Food security status and dietary intake among small farming families in Haiti (805.6)
Bibliographic record
Abstract
Food insecurity (FIS) greatly affects small farmer families in Haiti. This study includes 502 Haitian households where FIS was estimated using the Latin American and Caribbean Food Security Scale. FIS was categorized as mild (7.2%), moderate (28.3%), and severe (62.0%) FIS; 2.5% of the households were food secure. Food consumption was estimated for the month previous to the interview using a food frequency questionnaire. Findings show significant differences (p<0.001) in the number of food items (fi) consumed at least once a week by FIS level: mild (33.5 fi), moderate (25.2 fi) and severe (22.1 fi). The number of staple food items consumed decreased as follows: mild FIS=7.5, moderate FIS=5.9, and severe FIS=5.0. Furthermore, with increasing FIS severity, mean food item consumption for nutrient dense food groups decreased, affecting the consumption of eggs, dairy, meat/fish, fruits and vegetables (p<0.001). Additionally to a decreased consumption in the number of fi, the proportion of households consuming animal source foods decreased as FIS increased. For example, the proportion of households consuming eggs was 69% (mild FIS), 50% (moderate FIS), and 35% (severe FIS). Sugar intake remained the same across FIS categories, though. These results indicate that dietary diversity might decrease, as FIS turns more severe. Lower protein and micronutrient intake might have negative impacts on the nutrition status of Haitian farmer families. Differences in food intake between FIS levels demonstrate the need for differentiated interventions by FIS level.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".