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Record W2987806675 · doi:10.1002/aet2.10412

A Modified Delphi Study to Prioritize Content for a Simulation‐based Pediatric Curriculum for Emergency Medicine Residency Training Programs

2019· article· en· W2987806675 on OpenAlexaff
Jennifer Mitzman, Ilana Bank, Rebekah Burns, Michael C. Nguyen, Pavan Zaveri, Michael Falk, Manu Madhok, Ann Dietrich, Jessica Wall, Muhammad Waseem, Teresa Wu, Alisa McQueen, Cynthia Peng, Brian J. Phillips, Francesca Bullaro, Cindy Chang, Sam Shahid, David P. Way, Marc Auerbach

Bibliographic record

VenueAEM Education and Training · 2019
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsMcGill UniversityMontreal Children's Hospital
FundersNational Center for Advancing Translational Sciences
KeywordsCurriculumDelphi methodMedical educationSet (abstract data type)StakeholderResidency trainingDelphiMedicineFamily medicinePsychologyComputer sciencePedagogy

Abstract

fetched live from OpenAlex

OBJECTIVES: Pediatric training is an essential component of emergency medicine (EM) residency. The heterogeneity of pediatric experiences poses a significant challenge to training programs. A national simulation curriculum can assist in providing a standardized foundation of pediatric training experience to all EM trainees. Previously, a consensus-derived set of content for a pediatric curriculum for EM was published. This study aimed to prioritize that content to establish a pediatric simulation-based curriculum for all EM residency programs. METHODS: Seventy-three participants were recruited to participate in a three-round modified Delphi project from 10 stakeholder organizations. In round 1, participants ranked 275 content items from a published set of pediatric curricular items for EM residents into one of four categories: definitely must, probably should, possibly could, or should not be taught using simulation in all residency programs. Additionally, in round 1 participants were asked to contribute additional items. These items were then added to the survey in round 2. In round 2, participants were provided the ratings of the entire panel and asked to rerank the items. Round 3 involved participants dichotomously rating the items. RESULTS: A total of 73 participants participated and 98% completed all three rounds. Round 1 resulted in 61 items rated as definitely must, 72 as probably should, 56 as possibly could, 17 as should not, and 99 new items were suggested. Round 2 resulted in 52 items rated as definitely must, 91 as probably should, 120 as possibly could, and 42 as should not. Round 3 resulted in 56 items rated as definitely must be taught using simulation in all programs. CONCLUSIONS: The completed modified Delphi process developed a consensus on 56 pediatric items that definitely must be taught using simulation in all EM residency programs (20 resuscitation, nine nonresuscitation, and 26 skills). These data will serve as a targeted needs assessment to inform the development of a standard pediatric simulation curriculum for all EM residency programs.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.092
metaresearch head score (Gemma)0.084
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.092
Threshold uncertainty score0.486

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0920.084
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0050.003
Scholarly communication0.0020.003
Open science0.0020.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.231
GPT teacher head0.450
Teacher spread0.219 · 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 source (direct Gemma or distilled Codex), 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".

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Citations18
Published2019
Admission routes1
Has abstractyes

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