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Record W2403460843 · doi:10.3233/978-1-60750-706-2-125

MeRiTS: Simulation-Based Training for Healthcare Professionals

2011· article· en· W2403460843 on OpenAlexaff
David Chodos, Eleni Stroulia, Sharla King

Bibliographic record

VenueStudies in health technology and informatics · 2011
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsHealth professionalsTraining (meteorology)Health careComputer scienceMedical educationPsychologyMedicinePolitical scienceGeography

Abstract

fetched live from OpenAlex

Simulation-based training has been used in numerous settings for procedural training. In this research, we focus on a method of simulation-based procedural skills training that uses virtual worlds. This method, implemented in our MeRiTS software system, models procedures using executable workflows, which are enacted by the trainee in a virtual world. The workflows may be defined by educators, or demonstrated by experts and then extracted from system logs. To demonstrate the utility of the system, we have created a scenario for training EMTs in patient rescue and transition procedures. We have pilot tested this scenario with students at a polytechnical institute, and will be conducting more rigorous testing with a range of students and institutions in the near future.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.573
Threshold uncertainty score0.599

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.250
GPT teacher head0.492
Teacher spread0.242 · 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 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

Citations7
Published2011
Admission routes1
Has abstractyes

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