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Record W4388592337 · doi:10.51161/conbrasp2023/25843

VISITAS VIRTUAIS EM UMA UNIDADE NEONATAL DURANTE A PANDEMIA DA COVID-19: HUMANIZAÇÃO DO CUIDADO

2023· article· pt· W4388592337 on OpenAlexaff
Daniele da Silva Araújo, Erica Carine Rodrigues Pedrosa, ANTÔNIO AUGUSTO FERREIRA CARIOCA, Sara Soares Sena

Bibliographic record

Venuenot available
Typearticle
Languagept
FieldComputer Science
TopicHealthcare during COVID-19 Pandemic
Canadian institutionsCanadian Association of Nurses in Oncology
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)VirologyComputer scienceMedicine

Abstract

fetched live from OpenAlex

Introduo: A COVID-19 um problema de sade pblica, sendo um desafio em escala global. Com altos nmeros de casos e bitos no Brasil, diante da gravidade da doena e da alta transmissibilidade. Em ambiente hospitalar foram implementadas medidas sanitrias como a restries de visitas de familiares aos pacientes hospitalizados. Porm para as mes gerou uma quebra de vnculo entre mefilho causando prejuzos a todos os recm-nascidos (RNs). Com intuito de manter a proximidade familiar e o apoio psicolgico, os recursos tecnolgicos surgem como medidas alternativas de minimizar a angstia, pois a comunicao pode ocorrer de forma virtual. Dada a importncia dessa possibilidade, foi aprovado

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)0.002
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0030.001
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.067
GPT teacher head0.359
Teacher spread0.292 · 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 designQualitative
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
Published2023
Admission routes1
Has abstractyes

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