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Record W2117682057 · doi:10.1093/her/cys072

Scientific and popular health knowledge in the education work of community health agents in Rio de Janeiro shantytowns

2012· article· en· W2117682057 on OpenAlexafffund
Margareth Santos Zanchetta, Bukola Salami, Michel Perreault, Lígia Costa Leite

Bibliographic record

VenueHealth Education Research · 2012
Typearticle
Languageen
FieldHealth Professions
TopicHealth, Nursing, Elderly Care
Canadian institutionsUniversity of TorontoToronto Metropolitan University
FundersUniversité de Montréal
KeywordsHealth educationHealth promotionCommunity healthSociologyPublic healthWork (physics)Public relationsEconomic growthNursingPolitical scienceMedicine

Abstract

fetched live from OpenAlex

Health education for socially marginalized populations challenges the efficacy of existing strategies and methods, and the pertinence of the educational and philosophical principles that underpin them. The Brazilian Community Health Agents Initiative (CHAI) hires residents of deprived marginalized communities to undertake health promotion and education in their communities. The ultimate goal of the CHAI is to connect populations with the public healthcare system by promoting social re-affiliation, protecting civil rights and enhancing equity of access to health services. In this article, we present the education work of community health agents through interplay between popular and scientific health knowledge in nine Rio de Janeiro shantytowns. A critical ethnographic research design, using thematic analysis, allowed us to explore agents' education work to enhance family health literacy in shantytowns. Local culture and social practices inspire Agents to create original strategies to reconcile forms of health knowledge in their work.

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)0.002
metaresearch head score (Gemma)0.003
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.117
Threshold uncertainty score0.233

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.010
Scholarly communication0.0030.001
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.481
GPT teacher head0.646
Teacher spread0.165 · 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

Citations25
Published2012
Admission routes2
Has abstractyes

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