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Record W2049973256 · doi:10.1002/hpm.731

Determinants of health status and the influence of primary health care services in Latin America, 1990–98

2003· article· en· W2049973256 on OpenAlexaff
David Moore, Eliana Castillo, Chris G. Richardson, Robert J. Reid

Bibliographic record

VenueThe International Journal of Health Planning and Management · 2003
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Systems and Reforms
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsLatin AmericansPer capitaPovertyGovernment (linguistics)LiteracyHealth careDemographyMedicineHealth literacyDeveloping countryMultivariate analysisEnvironmental healthSocioeconomicsGerontologyEconomic growthPolitical scienceEconomicsPopulationSociology

Abstract

fetched live from OpenAlex

Primary health care (PHC) services have been advocated as a means by which less developed countries may improve the health of their populations even in the face of poverty, low levels of literacy, poor nutrition and other factors that negatively influence health status. Using aggregated data from the World Bank and UNICEF this study examined which factors, both within the health care system and outside of it, are associated with under-5 mortality rates in 22 countries of Latin America and the Caribbean during the 1990s. In a multivariate analysis using generalized estimating equations for repeated measures, five factors were found to be independent predictors of lower under-5 mortality rates (U5MRs). These were vaccination levels, female literacy, the use of oral rehydration therapy, access to safe water and GNP per capita. When the magnitude of these associations were assessed, higher levels of GNP per capita was found to be very weakly associated with lower U5MRs, compared with female literacy and vaccination rates. These findings suggest that government policies which focus only on promoting economic growth, while not making important investments in PHC services, female education and access to safe water are unlikely to see large improvements in health status.

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)0.003
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 score0.159
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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)0.0000.000

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 head0.026
GPT teacher head0.298
Teacher spread0.272 · 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

Citations29
Published2003
Admission routes1
Has abstractyes

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