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Examining the changing profile of undernutrition in the context of food price rises and greater inequality

2015· article· en· W2190695285 on OpenAlexfundno aff
Shailen Nandy, Adel Daoud, David Gordon

Bibliographic record

VenueSocial Science & Medicine · 2015
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsnot available
FundersEconomic and Social Research CouncilVetenskapsrådetSvenska Forskningsrådet FormasMcGill University
KeywordsMalnutritionFood pricesAnthropometryPovertyContext (archaeology)InequalityPsychological interventionFood securityEnvironmental healthDeveloping countryPublic healthMedicineEconomicsDevelopment economicsEconomic growthGeographyAgriculture

Abstract

fetched live from OpenAlex

This paper examines how the profile of undernutrition among children in two African countries (Ethiopia and Nigeria) changed over the period of the 2007/08 food, fuel and financial crisis. Using the Composite Index of Anthropometric Failure (CIAF), an indicator which allows for a comprehensive assessment of undernutrition in young children, we examine what changes occurred in the composition of undernutrition, and how these changes were distributed amongst children in different socio-economic groups. This is important as certain combinations of anthropometric failure (AF), especially the experience of multiple failures (dual and triple combinations of AF) are associated with higher morbidity and mortality risks, and are also related to poverty. Our hypothesis is that increases in food prices during the crisis contributed to an increase in inequality, which may have resulted in concurrent increases in the prevalence of more damaging forms of undernutrition amongst poorer children. While both countries witnessed large increases in food prices, the effects were quite different. Ethiopia managed reduce the prevalence of multiple anthropometric failure between 2005 and 2011 across most groups and regions. By contrast, in Nigeria prevalence increased between 2008 and 2013, and particularly so in the poorer, northern states. The countries studied applied quite different policies in response to food price increases, with the results from Ethiopia demonstrating that protectionist public health and nutrition interventions can mitigate the impacts of price increases on poor children.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.436
Threshold uncertainty score0.373

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.112
GPT teacher head0.345
Teacher spread0.233 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations95
Published2015
Admission routes1
Has abstractyes

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