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Record W2215208820

Environmental health in South Africa

2013· article· en· W2215208820 on OpenAlexaboutno aff
Angela Mathee

Bibliographic record

VenueSouth African Health Review · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Social Impact Assessments
Canadian institutionsnot available
Fundersnot available
KeywordsPovertyEnvironmental healthDeveloping countryEconomic growthUrbanizationQuarter (Canadian coin)Burden of diseaseGlobal healthDiseaseEnvironmental planningGeographyMedicineDevelopment economicsHealth carePopulationEconomics
DOInot available

Abstract

fetched live from OpenAlex

The World Health Organization has estimated that as much as one quarter of the global burden of disease is due to modifiable environmental factors. In children, and in developing countries, the proportion of illness that can be attributed to modifiable environmental factors is even higher. Addressing environmental hazards in the places in which people live, learn and play, is therefore a cost-effective means of preventing ill health and reducing the burden of treatment currently borne by the health services.This chapter provides an overview of some of the challenges and progress in addressing environmental health issues in South Africa. After providing some basic definitions, the chapter focuses on the environmental contribution to the global burden of disease, environmental risk factors such as urbanisation, living environments, and exposure to toxins, and the role of poverty and inequity in perpetuating these risk factors. The chapter concludes by discussing a possible framework for responding to environmental health in South Africa and briefly considers the role of environmental health practitioners in a post-apartheid South Africa.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.592
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0110.009

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 head0.031
GPT teacher head0.303
Teacher spread0.273 · 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; both teacher heads agree on what is shown here.

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

Citations2
Published2013
Admission routes1
Has abstractyes

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