Validity of the Latin American and Caribbean Household Food Security Scale (ELCSA) in South Haiti
Bibliographic record
Abstract
We tested the validity of ELCSA in a convenience sample of 153 women with children under five in South Haiti. ELCSA was applied to the women in Creole, contained 16 items and used a reference period of 3 months. Cronbach's alpha was 0.92. Based on affirmative response prevalence, the items referring to child hunger tended to be the most severe. However, social unacceptability of procuring food was the most severe item. This question asked ‘Was there any time during the past 3 months when you had to do something that you would have preferred not to do (such as begging or sending the children to work) to be able to get food?’ There were no food secure households in the sample, 44% were food insecure (FI), 49% were very FI, and 7% were extremely FI. Criterion validity was strong. Those reporting having good/very good health ranged from 38.8% among those FI to 9.1% among those extremely FI (p=0.02). Households with children who had recently had malaria were more likely to be very/extremely FI than households where the index child had been free of malaria (82.0% vs. 37.1%, p<0.001). Additional factors associated with very/extreme FI (p<0.05) were: female‐headed household, lack of electricity at home, no land ownership, and poorer dietary quality. Results suggest that ELCSA is a valid tool for assessing household FI in rural Haiti. Funded by CIDA (7034161) through a grant to the Centro Internacional de Agricultura Tropical (CIAT).
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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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".