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Record W3032760909 · doi:10.3928/00220124-20200514-05

Teaching Crisis Resource Management Skills to Nurses Using Simulation

2020· article· en· W3032760909 on OpenAlexaboutno aff
Amanda Lucas, Marie Edwards, Nicole Harder, Lawrence M. Gillman

Bibliographic record

VenueThe Journal of Continuing Education in Nursing · 2020
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsChecklistCompetence (human resources)FidelitySituation awarenessObservational studyPsychologySituational ethicsRating scaleMedical educationAffect (linguistics)Crisis interventionCrisis managementNursingApplied psychologyMedicineComputer scienceEngineeringSocial psychologyManagementDevelopmental psychology

Abstract

fetched live from OpenAlex

BACKGROUND: Education programs teaching crisis resource management (CRM) skills (problem solving, situational awareness, resource utilization, communication, and leadership) have been shown to positively affect learner competence in handling crisis events. As part of an education program, a high-fidelity simulation program was used as a learning intervention to teach these skills to practicing nurses. METHOD: In this repeated-measures observational study, 11 RNs were evaluated at four time points, measuring the effect of an education program on observed performance of CRM skills. Performance was measured using the Ottawa Global Rating Scale and a checklist tool. RESULTS: Statistically significant changes in mean scores occurred between times one and two, and nonstatistically significant improvement occurred in means overall. CONCLUSION: This study adds evidence of the effectiveness of high-fidelity simulation education and highlights the need for further research. [J Contin Educ Nurs. 2020;51(6):257-266.].

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)0.001
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.375
Threshold uncertainty score0.448

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.021
GPT teacher head0.409
Teacher spread0.388 · 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 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".

Quick stats

Citations6
Published2020
Admission routes1
Has abstractyes

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