Food Insecurity Leads to Lower Dietary Diversity Among Smallholder Farmers in Haiti
Bibliographic record
Abstract
Food insecurity (FI) (mild to severe) is highly prevalent amongst smallholder farmers in Haiti. This study is based on 500 surveys nationally distributed where FI was measured using the Latin American and Caribbean Household Food Security Scale which categorizes the households (HH) in food secure (FS=2.6%), mildly (7.2%), moderately (28.3%) and severely (62.0%) FI. Dietary Diversity Scores (DDS) were generated using the FAO's Household Dietary Diversity Index Guidelines. Results show that the DDS decreased significantly from mild (11.0) to moderate (10.0) to severe (9.4) FI levels, after controlling for number of children, gender of head of HH, daily per capita income, education, number of animals and land size. Furthermore, the number of total food items consumed decreased significantly from the mildly (34.3) to the moderately (25.9) and severely (22.5) FI households after controlling for the same variables. The decrease affected staple foods, eggs, dairy, meat/fish, fruits and vegetables. Sugar consumption remained the same in all groups. When compared to all foods consumed, the proportion of animal source foods decreased from FS HH (18.6%) to severely FI HH (11.6%) while the proportion of sugars and oils increased from FS HH (14.0%) to severely FI HH (18.6%). These results show that more severe FI in a HH, the lower the dietary diversity, as well as the intake of nutrient dense foods. Dietary diversity has been shown to imply nutrient adequacy and when compromised, results in poor health status. Furthermore, higher intakes of low nutrient dense foods like sugars and oils imply a need for appropriate interventions to improve access to a greater variety of nutrient dense foods in all FI groups.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".