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Record W4405492723 · doi:10.12957/reuerj.2024.86420

Vulnerabilidade e populações vulnerabilizadas às infecções sexualmente transmissíveis nos currículos de enfermagem: percepção dos docentes

2024· article· pt· W4405492723 on OpenAlexaff
Stéfany Petry, María Itayra Padilha, Amina Silva, Mariana Vieira Villarinho, Roberta Costa

Bibliographic record

VenueRevista Enfermagem UERJ · 2024
Typearticle
Languagept
FieldHealth Professions
TopicAdolescent Sexual and Reproductive Health
Canadian institutionsBrock University
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsSociologyVulnerability (computing)HumanitiesGerontologyPhilosophyMedicineComputer scienceComputer security

Abstract

fetched live from OpenAlex

Objetivo: analisar o interesse teórico, político e filosófico acerca da vulnerabilidade e das populações vulnerabilizadas às infecções sexualmente transmissíveis na formação dos estudantes de Graduação em Enfermagem de Universidades Federais brasileiras. Método: estudo qualitativo, histórico social, com uso de fontes orais e documentais. Realizadas 23 entrevistas com docentes de cinco cursos de graduação em enfermagem. Os dados foram inseridos no software Atlas.ti versão 9.0 para codificação, e operacionalizada a Análise de Conteúdo. Resultados: a vulnerabilidade e as populações vulnerabilizadas são compreendidas pelos docentes diante de sua complexidade, que envolve fatores sociais, estruturais e econômicos. São discutidas em alguns momentos durante o processo formativo. Os estudantes possuem dificuldades em perceber a própria vulnerabilidade. Considerações finais: a abordagem dessa temática deve ser discutida no ensino dos futuros enfermeiros. A vulnerabilidade do estudante precisa ser refletida de modo que o mesmo perceba sua própria vulnerabilidade e compreenda a importância do autocuidado.

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)0.020
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 score0.025
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.020
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0030.003
Scholarly communication0.0040.002
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.146
GPT teacher head0.462
Teacher spread0.316 · 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
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueRevista Enfermagem UERJSame topicAdolescent Sexual and Reproductive HealthFrench-language works237,207