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Record W4321384465 · doi:10.1590/1983-80422022304566pt

Recursos fílmicos e ensino da bioética nas ciências do movimento humano

2022· article· pt· W4321384465 on OpenAlexaff
Luciana Teixeira Waltrick, Fernando Hellmann, Gelcemar Oliveira Farias, Alcyane Marinho

Bibliographic record

VenueRevista Bioética · 2022
Typearticle
Languagept
FieldHealth Professions
TopicFilm in Education and Therapy
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsHumanitiesPhilosophyPolitical sciencePsychology

Abstract

fetched live from OpenAlex

Resumo Por meio de estudo descritivo-exploratório realizado com egressos do Programa de Pós-graduação em Ciências do Movimento Humano da Universidade do Estado de Santa Catarina, buscou-se compreender os significados dos filmes no ensino da bioética e identificar obras cinematográficas de temas bioéticos relacionados à atividade física e à saúde nas ciências do movimento humano. Utilizaram-se entrevistas semiestruturadas, cujos dados foram analisados pela análise de conteúdo. As categorias a priori partiram dos objetivos e as respostas dos participantes geraram as subcategorias a posteriori , organizadas em: contribuições sobre o aprendizado de bioética para o Programa de Pós-graduação em Ciências do Movimento Humano e para a vida profissional; e percepção sobre o uso de recursos fílmicos como recurso pedagógico, incluindo sugestões de filmes e temáticas próprias ao curso. Os recursos fílmicos com temáticas próprias tornam o aprendizado mais significativo e prazeroso, possibilitando aproximações das realidades das profissões dos alunos aos conteúdos bioéticos, facilitando tal aprendizado.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, 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 score0.452
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0040.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0760.006

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 head0.075
GPT teacher head0.403
Teacher spread0.328 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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