Escala Basada en la Experiencia de Inseguridad Alimentaria (FIES) en Colombia, Guatemala y México
Bibliographic record
Abstract
OBJECTIVE: To compare the psychometric characteristics of the Food Insecurity Experience Scale (FIES) and the grade of similitude or difference among Colombia, Guatemala, and Mexico during three years. MATERIALS AND METHODS: Psychometric comparison using the Rasch model to calculate the relative severity of each item in FIES, INFIT and contrast in the Differential Functioning of Items (c-DIF). RESULTS: The majority of items showed a relative severity corresponding to the theoretical construct and acceptably fit the model (INFIT=0.7-1.3). No c-DIF above 1.0 logit was observed in the comparison men vs women. In the comparison among countries by year 87% of the items showed c-DIF below 0.5 logit. CONCLUSIONS: The FIES presents psychometric characteristics corresponding to the theoretical construct of the tool. Future studies with the inclusion of more countries and more time points are essential to evaluate the relative severity, behavior and distribution of items.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".