MétaCan
Menu
Back to cohort
Record W3012975130 · doi:10.1097/sih.0000000000000424

Formal Training Efforts to Develop Simulation Educators

2020· review· en· W3012975130 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSimulation in Healthcare The Journal of the Society for Simulation in Healthcare · 2020
Typereview
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsDurham College
Fundersnot available
KeywordsMedical educationInclusion (mineral)Process (computing)Training (meteorology)Best practiceStatement (logic)Computer sciencePsychologyMedicinePolitical science

Abstract

fetched live from OpenAlex

STATEMENT: Formal training for educators who use simulation-based education (SBE) is required by standards of best practice, simulation guidelines, regulatory, and accrediting bodies. Training efforts to establish educator competency for SBE are being offered. However, a systematic review of this body of literature has yet to be conducted. The purpose of this integrative review was to appraise formal training efforts of educators who use SBE. The aims were to summarize the training topics, describe the structure of training programs, and explore evaluation methods of educators. The New World Kirkpatrick Model guided the review. A PRISMA search approach yielded 2007 citations of which 38 met inclusion criteria. Analysis supports a formalized training process that uses a combination of didactic material, time for repetitive practice, and ongoing feedback with longitudinal and scaffolded delivery approaches. An identified gap in the literature is threshold levels for determining competency of educators. Recommendations for planning simulation training programs are provided.

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.

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)0.006
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.847
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0010.007
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.003
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 head0.146
GPT teacher head0.469
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