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Record W6929001914 · doi:10.3886/e108684v1

Assessment of nutritional status of children of pastoralists in a humanitarian setting: A Cross-sectional Standardized Monitoring and Assessment of Relief and Transitions Survey

2017· dataset· en· W6929001914 on OpenAlexaff

Bibliographic record

VenueICPSR Data Holdings · 2017
Typedataset
Languageen
FieldDecision Sciences
TopicPsychometric Methodologies and Testing
Canadian institutionsHealth Research Foundation
Fundersnot available
KeywordsMalnutritionBreastfeedingAnthropometryCluster (spacecraft)PastoralismCluster samplingSystematic samplingMalnutrition in children

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.021
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.099
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.439
GPT teacher head0.550
Teacher spread0.111 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreDataset

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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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