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Impacto da Residência de Medicina da Família e Comunidade no Atendimento a Mulheres Vítimas de Violência

2019· article· pt· W2939984742 on OpenAlexaff
Sávia Denise Silva Carlotto Herrera, Cristina Morais Machado, Neilton Araújo de Oliveira, Marcos Gontijo da Silva, Yuniel Martínez Hernández, Florence Germaine Tible Lainscek

Bibliographic record

VenueRevista Cereus · 2019
Typearticle
Languagept
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsImpact
Fundersnot available
KeywordsArtHumanities

Abstract

fetched live from OpenAlex

Mulheres Vítimas de ViolênciaImpact of the residence of family and community medicine in the care of victims of violence RESUMO Os programas de residência devem garantir meios de aprendizado e aplicabilidade no âmbito do SUS e promover melhorias da atenção integral, em especial a mulheres vítimas de violência.Objetivo: avaliar a qualidade dos serviços da AB no quesito violência contra a mulher no município de Gurupi-TO.Pesquisa do tipo transversal realizada de Dezembro/2017 a Março/2018 em 12 UBS da rede urbana.Os profissionais de saúde responderam ao questionário QualiAB, e foram extraídas as questões que tratam da avaliação da violência contra a mulher.Os dados das UBS com o programa foram comparados com as sem, por meio do teste de qui-quadrado, no programa EPI INFO 3.2.2.Responderam ao questionário 107 profissionais.No quesito "Diagnóstico e acompanhamento das situações de violência doméstica e sexual", não houve diferença entre os grupos, porém nos indicadores "estratégias utilizadas pela unidade para detecção" e "procedimentos realizados em caso de detecção da violência contra mulher" são melhores nas unidades com programa.Conclui-se que esta pesquisa evidenciou que o programa de residência enquanto política do SUS, contribui na melhora da qualidade do serviço de atendimento à violência contra a mulher de forma efetiva e resolutiva aos usuários do SUS.

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)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.014
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.033
GPT teacher head0.349
Teacher spread0.316 · 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 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".

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Citations1
Published2019
Admission routes1
Has abstractyes

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