Food security and dietary diversity amongst smallholder farmers in Haiti
Bibliographic record
Abstract
As defined by the United Nations Food and Agriculture Organization, food security "exists when all people, at all times, have physical, social and economic access to sufficient, safe and nutritious food which meets their dietary needs and food preferences for an active and healthy life". The nature of food security is multi-faceted and therefore difficult to accurately measure. The Latin American and Caribbean Food Security Scale (ELCSA) is a tool that has been developed and validated to measure food security through the psychosocial experience of a household. Haiti, being the poorest country in the Western Hemisphere, is highly susceptible to poverty, malnutrition and food insecurity. It is estimated that over half of the population lives in extreme poverty (less than one US dollar a day). Since purchasing power for food is directly related to a household’s consumption of food, dietary quantity and quality are greatly reduced in situations of extreme poverty and food insecurity. Dietary diversity has been shown to imply nutrition adequacy among populations and when compromised, results in poor health status. Food variety and dietary diversity scores have been traditionally used to assess dietary quality in developing countries. The primary objective of this study is to assess the relationship of food security with dietary diversity among small rural farming households in Haiti using a secondary data set analysis. Data are drawn from a quantitative cross sectional study including 500 households from 5 departments of rural Haiti that were surveyed by the Inter American Institute for Cooperation on Agriculture. Data were analyzed using IBM® SPSS® 2012 software for descriptive and inferential analyses. Results show that sixty-two percent of households were severely food insecure, with only 2.6 percent being food secure. Dietary Diversity Scores (DDS) were generated using the FAO’s Household Dietary Diversity Index Guidelines. Results show that DDS decreased significantly from mild (11.0) to moderate (10.0) to severe (9.4) Food insecurity levels, after controlling for number of children in the household, gender of head of household, 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) food insecure 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 food secure households (18.6 percent) to severely food insecure households (11.6 percent) while the proportion of sugars and oils increased from food secure households (14.0 percent) to severely food insecure households (18.6 percent). The changes seen in consumption of low nutrient dense foods like sugars and oils implies greater access to foods that may provide calories but have little nutrient quality, therefore masking the achievement of food and nutrition security. Organizations, governmental and non-governmental should be informed of these trends to better adapt existing and future intervention programs that aim to inversely improve access to energy and nutrient dense foods and decrease the access to empty calorie foods.
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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 | 0.001 | 0.001 |
| 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".