Assessment of nutritional status of children of pastoralists in a humanitarian setting: A Cross-sectional Standardized Monitoring and Assessment of Relief and Transitions Survey
Bibliographic record
Abstract
The AVSI Foundation in collaboration with the South Sudan Nutrition Cluster conducted a SMART nutrition and mortality survey covering all the eight Payams of the former Ikwoto County in the former Eastern Equitoria State. The main objective of the survey was to assess the current prevalence of acute malnutrition and retrospective mortality rates in the County. The Standardized Methodology for Assessment in Relief and Transitions (SMART) which applies a two-stage cluster sampling was used. A total of 623 children aged 6-59 months from 532 households in 36 clusters were sampled for anthropometric measurements. The mortality assessment was conducted concurrently in all 532 households. Additional information on Infant and Young Child Feeding practices (IYCF) was collected in the 532 households visited to provide more insight into possible risk factors associated with the high acute malnutrition prevalence. The prevalence of Global Acute Malnutrition (GAM) defined as Weight-for-Height 80%. Another 76.2% of children (0-23.9 months) were initiated to breastfeeding within one hour after delivery. Introduction of solid, semi-solid or soft foods among children aged 6 to 8.9 months was low (51.5%). Findings indicated suboptimal IYCF practices.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".