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Record W4286357142 · doi:10.1590/0034-7167-2021-0103

Nurses’ patterns of knowing about HIV disclosure to children

2022· article· en· W4286357142 on OpenAlexaff
Renata de Moura Bubadué, Ivone Evangelista Cabral, Franco A. Carnevale

Bibliographic record

VenueRevista Brasileira de Enfermagem · 2022
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsMcGill University
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsAppropriationPsychologyIdeologyPrejudice (legal term)Competence (human resources)NursingContext (archaeology)Stigma (botany)MedicineSocial psychologyPsychiatryPolitical science

Abstract

fetched live from OpenAlex

OBJECTIVES: to identify and analyze nurses' patterns of knowing and experiences with the preparation of families for disclosure to children living with HIV seropositivity. METHODS: thirteen pediatric nurses from Rio de Janeiro participated in the research using the sensitive creative method. Data were treated with Orlandi's discourse analysis and Carper's patterns of knowing. RESULTS: nurses' speeches revealed socioculturally constructed imaginary and ideological formations. The personal pattern of knowing, under the influence of negative media about the disease in the 1980s, generated stigma and prejudice. Empirical, esthetic, and ethical patterns were built on training and professional practice of the 1990s-2010s. They composed a context of (in)security about competence, to contribute to preparing families to disclose HIV to children. FINAL CONSIDERATIONS: nurses' experience demonstrates knowledge to intervene and many challenges for their practical appropriation.

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)0.003
metaresearch head score (Gemma)0.022
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 score0.007
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.345
Teacher spread0.321 · 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".

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Citations1
Published2022
Admission routes1
Has abstractyes

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