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Record W2473977875

Educação interprofissional e prática colaborativa na Atenção Primária à Saúde*

2015· article· pt· W2473977875 on OpenAlexaff
Jaqueline Alcântara Marcelino da Silva, Marina Peduzzi, Carole Orchard, Valéria Marli Leonello

Bibliographic record

VenueLA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · 2015
Typearticle
Languagept
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsWestern University
Fundersnot available
KeywordsInterprofessional educationNursingPrimary health careFocus groupTriangulationPerceptionHealth carePrimary careQualitative researchPerspective (graphical)PsychologyMedical educationMedicineSociologyFamily medicinePolitical science
DOInot available

Abstract

fetched live from OpenAlex

RESUMO Objetivo Compreender as percepções de docentes, trabalhadores e estudantes sobre a articulação da educação interprofissional com as práticas na Atenção Primária à Saúde. Método Qualitativo compreensivo e interpretativo, cuja coleta de dados foi realizada de 2012 a 2013, por meio de 18 entrevistas semiestruturadas com docentes e quatro sessões de grupos focais homogêneos com estudantes, docentes e trabalhadores da Atenção Primária. Resultados A triangulação dos resultados possibilitou a construção de duas categorias: prática colaborativa centrada no usuário e barreiras para educação interprofissional. A primeira indicou a necessidade de mudança do modelo de atenção e de formação dos profissionais de saúde, e a segunda apontou dificuldades percebidas pelos diferentes atores sociais no que se refere à implementação da educação interprofissional. Conclusão A educação interprofissional é incipiente no Brasil e sinaliza possibilidades de mudança em direção à prática colaborativa, mas requer maiores investimentos na articulação ensino-serviço.

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)0.011
metaresearch head score (Gemma)0.028
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 score0.025
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.028
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0040.004
Scholarly communication0.0050.002
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.046
GPT teacher head0.345
Teacher spread0.299 · 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".

Quick stats

Citations44
Published2015
Admission routes1
Has abstractyes

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