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Record W3111060949 · doi:10.33448/rsd-v9i11.10701

Práticas interdisciplinares de cuidado por meio de uma residência multiprofissional em diabetes

2020· article· pt· W3111060949 on OpenAlexfundno aff
Tatiana Rebouças Moreira, Francisca Diana da Silva Negreiros, Lucilane Maria Sales da Silva, Thereza Maria Magalhães Moreira, Silvana Linhares de Carvalho, Maria de Jesus Nascimento de Aquino, Samila Torquato Araújo, Renan Magalhães Montenegro

Bibliographic record

VenueResearch Society and Development · 2020
Typearticle
Languagept
FieldHealth Professions
TopicHealth, Nursing, Elderly Care
Canadian institutionsnot available
FundersMinistério da EducaçãoGoverno BrasilDiabetes Canada
KeywordsHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

O presente estudo descreve práticas interdisciplinares de cuidado desenvolvidas em uma residência multiprofissional em diabetes. Trata-se de estudo descritivo realizado em um Serviço de Endocrinologia e Diabetes. A coleta de dados ocorreu no período de setembro a dezembro de 2020, por meio de formulário semi-estruturado e observação direta. A amostra foi composta por 16 profissionais da área da saúde e de apoio. Os dados foram analisados de forma descritiva. Os resultados mostram que a residência multiprofissional em diabetes é uma formação permanente em saúde, promissora e inovadora, que busca agregar a teoria e a prática durante o exercício das atividades em serviço e possibilita o compartilhamento de informações na equipe multiprofissional, gerando condutas interdisciplinares, integradas e holísiticas. Desse modo, conclui-se que as práticas interdisciplinares de cuidado possibilitam, além de uma formação específica, a ampliação da assistência especializada em diabetes nos três níveis de atenção à saúde, subsidiando melhorias na qualidade de vida dos pacientes e a formação de profissionais qualificados para o mercado de trabalho.

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)0.009
metaresearch head score (Gemma)0.016
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0030.002
Scholarly communication0.0060.002
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.205
GPT teacher head0.493
Teacher spread0.288 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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