MétaCan
Menu
Back to cohort
Record W2950571259 · doi:10.6084/m9.figshare.7507823

Teaching Bioethics: evaluation of a virtual learning object

2018· dataset· en· W2950571259 on OpenAlexaff
Cristine Maria Warmling, Fabiana Schneider Pires, Júlio Baldisserotto, Martine Lévesque

Bibliographic record

VenueLA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · 2018
Typedataset
Languageen
FieldEngineering
TopicBiomedical and Engineering Education
Canadian institutionsMcGill University
Fundersnot available
KeywordsBioethicsObject (grammar)Computer sciencePsychologyMathematics educationCognitive scienceHuman–computer interactionArtificial intelligenceBiologyGenetics

Abstract

fetched live from OpenAlex

Abstract The use of information and communication technologies brought together the teaching of bioethics and professional practice. The objective of the study is to evaluate the Virtual Learning Object ─ Analysis of Ethical Situations, developed and used as an innovative approach to the teaching of bioethics in courses in the field of health. The methodology integrates quantitative and qualitative analysis. Participants are students who used the virtual object in the disciplines of Ethics and Bioethics of Dentistry and Speech Therapy courses. A questionnaire (open and closed questions) was applied, and the categories analyzed related to the use of the virtual object and learning of bioethics: interaction, curriculum content, and teaching and learning dynamics. Testimonials show that the educational material provided analysis of situations with potential bioethical conflicts and demonstrated the possibility of practicing interdisciplinarity, considering this experience important in the training of health professionals. The study points to bioethics as a cross-curricular field of health practices.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.557
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.029
GPT teacher head0.276
Teacher spread0.246 · 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 designNot applicable
Domainnot available
GenreDataset

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

Citations6
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueLA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)Same topicBiomedical and Engineering EducationFrench-language works237,207