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Record W4387778921 · doi:10.1002/wfp2.12063

Women's agency in nutrition in the association between women's empowerment in agriculture and food security: A case study from Uganda

2023· article· en· W4387778921 on OpenAlexaff
Farzaneh Barak, Jackson Efitre, Robinson Odong, Hugo Melgar‐Quiñonez

Bibliographic record

VenueWorld Food Policy · 2023
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsMcGill University
Fundersnot available
KeywordsFood securityEmpowermentAgency (philosophy)AgricultureWomen's empowermentBusinessEnvironmental healthSocioeconomicsEconomic growthEconomicsGeographyMedicineSociology

Abstract

fetched live from OpenAlex

Abstract This study examined the relationship between women's empowerment in agriculture (WEA), women's agency in nutrition, and their food security. It aimed to quantify the moderating effect of women's agency in nutrition on the association between WEA and food security. Data from the NutriFish project, a gender‐ and nutrition‐sensitive agricultural intervention in fishing villages in Uganda, were utilized. The study included 380 primary Ugandan female decision makers in dual adult households. WEA was measured using the Project‐level Women's Empowerment in Agriculture Index (pro‐WEAI). Women's agency in nutrition was assessed through measures of agency in regular diet, pregnancy diet, breastfeeding diet, and food purchase. Binary logit regression models were employed to estimate differential associations between WEA and food security, testing three‐way interactions between WEA, agency in regular diet, and food purchase. Results showed that WEA was associated with a 0.18 increase in the predicted probability of food security ( p < .01). Women's participation in food purchase decisions strengthened the WEA‐food security association by 0.33 ( p < .05). The results suggested that promoting women's food purchase agency can enhance the positive link between WEA and food security. Prioritizing interventions empowering women in food purchase decisions improves food security in gender‐ and nutrition‐sensitive programs.

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)0.000
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 score0.274
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
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 head0.017
GPT teacher head0.284
Teacher spread0.267 · 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

Citations9
Published2023
Admission routes1
Has abstractyes

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