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Perfil de crianças com necessidades especiais de saúde e seus cuidadores em um hospital de ensino

2020· article· pt· W3161301756 on OpenAlexaff
Raíssa Passos dos Santos, Valéria Regina Gais Severo, Jaquiele Jaciára Kegler, Leonardo Bigolin Jantsch, Débora Cordeiro, Eliane Tatsch Neves

Bibliographic record

VenueCiência Cuidado e Saúde · 2020
Typearticle
Languagept
FieldHealth Professions
TopicMaternal and Neonatal Healthcare
Canadian institutionsMcGill University
Fundersnot available
KeywordsHumanitiesMedicinePhilosophy

Abstract

fetched live from OpenAlex

Objetivo: caracterizar as crianças com necessidades especiais de saúde, internadas em unidade pediátrica de um hospital de ensino, em relação às suas condições clínicas, demandas de cuidados e situação sociodemográfica. Ainda, caracterizar os familiares cuidadores das crianças quanto a sua idade e grau de parentesco. Método: estudo descritivo, com abordagem quantitativa, realizado com 25 crianças com necessidades especiais de saúde, internadas na Unidade de Internação Pediátrica de um hospital de ensino. Os dados foram coletados por meio de formulário e analisados por meio da estatística descritiva. Resultados: das crianças internadas no período, 44% apresentaram necessidades especiais de saúde. Com relação às demandas de cuidados, todas possuem cuidados habituais modificados, 36% utilizam algum tipo de tecnologia, 40% possuem demanda de desenvolvimento neuropsicomotor, 92% fazem acompanhamento com algum serviço de saúde e 80% fazem uso contínuo de medicação. Conclusão: Este estudo oferece dados que podem ser utilizados como suporte para a elaboração de estratégias que reorientem a prática assistencial de enfermagem.

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)0.001
metaresearch head score (Gemma)0.008
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 score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.351
Teacher spread0.305 · 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".

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Citations9
Published2020
Admission routes1
Has abstractyes

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