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Record W3102547876 · doi:10.1016/j.dib.2020.106542

Survey data on income, food security, and dietary behavior among women and children from households of differing socio-economic status in urban and peri-urban areas of Nairobi, Kenya

2020· article· en· W3102547876 on OpenAlexfundno aff
Fridah Nyakundi, Mercy Mutua, Mercy G Lung' aho, Christine K Chege, John Ndung' u, Rhoda Nungo, David Karanja

Bibliographic record

VenueData in Brief · 2020
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsnot available
FundersConsortium of International Agricultural Research CentersAustralian Centre for International Agricultural ResearchInternational Development Research Centre
KeywordsFood securityLivelihoodHousehold incomeEnvironmental healthPovertySocioeconomicsSocioeconomic statusGeographyVulnerability (computing)Food insecurityConsumption (sociology)Index (typography)PopulationBusinessEconomicsEconomic growthMedicineAgriculture

Abstract

fetched live from OpenAlex

This article describes data collected to analyze consumer behaviors in vulnerable populations by examining key access constraints to nutritious foods among households of differing socio-economic status in urban and peri‑urban areas of Nairobi, Kenya. The key variables studied include wealth status, food security, and dietary behavior indicators at individual and household level. Household food insecurity access scale (HFIAS), livelihood coping strategies (LCS), food expenditure share (FES), food consumption score (FCS), household dietary diversity score (HDDS), minimum dietary diversity-women(MDD-W), and child dietary diversity score (CDDS) indicators were used to measure food security. Household assets were used to develop an asset-based wealth index that grouped the study sample population into five wealth quantiles, while income levels were used to estimate FES. The hypothesis that guided the cross-sectional survey conducted to generate these data is that vulnerability to food insecurity and poverty are important drivers of food choice that influence household and individual dietary behavior. Data from this study was thus used to assess direction and strength of association between; household food insecurity, wealth status, women, children, and household dietary behavior in both urban and peri‑urban populations sampled.

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)0.000
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 score0.033
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.038
GPT teacher head0.262
Teacher spread0.224 · 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
Published2020
Admission routes1
Has abstractyes

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