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Record W3088691906 · doi:10.1002/aet2.10536

Retention of Critical Procedural Skills After Simulation Training: A Systematic Review

2020· review· en· W3088691906 on OpenAlexafffund
Camille Legoux, Richard Bradley Gerein, Kathy Boutis, Nicholas Barrowman, Amy C. Plint

Bibliographic record

VenueAEM Education and Training · 2020
Typereview
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsSickKids FoundationUniversity of TorontoChildren's Hospital of Eastern OntarioHospital for Sick ChildrenUniversity of Ottawa
FundersCHEO Research Institute
KeywordsObservational studyMedicineBenchmark (surveying)Physical therapySimulationComputer science

Abstract

fetched live from OpenAlex

OBJECTIVE: While short-term gains in performance of critical emergency procedures are demonstrated after simulation, long-term retention is relatively uncertain. Our objective was to determine whether simulation of critical emergency procedures promotes long-term retention of skills in nonsurgical physicians. METHODS: We searched multiple electronic databases using a peer-reviewed strategy. Eligible studies 1) were observational cohorts, quasi-experimental or randomized controlled trials; 2) assessed intubation, cricothyrotomy, pericardiocentesis, tube thoracostomy, or central line placement performance by nonsurgical physicians; 3) utilized any form of simulation; and 4) assessed skill performance immediately after and at ≥ 3 months after simulation. The primary outcome was skill performance at or above a preset performance benchmark at ≥ 3 months after simulation. Secondary outcomes included procedural skill performance at 3, 6, and ≥ 12 months after simulation. RESULTS: We identified 1,712 citations, with 10 being eligible for inclusion. Methodologic quality was moderate with undefined primary outcomes; inadequate sample sizes; and use of nonstandardized, unvalidated tools. Three studies assessed performance to a specific performance benchmark. Two demonstrated maintenance of the minimum performance benchmark while two demonstrated significant skill decay. A significant decline in the mean performance scores from immediately after simulation to 3, 6, and ≥ 12 months after simulation was observed in four of four, three of four, and two of five studies, respectively. Scores remained significantly above baseline at 3, 6, and ≥ 12 months after simulation in three of four, three of four, and four of four studies, respectively. CONCLUSION: There were a limited number of studies examining the retention of critical skills after simulation training. While there was some evidence of skill retention after simulation, overall most studies demonstrated skill decline over time.

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)0.010
metaresearch head score (Gemma)0.052
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 score0.010
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.052
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0080.005
Bibliometrics0.0080.011
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.134
GPT teacher head0.468
Teacher spread0.334 · 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

Citations46
Published2020
Admission routes2
Has abstractyes

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