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Record W4214672675 · doi:10.1186/s12889-022-12772-2

Gender differences in nutritional status and determinants among infants (6–11 m): a cross-sectional study in two regions in Ethiopia

2022· article· en· W4214672675 on OpenAlexafffund
Aregash Samuel, Saskia Osendarp, Edith J. M. Feskens, Azeb Lelisa, Abdulaziz Adish, Amha Kebede, Inge D. Brouwer

Bibliographic record

VenueBMC Public Health · 2022
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsNutrition International
FundersGlobal Affairs CanadaWageningen University and ResearchUNICEFNederlandse Organisatie voor Wetenschappelijk OnderzoekGovernment of the United KingdomNutricia Research FoundationWorld Health Organization
KeywordsWastingBreastfeedingMedicineResidenceBiostatisticsCross-sectional studyDemographyLogistic regressionPublic healthMicronutrientConfoundingEnvironmental healthSocioeconomic statusBreast feedingPediatricsPopulation

Abstract

fetched live from OpenAlex

BACKGROUND: A limited number of studies suggest that boys may have a higher risk of stunting than girls in low-income countries. Little is known about the causes of these gender differences. The objective of the study was to assess gender differences in nutritional status and its determinants among infants in Ethiopia. METHODS: We analyzed data for 2036 children (6-11 months old) collected as the baseline for a multiple micronutrient powders effectiveness study in two regions of Ethiopia in March-April 2015. Child, mother, and household characteristics were investigated as determinants of stunting and wasting. Multiple logistic regression models were used separately for boys and girls to check for gender differences while adjusting for confounders. The study is registered at http://www.clinicaltrials.gov/ with the clinical trials identifier of NCT02479815. RESULTS: Stunting and wasting prevalence is significantly higher among boys compared to girls, 18.7 vs 10.7% and 7.9 vs 5.4%, respectively. Untimely initiation of breastfeeding, not-exclusive breastfeeding at the age of 6 months, region of residence, and low maternal education are significant predictors of stunting in boys. Untimely introduction to complementary food and low consumption of legumes/nuts are significant predictors of stunting in both boys and girls, and low egg consumption only in girls. Region of residence and age of the mother are significant determinants of wasting in both sexes. Analysis of interaction terms for stunting, however, shows no differences in predictors between boys and girls; only for untimely initiation of breastfeeding do the results for boys (OR 1.46; 95%CI 1.02,2.08) and girls (OR 0.88; 95%CI 0.55,1.41) tend to be different (p = 0.12). CONCLUSION: In Ethiopia, boys are more malnourished than girls. Exclusive breastfeeding and adequate dietary diversity of complementary feeding are important determinants of stunting in boys and girls. There are no clear gender interactions for the main determinants of stunting and wasting. These findings suggest that appropriate gender-sensitive guidance on optimum infant and young child feeding practices is needed.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalhigh
models agreeAgreement compares identical category sets and study designs across arms.

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.106
GPT teacher head0.391
Teacher spread0.285 · 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

Labeled directly by 2 models reading the full record.

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

Citations40
Published2022
Admission routes2
Has abstractyes

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