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Record W4400168808 · doi:10.47820/recima21.v5i7.5423

BENEFÍCIOS DA VISITA DE ENFERMAGEM NO PRÉ-OPERATÓRIO

2024· article· pt· W4400168808 on OpenAlexaff
R Costa, Solange Queiroga Serrano, Matheus Gabriel da Silva Cavalcanti Viturino, Alana Laleska Azevedo Cavalcanti

Bibliographic record

VenueRECIMA21 - Revista Científica Multidisciplinar - ISSN 2675-6218 · 2024
Typearticle
Languagept
FieldMedicine
TopicPalliative and Oncologic Care
Canadian institutionsImpact
Fundersnot available
KeywordsMedicineHumanitiesGynecologyPhilosophy

Abstract

fetched live from OpenAlex

Objetivo: Identificar na literatura o estado da arte acerca dos benefícios da visita de enfermagem no pré-operatório. Método: Revisão integrativa da literatura, realizada nas bases de dados da LILACS, BDENF e MEDLINE, presentes na Biblioteca Virtual em Saúde (BVS). Utilizaram-se os Descritores em Ciências da Saúde (DeCS): Assistência Pré-Operatória, Enfermagem Pré-Operatória, Período Pré-Operatório, com as respectivas traduções dos seus correspondentes em inglês e espanhol, correlacionados pelo operador booleano AND entre os termos. Resultados: A análise do levantamento bibliográfico evidenciou os benefícios da visita de enfermagem no pré-operatório para o paciente cirúrgico e sua importância no melhor prognóstico do paciente, para ressaltar a importância do enfermeiro nesse processo e as possíveis estratégias para realização dessa etapa, bem como seus impactos. Conclusão: Observou-se que os artigos constatam a ansiedade como maior problema na dimensão emocional dos pacientes cirúrgicos. Se evidenciou que a visita de enfermagem reduz os níveis de ansiedade, a partir da educação em saúde, ao favorecer o melhor prognóstico do paciente e sua experiência cirúrgica.

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)0.007
metaresearch head score (Gemma)0.035
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.035
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0060.006
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.059
GPT teacher head0.379
Teacher spread0.320 · 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 designNot applicable
Domainnot available
GenreOther

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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Citations0
Published2024
Admission routes1
Has abstractyes

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