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Record W2885316636 · doi:10.15253/2175-6783.2018193408

Metodologias de ensino-aprendizagem sob a perspectiva de discentes de enfermagem

2018· article· pt· W2885316636 on OpenAlexaff
Rafaella Queiroga Souto, Francisca Márcia Pereira Linhares, Maria Isabelly de Melo Canêjo, Francis Solange Vieira Tourinho, Renata Cavalcanti Cordeiro, Pierre Pluye

Bibliographic record

VenueRev Rene · 2018
Typearticle
Languagept
FieldSocial Sciences
TopicEducation and Critical Thinking Development
Canadian institutionsMcGill University
Fundersnot available
KeywordsDialogicPerspective (graphical)Context (archaeology)Mathematics educationTeaching methodQualitative propertyProcess (computing)PsychologyPedagogyNursingMedical educationMedicineComputer science

Abstract

fetched live from OpenAlex

Objetivo: avaliar as metodologias de ensino-aprendizagem adotadas por docentes de um curso de Enfermagem, sob a perspectiva discente. Métodos: pesquisa com métodos mistos do tipo convergente. Recorte de projeto amplo de avaliação de programa, com uso do modelo Contexto, Insumos, Processo e Produto. Dados quantitativos (estudo transversal com dados secundários) e qualitativos coletados concomitantes e, posteriormente, triangulados. Resultados: sobre as metodologias de ensino adotadas por docentes, os discentes participantes referiram maior utilização de aulas expositivas e dialogadas, 161 (67,6%), entre professores das disciplinas básicas; e de aulas expositivas e não dialogadas, 226 (92,6%), por docentes de disciplinas específicas da enfermagem. Em todas as disciplinas, predominaram metodologias tradicionais de ensino e avaliação. Conclusão: os discentes participantes consideraram as metodologias predominantemente tradicionais e desejaram vivenciar métodos ativos, destacando a necessidade da interdisciplinaridade e maior integração ensino-serviço-comunidade.

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)0.028
metaresearch head score (Gemma)0.071
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 score0.028
Threshold uncertainty score0.147

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0280.071
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.005
Science and technology studies0.0050.004
Scholarly communication0.0080.005
Open science0.0020.007
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.105
GPT teacher head0.412
Teacher spread0.306 · 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

Citations3
Published2018
Admission routes1
Has abstractyes

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