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Record W4367610208 · doi:10.55905/revconv.16n.3-003

Violência obstétrica e os Estudos CTS: o processo de acolhimento durante a pandemia da COVID-19 no Município de Paranaguá/PR

2023· article· pt· W4367610208 on OpenAlexaff
Gloria Maria Pereira Funes, Cíntia De Souza Batista Tortato

Bibliographic record

VenueContribuciones a las Ciencias Sociales · 2023
Typearticle
Languagept
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsCascades (Canada)
Fundersnot available
KeywordsHumanitiesPhilosophyCoronavirus disease 2019 (COVID-19)Medicine

Abstract

fetched live from OpenAlex

A pesquisa propôs investigar a violência obstétrica e o processo de acolhimento frente a gravidez na pandemia da COVID-19 entre os anos de 2020 a 2021, em Paranaguá – PR. Os objetivos específicos pretendem estabelecer em bases teóricas sobre os estudos em Ciência, Tecnologia e Sociedade a partir da perspectiva da Epistemologia Feminista, violência obstétrica e o processo de acolhimento enquanto tecnologia; descrever e interpretar casos de violência obstétrica, bem como a sua relação com os direitos reprodutivos e sexuais, a partir do relato de mulheres que estão/estavam em um ciclo gravídico-puerperal durante a pandemia da COVID-19, em Paranaguá – PR, entre 2020 a 2021. A metodologia de pesquisa utilizada é a qualitativa, conduzida através de um roteiro semiestruturado e a análise de dados elaborada por intermédio da categorização de conteúdo proposto por Lawrence Bardin (2011). Com base nos relatos das mulheres em situação de gravidez durante a pandemia desse vírus, se busca analisar casos de violência obstétrica e como essas mulheres perceberam os mecanismos de acolhimento tanto em instituições de saúde públicas e privadas situadas em Paranaguá/PR. Com a amostra dos dados, foi possível constatar que houve um aumento da vulnerabilidade das gestantes, parturientes e puérperas em virtude da pandemia da COVID-19, durante o período informado.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.210
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.005
Science and technology studies0.0060.002
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.002

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 head0.107
GPT teacher head0.395
Teacher spread0.287 · 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 teacher head, not a consensus.

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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