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Record W2961977491 · doi:10.1055/s-0039-1693465

Simulation in Neonatal-Perinatal Medicine Fellowship Programs

2019· article· en· W2961977491 on OpenAlexaff
Taylor Sawyer, Theodora A. Stavroudis, Anne Ades, Rita Dadiz, Christiane E.L. Dammann, Louis P. Halamek, Ahmed Moussa, Lamia Soghier, Arika G. Gupta, Sofia Aliaga, Rachel Umoren, Heather French

Bibliographic record

VenueAmerican Journal of Perinatology · 2019
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsAccreditationMedicineGraduate medical educationCurriculumMedical educationNeonatal resuscitationScope (computer science)Simulation trainingFamily medicineNursingResuscitationEmergency medicineSimulationPsychologyPedagogy

Abstract

fetched live from OpenAlex

OBJECTIVE: This study aimed to investigate the use of simulation in neonatal-perinatal medicine (NPM) fellowship programs. STUDY DESIGN: This was a cross-sectional survey of program directors (PDs) and simulation educators in Accreditation Council for Graduate Medical Education (ACGME) accredited NPM fellowship programs. RESULTS: Responses were received from 59 PDs and 52 simulation educators, representing 60% of accredited programs. Of responding programs, 97% used simulation, which most commonly included neonatal resuscitation (94%) and procedural skills (94%) training. The time and scope of simulation use varied significantly. The majority of fellows (51%) received ≤20 hours of simulation during training. The majority of PDs (63%) wanted fellows to receive >20 hours of simulation. Barriers to simulation included lack of faculty time, experience, funding, and curriculum. CONCLUSION: While the majority of fellowship programs use simulation, the time and scope of fellow exposure to simulation experiences are limited. The creation of a standardized simulation curriculum may address identified barriers to simulation.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.141
Threshold uncertainty score0.625

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.024
GPT teacher head0.367
Teacher spread0.343 · 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

Citations10
Published2019
Admission routes1
Has abstractyes

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