Socioeconomic Characteristics of Food Insecure Households in San Pedro, Paraguay
Bibliographic record
Abstract
A study in San Pedro Paraguay applied an experience‐based food security (FS) scale (Escala Latinoamericana y Caribeña de Seguridad Alimentaria ‐ ELCSA), classifying 598 households (HH) in the national FS plan PLANAL into 4 groups: food secure (fsHH – 14%), mildly (53%), moderately (21%), and severely (12%) food insecure (fiHH). This study aimed to determine the socioeconomic characteristics of fiHH. The sample was randomly selected, and is representative of PLANAL HH in San Pedro. The internal validity of ELCSA was assessed using Rasch modelling. The relative severity of the scale items reflected the theoretical framework of ELCSA. Most of the items (14) showed acceptable INFIT values (0.7 ‐ 1.3). The Cronbach's alpha coefficient was 0.94. In contrast with fsHH, and with increasing severity of FI, fiHH had less access to health insurance and lower per capita income. Additionally, the proportion of single‐headed HH and of HH speaking only Guaraní language, the rates of illiteracy and poverty, as well as the inability to satisfy basic needs increased with the severity of FI. Finally, fiHH showed a higher rate of stunted children, and reported consuming a lower number of food items, particularly animal‐source foods. Multivariate statistical models confirmed the association of FI with the variables described above. This study contributes to a better understanding of the situation of fiHH in Paraguay, providing policy makers and practitioners with valuable information on how to better address FI. Furthermore, it shows the importance of experience‐based‐scales for targeting fiHH, and its relevance for the national evaluation and monitoring system in Paraguay.
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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) | 0.001 | 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".