Breastfeeding assessment tools for at-risk and malnourished infants aged under 6 months old: a systematic review
Bibliographic record
Abstract
Background: Many small and malnourished infants under 6 months of age have problems with breastfeeding and restoring effective exclusive breastfeeding is a common treatment goal. Assessment is a critical first step of case management, but most malnutrition guidelines do not specify how best to do this. We aimed to identify breastfeeding assessment tools for use in assessing at-risk and malnourished infants in resource-poor settings. Methods : We systematically searched: Medline and Embase; Web of Knowledge; Cochrane Reviews; Eldis and Google Scholar databases. Also the World Health Organization (WHO), United Nations International Children’s Emergency Fund (UNICEF), CAse REport guidelines, Emergency Nutrition Network, and Field Exchange websites. Assessment tool content was analysed using a framework describing breastfeeding ‘domains’ (baby’s behaviour; mother’s behaviour; position; latching; effective feeding; breast health; baby’s health; mother’s view of feed; number, timing and length of feeds). Results : We identified 29 breastfeeding assessment tools and 45 validation studies. Eight tools had not been validated. Evidence underpinning most tools was low quality and mainly from high-income countries and hospital settings. The most comprehensive tools were the Breastfeeding, Evaluation and Education Tool, UNICEF Baby-Friendly Hospital Initiative tools and CARE training package. The tool with the strongest evidence was the WHO/UNICEF B-R-E-A-S-T-Feed Observation Form. Conclusions : Despite many possible tools, there is currently no one gold standard. For assessing malnourished infants in resource-poor settings, UNICEF Baby-Friendly Hospital Initiative tools, Module IFE and the WHO/UNICEF B-R-E-A-S-T-Feed Observation Form are the best available tools but could be improved by adding questions from other tools. Allowing for context, one tool for rapid community-based assessment plus a more detailed one for clinic/hospital assessment might help optimally identify breastfeeding problems and the support required. Further research is important to refine existing tools and develop new ones. Rigorous testing, especially against outcomes such as breastfeeding status and growth, is key.
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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 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".