MétaCan
Menu
Back to cohort
Record W2303754346 · doi:10.12927/whp.2016.24516

Increasing Levels of Urban Malnutrition with Rapid Urbanization in Informal Settlements of Katutura, Windhoek: Neighbourhood Differentials and the Effect of Socio-Economic Disadvantage

2016· article· en· W2303754346 on OpenAlexvenueno aff
Ndeyapo Nickanor, Lawrence N. Kazembe

Bibliographic record

VenueWorld health & population · 2016
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsnot available
Fundersnot available
KeywordsUrbanizationMalnutritionNeighbourhood (mathematics)PovertyDisadvantageGeographySocioeconomicsEnvironmental healthUrban povertyHuman settlementPublic healthEconomic growthMedicineSociologyPolitical scienceEconomics

Abstract

fetched live from OpenAlex

Rapid urbanization and increasing urban poverty characterize much of Southern Africa, resulting in poor urban health. This study investigates inter-urban differences and determinants of undernutrition among marginalized communities. Using the 1992, 2000 and 2006/2007 Namibia Demographic and Health Survey data, we fitted hierarchical random intercept logit models, applied at 52 enumeration areas in the capital city (Windhoek), to estimate trends in undernutrition, and investigate risk factors associated with stunting and underweight. Findings demonstrate that undernutrition among children has risen (7.4% to 25.1%, p<0.001 for stunting; and 9.7% to 17.6%, p<0.001 for underweight, between 1992 and 2006/2007). The risk was pronounced for children from socioeconomically disadvantaged households (OR=1.53, 95% CI:[1.01, 2.31] for stunting and OR=2.16, 95% CI:[1.03, 4.89]for underweight). Evidence emerged of intra-urban variation in undernutrition. We argue that with increasing urbanization, comes the challenge of food insecurity and, consequently, malnutrition. For improved child health, urban planners should have targeted interventions for poor urban households and deprived neighbourhoods.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.341

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.008
GPT teacher head0.273
Teacher spread0.265 · 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 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

Citations14
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueWorld health & populationSame topicChild Nutrition and Water AccessFrench-language works237,207