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Record W7056742787

Food security and dietary diversity amongst smallholder farmers in Haiti

2015· dissertation· en· W7056742787 on OpenAlexfundno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2015
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
FundersMcGill University
KeywordsFood securityPovertyAgricultureMalnutritionPopulationFood systemsDietary diversityPurchasing powerExtreme poverty
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.023
GPT teacher head0.250
Teacher spread0.227 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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