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Approaching sexuality in aging: an integrative review / Abordagem da sexualidade no envelhecimento: uma revisão integrativa

2021· article· pt· W3209746214 on OpenAlexfundno aff
Fernanda Alencar de Almeida Pereira Fabrício, Gilka Paiva Oliveira Costa, Maria Adelaíde Silva Paredes Moreira, Antônia Lêda Oliveira Silva, Maria Socorro Costa Feitosa Alves, Mariana Lacerda Siqueira Brasileiro

Bibliographic record

VenueRevista de Pesquisa Cuidado é Fundamental Online · 2021
Typearticle
Languagept
FieldMedicine
TopicSexual function and dysfunction studies
Canadian institutionsnot available
FundersHumanities Research Group, University of WindsorUniversidade Federal da ParaíbaUniversidade Federal do Rio Grande do NorteUniversidade de São Paulo
KeywordsHumanitiesPsychologyMedicinePhilosophy

Abstract

fetched live from OpenAlex

Objetivo: identificar na literatura evidências científicas que influenciam a abordagem da sexualidade na pessoa idosa pelos profissionais de saúde. Método: pesquisa de revisão integrativa, por meio da busca de artigos científicos nas bases MEDLINE e CINAHAL e nas Bibliotecas Virtuais/Repositórios Bibliográficos LILACS, Scielo e Science Direct com enfoque sobre profissionais de saúde e sexualidade em idosos. Resultados: foram selecionados 14 artigos que evidenciaram que a maioria dos profissionais de saúde possuem conhecimento limitado, atitudes variadas e não abordam o assunto proativamente. A ausência de conhecimento e treinamento no assunto, a falta de tempo nas consultas e o desconforto gerado pelo tema foram dificuldades relatadas. Conclusão: a maioria dos profissionais de saúde negligencia a conversa sobre sexualidade com os pacientes mais velhos. A mínima atenção ao assunto proporciona um ambiente embaraçoso tanto para os idosos quanto para os profissionais de saúde.

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)0.009
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.011
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.022
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0110.009
Science and technology studies0.0010.002
Scholarly communication0.0070.005
Open science0.0020.002
Research integrity0.0030.003
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 head0.062
GPT teacher head0.387
Teacher spread0.325 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations3
Published2021
Admission routes1
Has abstractyes

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