A critical appraisal of guidelines used for management of severe acute malnutrition in South Africa’s referral system
Bibliographic record
Abstract
BACKGROUND: Focusing on healthcare referral processes for children with severe acute malnutrition (SAM) in South Africa, this paper discusses the comprehensiveness of documents (global and national) that guide the country's SAM healthcare. This research is relevant because South African studies on SAM mostly examine the implementation of WHO guidelines in hospitals, making their technical relevance to the country's lower level and referral healthcare system under-explored. METHODS: To add to both literature and methods for studying SAM healthcare, we critically appraised four child healthcare guidelines (global and national) and conducted complementary expert interviews (n = 5). Combining both methods enabled us to examine the comprehensiveness of the documents as related to guiding SAM healthcare within the country's referral system as well as the credibility (rigour and stakeholder representation) of the guideline documents' development process. RESULTS: None of the guidelines appraised covered all steps of SAM referrals; however, each addressed certain steps thoroughly, apart from transit care. Our study also revealed that national documents were mostly modelled after WHO guidelines but were not explicitly adapted to local context. Furthermore, we found most guidelines' formulation processes to be unclear and stakeholder involvement in the process to be minimal. CONCLUSION: In adapting guidelines for management of SAM in South Africa, it is important that local context applicability is taken into consideration. In doing this, wider stakeholder involvement is essential; this is important because factors that affect SAM management go beyond in-hospital care. Community, civil society, medical and administrative involvement during guideline formulation processes will enhance acceptability and adherence to the guidelines.
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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 | 0.277 | 0.547 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.028 | 0.019 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.005 | 0.008 |
| 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".