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Record W1913782140 · doi:10.1093/pch/10.5.269

Children with disabilities in low-income countries

2005· article· en· W1913782140 on OpenAlexaff
Debra Cameron, Stephanie Nixon, Penny Parnes, Mia Pidsadny

Bibliographic record

VenuePaediatrics & Child Health · 2005
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent and Pediatric Healthcare
Canadian institutionsCentre for Disability Prevention and Rehabilitation
Fundersnot available
KeywordsTanzaniaMedicineRehabilitationDeveloping countryPopulationStigma (botany)GerontologyEnvironmental healthEconomic growthPsychiatrySocioeconomicsPhysical therapySociology

Abstract

fetched live from OpenAlex

Disability is a major public health concern worldwide and the situation for children with disabilities is even more serious. The present article will focus on the issue of children with disabilities in low-income countries. Approximately one-third of the world's disabled population is children and many of these disabling conditions are preventable. In Africa, one the foremost causes of disability is infectious and communicable disease; the incidence of these diseases have been greatly reduced or eliminated in higher income countries. Other causes include war, trauma, accidents, and congenital and noninfectious diseases. The recent HIV/AIDS epidemic has further contributed to the prevalence of disability because many people living with HIV develop different types of impairments and functional limitations. Community-based rehabilitation is one approach that has been used in many low-income countries and which often focuses on children and their families. The work of one organization providing community-based rehabilitation in Tanzania is highlighted. The experiences of the coauthors in their work in Tanzania provide some field examples. For those readers who would like to become involved in international health, opportunities for engagement are described, including short-and long-term volunteer service or research experiences.

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 categoriesMeta-epidemiology (narrow), Science 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 score0.064
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.001

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 head0.022
GPT teacher head0.353
Teacher spread0.331 · 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

Citations43
Published2005
Admission routes1
Has abstractyes

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