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Record W4286356574 · doi:10.1590/0034-7167-2021-0103pt

Padrões do conhecer de enfermeiras sobre revelação do HIV à criança

2022· article· pt· W4286356574 on OpenAlexaff
Renata de Moura Bubadué, Ivone Evangelista Cabral, Franco A. Carnevale

Bibliographic record

VenueRevista Brasileira de Enfermagem · 2022
Typearticle
Languagept
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsMcGill University
Fundersnot available
KeywordsHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

RESUMO Objetivos: identificar e analisar os padrões do conhecer e as experiências de enfermeiras com a preparação de familiares para a revelação à criança que (con)vive com a soropositividade pelo HIV. Métodos: 13 enfermeiras pediátricas do Rio de Janeiro participaram da pesquisa com o método criativo sensível. Dados foram tratados com análise de discurso de Orlandi e padrões do conhecer de Carper. Resultados: os discursos das enfermeiras revelaram formações imaginárias e ideológicas socioculturalmente construídas. O padrão de conhecer pessoal, sob influência da mídia negativa da doença na década de 1980, gerou estigma e preconceito. Os padrões empírico, estético e ético foram construídos nas capacitações e prática profissional das décadas de 1990-2010. Eles compuseram um contexto de (in)segurança sobre a competência, para contribuir na preparação de familiares para a revelação do HIV à criança. Considerações Finais: a experiência das enfermeiras demonstra conhecimentos para intervir e muitos desafios para sua apropriação prática.

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)0.006
metaresearch head score (Gemma)0.018
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.003
Scholarly communication0.0030.001
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.052
GPT teacher head0.361
Teacher spread0.308 · 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
Published2022
Admission routes1
Has abstractyes

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