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Record W2505492291 · doi:10.5430/jnep.v6n12p12

Simulation in emergency nursing education: An integrative review

2016· article· en· W2505492291 on OpenAlexvenueno aff
Caio Guilherme S. Bias, Lorene Soares Agostinho, Roberta Pereira Coutinho, Genesis de Souza Barbosa

Bibliographic record

VenueJournal of Nursing Education and Practice · 2016
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsCINAHLInclusion (mineral)PortugueseInclusion and exclusion criteriaNursingEmergency nursingMEDLINENurse educationWork (physics)MedicinePsychologyEmergency departmentPsychological interventionAlternative medicineEngineeringPolitical science

Abstract

fetched live from OpenAlex

Introduction : The growing development of technological resources has allowed simulations to be increasingly used in nursing education. Aim: assessing the scientific literature on the use of simulations in emergency nursing education. Methods : Integrative literature review conducted in databases such as LILACS, MEDLINE, ERIC, and CINAHL, including full-text articles published in English, Portuguese or in Spanish, between 2005 and 2015. Results : After applying the inclusion and exclusion criteria, the current study selected six primary studies supporting three categories, namely: developing skills and assuredness to work in emergencies; types of simulation used in emergency training; and the impact of simulation on self-confidence and on satisfaction. Conclusions : Simulation is a satisfactory teaching methodology, which shows positive effects on the responses to emergencies, and it contributes to the participants’ assuredness and to the improvement of their skills and self-confidence.

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)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.950
Threshold uncertainty score0.600

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.142
GPT teacher head0.557
Teacher spread0.415 · 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 designOther design
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

Citations12
Published2016
Admission routes1
Has abstractyes

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