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Comprehensive Primary Health Care in South America: contexts, achievements and policy implications

2011· review· en· W2138713655 on OpenAlexafffund
Naydú Acosta-Ramírez, Jennifer Pollard Ruiz, Román Vega Romero, Ronald Labonté

Bibliographic record

VenueCadernos de Saúde Pública · 2011
Typereview
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsUniversity of Ottawa
FundersCanadian Institutes of Health ResearchUniversity of OttawaCanadian Health Services Research FoundationUniversity of the Western CapeInternational Development Research CentreHealth Canada
KeywordsPrimary health careHealth careDeclarationPrimary careAction (physics)Health policyNursingPublic relationsPolitical scienceNarrativeMedicineEconomic growthPublic healthFamily medicine

Abstract

fetched live from OpenAlex

This article summarizes an extensive review of South American experiences with primary health care since the Declaration of Alma-Ata. It aims to address the following specific questions: What are the enabling and constraining historical and structural conditions for primary health care policies and practices? How has health care reform supported or undermined primary health care? What evidence exists on the effectiveness of primary health care? What strategies are common to best practices? What evidence exists on the roles of citizen participation and intersectoral action? And finally, what are the policy lessons to be learned from these experiences? Narrative synthesis was used to identify and examine patterns in the data consistent with these questions. Conditions that were found to promote successful implementation of primary health care are outlined, together with features of effective primary health care systems that help create more equitable health services and health outcomes.

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)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.051
Threshold uncertainty score0.101

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.007
Science and technology studies0.0010.001
Scholarly communication0.0030.002
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 head0.120
GPT teacher head0.473
Teacher spread0.353 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations49
Published2011
Admission routes2
Has abstractyes

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