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Record W3020157081 · doi:10.2196/16363

Improving the Use of Simulation in Nursing Education: Protocol for a Realist Review

2020· review· en· W3020157081 on OpenAlexvenueno aff
Torbjørg Meum, Åshild Slettebø, Mariann Fossum

Bibliographic record

VenueJMIR Research Protocols · 2020
Typereview
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsnot available
FundersUniversitetet i Agder
KeywordsDelphi methodBachelorSystematic reviewProcess (computing)Foundation (evidence)Protocol (science)PsychologyComputer scienceNursingMedical educationManagement scienceEngineering ethicsMedicineMEDLINEEngineeringAlternative medicinePolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: Nursing education has evolved in line with societal needs, and simulation-based learning (SBL) is increasingly being used to bridge the gap between practice and education. Previous literature reviews have demonstrated the effectiveness of using SBL in nursing education. However, there is a need to explore how and why it works to expand the theoretical foundation of SBL. Realist reviews are a theory-based approach to synthesizing existing evidence on how complex programs work in particular contexts or settings. OBJECTIVE: This review aims to understand how, why, and in what circumstances the use of simulation affects learning as part of the bachelor's program in nursing. METHODS: A realist review will be conducted in accordance with the realist template for a systematic review. In particular, we will identify and explore the underlying assumption of how SBL is supposed to work, that is, identify and explore program theories of SBL. The review will be carried out as an iterative process of searching, appraising, and synthesizing the evidence to uncover theoretical concepts that explain the causal effects of SBL. In the final section of the review, we will involve stakeholders in the Norwegian community in a web-based Delphi survey to ensure that the emerging theoretical framework derived from the published literature aligns with stakeholders' experience in practice. RESULTS: The Norwegian Centre for Research Data (project number 60415) has approved the study. We have performed an initial literature search, whereas quality appraisal and data extraction are ongoing processes. CONCLUSIONS: The final outcome of the review is anticipated to extend the theoretical foundation for using simulation as an integrated component of the bachelor's program in nursing. Furthermore, the findings will be used to produce a briefing document containing guidance for national stakeholders in the community of simulation-based nursing education. Finally, the review findings will be disseminated in a peer-reviewed journal as well as national and international conferences. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/16363.

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)0.132
metaresearch head score (Gemma)0.172
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.868
Threshold uncertainty score0.698

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1320.172
Meta-epidemiology (narrow)0.0050.005
Meta-epidemiology (broad)0.0130.013
Bibliometrics0.0160.013
Science and technology studies0.0050.005
Scholarly communication0.0080.009
Open science0.0050.006
Research integrity0.0080.009
Insufficient payload (model declined to judge)0.0850.019

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 head0.793
GPT teacher head0.735
Teacher spread0.058 · 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.

Study designNot applicable
DomainMethods
GenreProtocol

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

Citations11
Published2020
Admission routes1
Has abstractyes

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