Aprimoramento da Escala Brasileira de Medida Domiciliar de Insegurança Alimentar: recomendação de EBIA com 14 itens
Bibliographic record
Abstract
ObjectiveTo review and refine Brazilian Household Food Insecurity Measurement Scale structure. MethodsThe study analyzed the impact of removing the item “adult lost weight” and one of two possibly redundant items on Brazilian Household Food Insecurity Measurement Scale psychometric behavior using the one-parameter logistic (Rasch) model. Brazilian Household Food Insecurity Measurement Scale psychometric behavior was analyzed with respect to acceptable adjustment values ranging from 0.7 to 1.3, and to severity scores of the items with theoretically expected gradients. The socioeconomic and food security indicators came from the 2004 National Household Sample Survey, which obtained complete answers to Brazilian Household Food Insecurity Measurement Scale items from 112,665 households. ResultsRemoving the items “adult reduced amount...” followed by “adult ate less...” did not change the infit of the remaining items, except for “adult lost weight”, whose infit increased from 1.21 to 1.56. The internal consistency and item severity scores did not change when “adult ate less” and one of the two redundant items were removed. ConclusionBrazilian Household Food Insecurity Measurement Scale reanalysis reduced the number of scale items from 16 to 14 without changing its internal validity. Its use as a nationwide household food security measure is strongly recommended.
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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 | 0.030 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".