) motivation; (2) breastfeeding views; (3) practicalities; (4) understanding; and (5) perceptions of hospital-based medicine. Within each major theme, more setting-specific subthemes can also be developed. Other health facilities considering SS roll-out could consider their own barriers and facilitators using our framework; this will facilitate the implementation of SS, improve staff confidence and therefore give SS a better chance of success. Used to shape and guide discussions and inform action plans for implementing SS, the framework has the potential to facilitate SS roll-out in settings other than Malawi, where this study was conducted. We hope that it will help pave the way to more widespread SS, more research into its use and effectiveness, and a stronger evidence-base on malnutrition in infants aged <6 months.
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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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".