Availability, Accessibility, and Diversity of The Highly Consumed Foods in A Rural Setting in Western Kenya
Bibliographic record
Abstract
Drift in the consumption of particular foods in a setting affects the pillars of food security. To avert the drift that results in adverse nutritional outcomes, there is a need to assess the consumption of highly consumed foods in a particular area for an evidence-based approach to policy formulation. This study was undertaken to determine access to food, own food production, market access, and consumption patterns of foods, as well as the diversity of highly consumed foods in rural areas of Western Kenya. The study employed a cross-sectional design incorporating purposive and multi-stage simple random sampling and was analyzed using Python software. The data was presented in tables and charts. From the results, 53.6% often have no food, 44.8% sometimes consume smaller meals, 43.8% sometimes were worried about food, 40% complained about the limited variety of food, and 39.3% sometimes consume fewer meals. Further, 81.2% of the respondents rely on farming for food, and 59.4% of the respondents access the market on foot, 26.6% by motorcycle, and 14.1% by bicycle. In addition, vegetables and grains were consumed an average of more than five times a week. The study shows the drift of food pillars with the diversity of vegetables, but not on grains. To address the drift, there is a need to improve the access and diversity of foods. The improved diversity of grains will compliment already existing diversified vegetables resulting into a good health outcome.
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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 | 0.001 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".