Food and Water Insecurity in Panamanian Households: A Cross-Sectional Analysis
Bibliographic record
Abstract
Food and water security are essential components for Panama’s advancement toward the Sustainable Development Goals. This study aimed to quantify the prevalence of household food insecurity and water insecurity, and to explore the association between them using standardized measurement tools. A cross-sectional survey was conducted between January and June 2024 using an online questionnaire administered via Google Forms. The survey collected sociodemographic data and applied the Food Insecurity Experience Scale (FIES) and the Household Water Insecurity Experiences (HWISE) scale to assess water and food insecurity, respectively. A total of 222 adult household heads were included (66.2% female), with a median age of 31.4 years. The prevalence of moderate and severe food insecurity was 29.7% (95% CI: 24.8–34.6%) and 6.1% severe food insecurity (95% CI: 3.7–8.4%), while water insecurity affected 27% of households (10.4% high; 16.7% moderate). Multiple linear regression showed that moderate to severe food insecurity was significantly associated with water insecurity (β = 0.19; 95% CI: 0.08–0.31) and lower income levels. Specifically, food insecurity was associated with households reporting no income (β = 0.25; 95% CI: 0.05–0.44) and those with monthly income between 501 and 1000 USD (β = 0.11; 95% CI: 0.01–0.22), compared to households with income above 1000 USD. The results suggest that food insecurity is significantly associated with water insecurity, supporting the need for integrated approaches in public policy to address basic resource access in vulnerable populations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".