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Males diarreicos en la costa ecuatoriana: cambios socioambientales y concepciones de salud

2010· article· es· W2105994199 on OpenAlexaff
James Trostle, Jeanneth Alexandra Yépez-Montufar, Betty Corozo-Angulo, Marylin Rodríguez

Bibliographic record

VenueCadernos de Saúde Pública · 2010
Typearticle
Languagees
FieldEnvironmental Science
TopicEnvironmental and Cultural Studies in Latin America and Beyond
Canadian institutionsTrinity College
FundersNational Institute of Allergy and Infectious DiseasesU.S. Public Health Service
KeywordsGeographyDiarrheal diseaseEnvironmental healthBiomedicineDiseaseMedicineDiarrhea

Abstract

fetched live from OpenAlex

The authors present an ethnoepidemiological study of diarrheal illnesses in 21 communities on the northern coast of Ecuador, where numerous social and environmental changes have taken place since 2001 due to a new highway. As communities realize that nature itself is changing, changes occur in their interpretations of health and disease, which the authors present through a taxonomic classification of diarrheal illnesses. Given the high incidence of diarrheal diseases, alternative concepts have emerged (as compared to those of biomedicine) in relation to causes, symptoms, and treatments. The non-biomedical and biomedical systems overlap, with mixtures of coexistence and resistance. Recognizing this reality means understanding a series of challenges for the official health system, including the indiscriminate use of antibiotics, non-use of health services for some diseases, and perceived relations between environmental contamination and the efficacy of modern and traditional medicines.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score0.112

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.006
GPT teacher head0.243
Teacher spread0.237 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations6
Published2010
Admission routes1
Has abstractyes

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