Relationship between Mothers’ Socio Demographic Characteristics and Food Security Status in Kangai and Mutithi Locations of Mwea West Sub County, Kenya
Bibliographic record
Abstract
The purpose of the study was to determine the relationship between mothers’ socio demographic characteristics and food security status in Kangai and Mutithi Locations of Mwea West Sub County, Kenya. The design was cross sectional survey while the data instrument was a structured researcher administered household questionnaire. Sampling techniques included probability proportionate to population, The Socio Demographic data were analyzed by the use of proportions and t-tests while food security status data were analyzed by the use of Health Canada’s, Household Food Security Survey Model (Health Canada, 2012). Logistical regression model was used to determine the relationship between Socio Demographics and Food Security Status. It was found out that the socio demographics of the mothers in the two locations were significantly different. The house hold food security status for the Sub County was that 39% of households were food secure, 21% were moderately food insecure while 40% were severely food insecure. Gender of the household head, marital status, religion, age, occupation, education, income sources, expenditure on food and land size were the most pronounced proxy indicators for food security status in the Sub County and they underscore the poverty levels in the area. Further research is suggested on possible interventions for food insecurity in the sub county. Keywords: Food Security Status, Socio Demographic Characteristics, Socio Economic Characteristics, Poverty
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".