MétaCan
Menu
Back to cohort
Record W2802786387 · doi:10.1177/0844562118767786

High-Fidelity Simulation of Pediatric Emergency Care: An Eye-Opening Experience for Baccalaureate Nursing Students

2018· article· en· W2802786387 on OpenAlexaffvenue
Sandra P. Small, Peggy Amanda Colbourne, Cynthia Murray

Bibliographic record

VenueCanadian Journal of Nursing Research · 2018
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsFeelingTeamworkContext (archaeology)FidelityNursingPsychologyPediatric nursingMedical educationMedicineComputer scienceSocial psychology

Abstract

fetched live from OpenAlex

Background Little attention has been given to in-depth examination of what high-fidelity simulation is like for nursing students within the context of a pediatric emergency, such as a cardiopulmonary arrest. It is possible that such high-fidelity simulation could provoke in nursing students intense psychological reactions. Purpose The purpose of this study was to learn about baccalaureate nursing students' lived experience of high-fidelity simulation of pediatric cardiopulmonary arrest. Method Phenomenological methods were used. Twenty-four interviews were conducted with 12 students and were analyzed for themes. Results The essence of the experience is that it was eye-opening. The students found the simulation to be a surprisingly realistic nursing experience as reflected in their perceiving the manikin as a real patient, thinking that they were saving their patient's life, feeling like a real nurse, and feeling relief after mounting stress. It was a surprisingly valuable learning experience in that the students had an increased awareness of the art and science of nursing and increased understanding of the importance of teamwork and were feeling more prepared for clinical practice and wanting more simulation experiences. Conclusion Educators should capitalize on the benefits of high-fidelity simulation as a pedagogy, while endeavoring to provide psychologically safe learning.

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)0.001
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 score0.049
Threshold uncertainty score0.753

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
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 head0.216
GPT teacher head0.569
Teacher spread0.353 · 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

Citations17
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of Nursing ResearchSame topicSimulation-Based Education in HealthcareFrench-language works237,207