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Record W3209324435 · doi:10.1007/s40670-021-01446-0

Rapid Cycle Deliberate Practice in Healthcare Simulation: a Scoping Review

2021· review· en· W3209324435 on OpenAlexaff
Carly Ng, Nadia Primiani, Ani Orchanian‐Cheff

Bibliographic record

VenueMedical Science Educator · 2021
Typereview
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsInclusion (mineral)Computer scienceMedical educationPsychologyMathematics educationMedicineSocial psychology

Abstract

fetched live from OpenAlex

Rapid cycle deliberate practice (RCDP) is a type of simulation-based medical education (SBME) where learners cycle between deliberate practice and directed feedback until skill mastery is achieved before progressing to subsequent learning objectives. This scoping review examines and summarizes the literature on RCDP, compares RCDP to other modes of instruction, and identifies knowledge gaps for future research. Of the 1224 articles identified, 23 studies met inclusion criteria. The studies varied in design, RCDP technique implementation strategies, and outcome measures. RCDP is associated with positive outcomes in immediate learner performance. It is unclear if RCDP is superior to traditional 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 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)0.011
metaresearch head score (Gemma)0.048
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.011
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.048
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0100.012
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.148
GPT teacher head0.559
Teacher spread0.410 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations59
Published2021
Admission routes1
Has abstractyes

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