Household food insecurity in Panamanian subsistence farming communities is associated with indicators of household wealth and constraints on food production
Bibliographic record
Abstract
OBJECTIVE: To determine if constraints on agricultural production were a novel construct in the Panama Food Security Questionnaire (FSQ) and to characterize agricultural and economic determinants of food insecurity during the planting, growing and harvesting time periods in subsistence farming communities. DESIGN: This longitudinal study followed households during land preparation, growing and harvest periods in one agricultural cycle. Agricultural production and economic variables were recorded and the Panama FSQ was administered. Exploratory factor analysis was used to verify construct validity of the FSQ. A food insecurity score (FIS), ranging from 0 to 42, was derived. Multiple regression analyses of FIS were conducted for each agricultural period. SETTING: Fifteen rural villages in Panama. PARTICIPANTS: Subsistence farming households (n 237). RESULTS: The FSQ contained four constructs: (i) ability to buy food; (ii) decreased amount/number of meals; (iii) feeling hungry; and (iv) lower agricultural production because of weather or lack of resources. Although most households were mildly food insecure in all time periods, determinants of food insecurity differed in each. Higher FIS was associated during land preparation with less rice and legumes planted and lower asset-based wealth; during growing months with less rice, more maize and pigeon peas planted and not selling produce; and during harvest with less rice planted, fewer chickens and lower income. CONCLUSIONS: Constraints on agriculture was a novel construct of the Panama FSQ. Different income-related variables emerged in each agricultural period. Planting staple foods and raising chickens were associated with food security, but some crop choices were associated with food insecurity.
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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 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".