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Record W2418343356 · doi:10.1111/acem.13019

Development of an Emergency Medicine Simulation Fellowship Consensus Curriculum: Initiative of the Society for Academic Emergency Medicine Simulation Academy

2016· article· en· W2418343356 on OpenAlexaff
Alise Frallicciardi, Samreen Vora, Suzanne Bentley, Nur‐Ain Nadir, Michael Cassara, Danielle Hart, Chan Park, Adam Cheng, Amish Aghera, Tiffany Moadel, Valerie Dobiesz

Bibliographic record

VenueAcademic Emergency Medicine · 2016
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsAlberta Children's Hospital
Fundersnot available
KeywordsCurriculumMedicineDelphi methodStandardizationMedical educationDelphiTransparency (behavior)Computer sciencePedagogyPsychology

Abstract

fetched live from OpenAlex

OBJECTIVES: There is currently no consolidated list of existing simulation fellowship programs in emergency medicine (EM). In addition, there are no universally accepted or expected standards for core curricular content. The objective of this project is to develop consensus-based core content for EM simulation fellowships to help frame the critical components of such training programs. METHODS: This paper delineates the process used to develop consensus curriculum content for EM simulation fellowships. EM simulation fellowship curricula were collected. Curricular content was reviewed and compiled by simulation experts and validated utilizing survey methodology, and consensus was obtained using a modified Delphi methodology. RESULTS: Fifteen EM simulation fellowship curricula were obtained and analyzed. Two rounds of a modified Delphi survey were conducted. The final proposed core curriculum content contains 47 elements in nine domains with 14 optional elements. CONCLUSION: The proposed consensus content will provide current and future fellowships a foundation on which to build their own specific and detailed fellowship curricula. Such standardization will ultimately increase the transparency of training programs for future trainees and potential employers.

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)0.004
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.193
GPT teacher head0.471
Teacher spread0.278 · 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 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

Citations35
Published2016
Admission routes1
Has abstractyes

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