Determinants of household food access among small farmers in the Andes: examining the path
Bibliographic record
Abstract
OBJECTIVE: Household food access remains a concern among primarily agricultural households in lower- and middle-income countries. We examined the associations among domains representing livelihood assets (human capital, social capital, natural capital, physical capital and financial capital) and household food access. DESIGN: Cross-sectional survey (two questionnaires) on livelihood assets. SETTING: Metropolitan Pillaro, Ecuador; Cochabamba, Bolivia; and Huancayo, Peru. SUBJECTS: Households (n = 570) involved in small-scale agricultural production in 2008. RESULTS: Food access, defined as the number of months of adequate food provisioning in the previous year, was relatively good; 41 % of the respondents indicated to have had no difficulty in obtaining food for their household in the past year. Using bivariate analysis, key livelihood assets indicators associated with better household food access were identified as: age of household survey respondent (P = 0.05), participation in agricultural associations (P = 0.09), church membership (P = 0.08), area of irrigated land (P = 0.08), housing material (P = 0.06), space within the household residence (P = 0.02) and satisfaction with health status (P = 0.02). In path models both direct and indirect effects were observed, underscoring the complexity of the relationships between livelihood assets and household food access. Paths significantly associated with better household food access included: better housing conditions (P = 0.01), more space within the household residence (P = 0.001) and greater satisfaction with health status (P = 0.001). CONCLUSIONS: Multiple factors were associated with household food access in these peri-urban agricultural households. Food security intervention programmes focusing on food access need to deal with both agricultural factors and determinants of health to bolster household food security in challenging lower- and middle-income country contexts.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".