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Record W2777589712 · doi:10.25336/p6gg7t

Socio-economic differences in disability by age in sub-Saharan Africa: A cross-national study using the World Health Survey

2017· article· en· W2777589712 on OpenAlexaffvenue
Yentéma Onadja, Simona Bignami, Marı́a Victoria Zunzunegui

Bibliographic record

VenueCanadian Studies in Population · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsUniversité de Montréal
FundersWorld Health Organization
KeywordsRural populationHumanitiesGeographyPolitical scienceDemographySociologyPopulationPhilosophy

Abstract

fetched live from OpenAlex

This study aims to examine the relationship between socio-economic status (as measured by education) and multiple disability measures among adults in eighteen sub-Saharan African countries, and to determine whether the strength of this relationship varies across age groups. The analysis uses data drawn from the 2002–04 World Health Survey. The findings indicate that low education is positively associated with poor functional health, and the functional health gap between educational levels remains stable across age. These findings suggest that in sub-Saharan African countries, the undereducated are less successful in postponing disability to later ages.Cette étude examine la relation entre le niveau d’éducation et les incapacités parmi les adultes dans 18 pays d’Afrique subsaharienne, et détermine si cette relation varie selon les groupes d’âge. L’analyse a utilisé les données de l’enquête mondiale de santé 2002–2004. Nos résultats indiquent que le manque d'éducation était positivement associé à des niveaux plus élevés d'incapacités, et le différentiel d’état de santé fonctionnelle entre les différents niveaux d'éducation augmentait entre les groupes d’âges. Ces résultats suggèrent qu’en Afrique subsaharienne, les individus faiblement éduqués ont moins de succès dans le report de l’incapacité dans la vieillesse.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
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 head0.282
GPT teacher head0.476
Teacher spread0.195 · 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.

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

Citations4
Published2017
Admission routes2
Has abstractyes

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