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Record W4205511375 · doi:10.35621/23587490.v9.n1.p2-24

COLABORAÇÃO INTERPROFISSIONAL EM EQUIPE: PERCEPÇÃO DE PROFISSIONAIS DE SAÚDE DA REGIÃO NORDESTE DO BRASIL

2022· article· pt· W4205511375 on OpenAlexaff
Emanuella Pinheiro de Farias Bispo, Rosana Aparecida Salvador Rossit, Carole Orchard

Bibliographic record

VenueRevista interdisciplinar em saúde · 2022
Typearticle
Languagept
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsWestern University
Fundersnot available
KeywordsPolitical science

Abstract

fetched live from OpenAlex

RESUMO: A Educação Interprofissional (EIP) é uma estratégia inovadora que proporciona a prática colaborativa que ocorre quando duas ou mais profissões aprendem sobre os outros, com os outros e entre si. Com o objetivo de analisar a percepção de profissionais do Nordeste do Brasil em relação à colaboração interprofissional nas equipes de saúde, 245 profissionais de 13 profissões da saúde completaram a AITCS II-BR que avalia o nível de colaboração interprofissional entre os membros de uma equipe. A escala possui 23 assertivas organizadas em três dimensões e foi hospedada em plataforma online. Os dados receberam tratamento estatístico e as respostas foram classificadas em zona de conforto, alerta e perigo. Identificou-se fragilidades relacionadas à parceria, cooperação e coordenação nas equipes. A colaboração interprofissional ainda é tema pouco explorado no contexto da formação inicial e permanente dos profissionais de saúde. Palavras chave. Educação interprofissionais. Aprendizado colaborativo. Formação. Equipe de assistência ao paciente.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.231
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.003
Science and technology studies0.0080.000
Scholarly communication0.0010.001
Open science0.0020.005
Research integrity0.0010.006
Insufficient payload (model declined to judge)0.0330.002

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 head0.038
GPT teacher head0.427
Teacher spread0.389 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations0
Published2022
Admission routes1
Has abstractyes

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