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Record W2767744227 · doi:10.1080/14703297.2017.1399808

Proposing a model to embed a simulated-person methodology program within higher education

2017· article· en· W2767744227 on OpenAlexaff
Eva Peisachovich, Laura Jayne Nelles, Susan Murtha, Celia Popovic, Iris Epstein, Celina Da Silva

Bibliographic record

VenueInnovations in Education and Teaching International · 2017
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsMount Sinai HospitalYork University
FundersUniversity of Florida
KeywordsExperiential learningComputer scienceWork (physics)Teaching methodField (mathematics)Mathematics educationPedagogyPsychologyEngineering

Abstract

fetched live from OpenAlex

This paper provides an overview of a collaborative model with which to equip educators with the tools to apply and embed simulated person (SP) methodology in their teaching. Our aim is to expand opportunities to foster student success through simulation by embedding the training of simulators and faculty within undergraduate education. The development of this model supports the application of SP methodology within higher education and involves a) developing an applied elective course for undergraduate theatre students that will provide them with the opportunity to learn simulation methodology and to develop skills that will prepare them to work in the field of simulation as SPs, b) delivering workshops to educate faculty to work with and effectively utilize SPs as a pedagogical approach for teaching undergraduate students, and c) using fundamental principles of experiential learning to provide educators, SPs, and students with opportunities to work collaboratively across disciplines.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.332
Threshold uncertainty score0.848

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.195
GPT teacher head0.518
Teacher spread0.323 · 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.

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

Citations5
Published2017
Admission routes1
Has abstractyes

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