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Brasil-Canadá: Lançando Sementes Através De Consulta Comunitária sobre o enfrentamento da violência contra a mulher

2020· article· pt· W3036462658 on OpenAlexaffabout
Margareth Santos Zanchetta, Sepali Guruge, Rosane Mara Pontes de Oliveira, Ingryd Cunha Ventura Felipe, Rafaella Queiroga Souto

Bibliographic record

VenueEscola Anna Nery · 2020
Typearticle
Languagept
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsToronto Metropolitan University
FundersUniversidade Federal da ParaíbaMinisterio de Economía y Competitividad
KeywordsHumanitiesSociologyPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

RESUMO Objetivos Relatar a primeira experiência da Cátedra de Pesquisa em Saúde Urbana (Universidade Ryerson-Canadá) no Brasil, em colaboração com a Universidade Federal da Paraíba por meio de uma consulta de opinião comunitária sobre as estratégias de enfrentamento da violência contra a mulher, e oferecer subsídios que estimulem uma visão renovada de colaborações e parcerias internacionais entre programas de Enfermagem. Método Abordagem descritiva do tipo relato de experiência de docentes e discentes de Enfermagem brasileiros e canadenses, desde o processo de planejamento até a análise das informações obtidas. Resultados Essa experiência foi fundamental para o estabelecimento de colaborações científicas recentemente implementadas e em planejamento, consolidando o potencial da Enfermagem em participar de acordos científicos e tecnológicos entre Brasil-Canadá. Conclusão e Implicações para a prática Recomendamos que programas de graduação e pós-graduação de Enfermagem no Brasil promovam o intercâmbio de seu corpo social com diversos países, não apenas em projetos de pesquisa, mas também em projetos de desenvolvimento social valorizados igualmente na construção de um corpo de conhecimentos para a Enfermagem global.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.294
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.063
GPT teacher head0.332
Teacher spread0.270 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations2
Published2020
Admission routes2
Has abstractyes

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