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Diagnósticos e intervenções de enfermagem em idosos frágeis segundo o modelo conceitual de henderson

2020· article· pt· W3083259768 on OpenAlexaboutno aff
Vitória Polliany de Oliveira Silva, Lucilla Vieira Carneiro, Neyce de Matos Nascimento

Bibliographic record

VenueSaúde Coletiva (Barueri) · 2020
Typearticle
Languagept
FieldNursing
TopicNursing Diagnosis and Documentation
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineGerontologyPsychology

Abstract

fetched live from OpenAlex

Objetivo: Identificar os principais Diagnósticos de Enfermagem (DE) em idosos frágeis, propondo intervenções segundo o modelo conceitual de Henderson. Método: Trata-se de um estudo transversal, descritivo com abordagem quantitativa e qualitativa, realizado com 25 idosos de uma Instituição de Longa Permanência. Foram aplicados um questionário sociodemográfico e econômico, o Mini Exame do Estado Mental e a escala de fragilidade de Edmonton. Para elaboração dos enunciados de diagnósticos e intervenções de enfermagem, empregou-se a CIPE®, versão 2017. Os dados foram processados no SPSS, versão 20 e analisados através da estatí­stica descritiva. Resultados: Os DE mais frequentes foram: Marcha prejudicada (94,4%), Risco de queda (94,4%), Memória prejudicada (94,4%), Falta de apetite (66,7%) e Humor deprimido (50%). Conclusão: A execução do estudo evidenciou que 72% dos participantes apresentaram fragilidade, onde foi possí­vel identificar 8 DE dentre os idosos frágeis, possibilitando a formulação de intervenções frente í s necessidades de cuidados.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.435
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.060
GPT teacher head0.331
Teacher spread0.271 · 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 teacher head, not a consensus.

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".

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Citations1
Published2020
Admission routes1
Has abstractyes

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