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Record W2792394825 · doi:10.1515/ijnes-2017-0050

The State of Knowledge Regarding the Use of Simulation in Pre-Licensure Nursing Education: A Mixed Methods Systematic Review

2018· review· en· W2792394825 on OpenAlexaff
Joanne Olson, Pauline Paul, Gerri Lasiuk, Sandra Davidson, Barbara Wilson-Keates, Rebecca J. Bartlett Ellis, Nichole Marks, Maryam Nesari, Winnie Savard

Bibliographic record

VenueInternational Journal of Nursing Education Scholarship · 2018
Typereview
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsAlberta Health ServicesUniversity of ReginaAlberta HealthUniversity of Alberta
Fundersnot available
KeywordsCINAHLLicensureRelevance (law)MEDLINEInclusion (mineral)FidelityMedical educationNursing researchQuality (philosophy)Research designNursingMedicinePsychologyComputer sciencePsychological interventionSociologySocial psychology

Abstract

fetched live from OpenAlex

This project is a mixed-methods systematic review on the use of simulation in pre-licensure nursing. This research question guided this review: What is the best evidence available upon which to base decisions regarding the use of simulation experiences with pre-licensure nursing students? Searches of CINAHL Plus with Full Text, MEDLINE, and ERIC were performed to identify relevant literature. These searches yielded 1220 articles. After duplicates were removed and titles and abstracts were reviewed for relevance to the inclusion criteria, the remaining 852 articles were independently assessed for quality by pairs of researchers. Forty-seven articles were retained. Findings were grouped into research using high-, medium-, and low-fidelity simulations and a group where researchers included several or all types of simulation. The conclusion is that insufficient quality research exists to guide educators in making evidence-based decisions regarding simulation. More rigorous and multi-site research is needed.

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)0.007
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.822
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.309
GPT teacher head0.594
Teacher spread0.285 · 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.

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

Citations13
Published2018
Admission routes1
Has abstractyes

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