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Record W2593016964

Mothers’ and Households’ Food Security Status in Kangai and Mutithi Locations of Mwea West Sub County, Kenya

2016· article· en· W2593016964 on OpenAlexaboutno aff
Rahab M. Mugambi, Jasper K. Imungi, Judith Waudo, A. Ondigi

Bibliographic record

VenueFood science and quality management · 2016
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsnot available
Fundersnot available
KeywordsFood securityFood insecurityGeographyEnvironmental healthSocioeconomicsPopulationMedicineAgricultureEconomics
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this study was to investigate household’s food security status.  The study was carried out in dry harvesting and wet planting seasons in the two locations of Mwea West Sub County, Kenya, namely, Kangai and Mutithi.  The study design was comparative cross sectional survey while the data instrument was a structured researcher administered household questionnaire. Sampling techniques  included probability proportionate to population, The data were analyzed by the use of Health Canada’s, Household Food Security Survey Model (HFSSM), On the whole, the findings were that 39% of the households were food secure, 21 % were moderately insecure, while 40 % were severely food insecure. The general conclusion was that in as much as the households in the two locations were significantly different in terms of households’ and mothers’ food security status, they both experienced chronic food insecurity which did not change with the season. The study recommends food intervention for the 40 % of households that are severely food insecure. Keywords: Food Security Status, Households’ Food Security, Mothers’ Food Security, Health Canada

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 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)0.000
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 score0.293
Threshold uncertainty score0.582

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.166
GPT teacher head0.421
Teacher spread0.256 · 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 source (direct Gemma or distilled Codex), 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
Published2016
Admission routes1
Has abstractyes

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