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Record W2951253349 · doi:10.1093/cdn/nzz034.p10-004-19

Patterns in the Risk Factors of Stunting Among Children 0 to 24 and 25 to 59 Month Old in Ethiopia: Evidence from the 2016 National Survey (P10-004-19)

2019· article· en· W2951253349 on OpenAlexaff
Tafere Gebreegziabher, Nigatu Regassa

Bibliographic record

VenueCurrent Developments in Nutrition · 2019
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMedicineDemographyBody mass indexMicronutrientAge groupsEnvironmental healthPediatrics

Abstract

fetched live from OpenAlex

The objective of this study was to examine the prevalence and patterns of individual and household level factors on stunting among two groups of under five children in Ethiopia. The study employed a quantitative cross-sectional design based on a nationally representative data. It included 4199 young children (age 0–24 months) and 5497 children (age 25–59 months), giving a total of 9696 children. Prevalence of stunting was 29% among the younger age group (age 0–24 months) and 47% among the older group (age 25–59 months). Being female, intake of multiple micronutrients, households having piped source of drinking water, high maternal Body Mass Index (BMI), household non-monetary wealth, and maternal education were associated with decreased likelihood of stunting in both groups. On the other hand, children who were anemic, small birth weight, drank from bottle, and children of stunted and working mother resulted in higher likelihood of stunting in both groups (P < 0.05). While most predictors and/or risk factors of stunting followed similar pattern across the two groups, child factors had higher leverage in the younger than the older groups. Multiple set of factors predicted childhood stunting among the young and older children in Ethiopia. The study underscores the importance of intervening in the first 1000 days through promoting maternal education, maternal-child health services, mother’s nutrition, and improving the intra household food distribution and good eating habits. No source of funding.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.053
GPT teacher head0.322
Teacher spread0.269 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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
Published2019
Admission routes1
Has abstractyes

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