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Record W2552803303 · doi:10.1590/1983-80422016243150

Ensino da bioética: avaliação de um objeto virtual de aprendizagem

2016· article· pt· W2552803303 on OpenAlexaff
Cristine Maria Warmling, Fabiana Schneider Pires, Júlio Baldisserotto, Martine Lévesque

Bibliographic record

VenueRevista Bioética · 2016
Typearticle
Languagept
FieldHealth Professions
TopicEthics in medical practice
Canadian institutionsMcGill University
Fundersnot available
KeywordsHumanitiesPhysicsPsychologyPhilosophy

Abstract

fetched live from OpenAlex

Resumo O uso de tecnologias de informação e comunicação aproximou o ensino da bioética à prática profissional. O objetivo deste estudo é avaliar o objeto virtual de aprendizagem Análises de Situações Éticas, produzido e utilizado como abordagem inovadora no ensino da bioética em cursos na área da saúde. A metodologia integra análises quantitativas e qualitativas. Os participantes são estudantes que utilizaram o objeto virtual nas disciplinas ética e bioética de cursos de odontologia e fonoaudiologia. Foi aplicado questionário (questões abertas e fechadas), e as categorias analisadas relacionam-se ao uso do objeto virtual e à aprendizagem da bioética: interação, conteúdo curricular e dinâmicas de ensino-aprendizagem. Depoimentos demonstram que o material educativo proporcionou análise de situações com possíveis conflitos bioéticos e evidenciam a possibilidade de interdisciplinaridade, considerando a experiência importante na formação de profissionais da saúde. O estudo aponta para a bioética enquanto campo curricular transversal das práticas 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)0.008
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.017
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0050.010
Scholarly communication0.0180.010
Open science0.0010.011
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0140.001

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 head0.090
GPT teacher head0.444
Teacher spread0.354 · 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 designObservational
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

Citations23
Published2016
Admission routes1
Has abstractyes

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