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PROGRAMA DE SAÚDE DA FAMÍLIA NO BRASIL: REFLEXÕES CRÍTICAS À LUZ DA PROMOÇÃO DE SAÚDE

2016· article· pt· W2516456634 on OpenAlexaff
Michel Perreault, Érica Rios, Lívia Vieira Lisboa, Bruno Viana de Alencar, Sílvia da Silva Santos Passos, Ana Francisca Tibúrcia Amorim Ferreira e Ferreira, Tânia Teixeira, Filipe Ferreira de Almeida Rego, Kátia Nunes Sá

Bibliographic record

VenueRevista Enfermagem Contemporânea · 2016
Typearticle
Languagept
FieldHealth Professions
TopicHealth, Nursing, Elderly Care
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsHumanitiesPolitical scienceMedicinePhilosophy

Abstract

fetched live from OpenAlex

O Programa de Saúde da Família (PSF) foi instituído há 20 anos no Brasil e exige reflexões atualizadas para seu aperfeiçoamento. Tendo se inspirado no modelo canadense, o PSF brasileiro se propõe a ser uma estratégia de enfrentamento dos riscos de agravos e condições de saúde contemporâneas baseada no modelo de abordagem biopsicossocial. Entretanto, a compreensão dos conceitos sobre comunidade, família, gênero e etnicidade precisam ser aprofundados para melhores resultados das estratégias utilizadas pelas equipes de saúde e pelos agentes comunitários.O objetivo desta análise documental foi comparar o SUS com o sistema de saúde canadense, discutindo possíveis causas dos determinantes sociais de saúde que persistem no Brasil dentro dos PSFs.Para isso, se confrontou os achados de programas nacionais e de programas canadenses, com a literatura científica no tema. Os resultados demonstram que o PSF brasileiro precisa de reestruturação para que haja uma verdadeira promoção da saúde. Dentre as principais necessidades, destacam-se um novo papel para os agentes comunitários de saúde, melhores condições e ampliação das equipes profissionais de saúde e reorganização do sistema quanto ao papel das mulheres e dos homens e dos diferentes grupos étnicos.

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)0.019
metaresearch head score (Gemma)0.040
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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.309
Threshold uncertainty score0.614

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.040
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.007
Science and technology studies0.0040.007
Scholarly communication0.0040.002
Open science0.0010.003
Research integrity0.0020.003
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 head0.140
GPT teacher head0.457
Teacher spread0.317 · 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
GenreCommentary

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".

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Citations2
Published2016
Admission routes1
Has abstractyes

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