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Record W4403273557 · doi:10.1080/20421338.2024.2398294

Determinants of nutrition security status of women in rural households in Northwest Ethiopia

2024· article· en· W4403273557 on OpenAlexaff
Fentaw Teshome Dagnaw, Adino Andaregie, Tess Astatkie

Bibliographic record

VenueAfrican Journal of Science Technology Innovation and Development · 2024
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsDalhousie University
Fundersnot available
KeywordsFood securitySocioeconomicsGeographyRural areaEnvironmental healthEconomic growthBusinessAgricultural economicsEconomicsPolitical scienceMedicineAgriculture

Abstract

fetched live from OpenAlex

The nutrition security of women is critically important for society’s well-being. Therefore, this study was conducted to determine the determinants of the nutrition security status of rural women in Northwest Ethiopia. Data collected from rural women in 197 randomly selected households were analyzed using a binary logit regression model. The results show that 72.6% of the women are nutritionally insecure. The determinants that significantly affect nutrition security status are family size (a negative effect), women’s daily feed frequency, milk consumption, feeding habits of fruits and vegetables, feeding habits of animal products, and women empowerment (all positive effects). The weight, height, and BMI of nutritionally insecure women were significantly lower than those of nutritionally secure women. These findings reveal the need for government and other stakeholders’ interventions to increase access to nutritious food products and to provide training on feeding culture and dietary diversity to women. The findings of this study can help the Government of Ethiopia to focus on the significant determinants to achieve its National Development Priorities focusing on the Sustainable Development Goals (SDGs) of the UN, particularly Goals 2, 3, and 5.

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)0.003
metaresearch head score (Gemma)0.000
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 score0.049
Threshold uncertainty score0.423

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.006
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.072
GPT teacher head0.403
Teacher spread0.332 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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