Virtual simulation for teaching cardiology in nursing: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Virtual simulation (VS) can be an effective learning strategy in the context of nursing education on cardiovascular disease; however, its use in teaching cardiology in nursing is less studied. The objective of this scoping review is to map the use of VS for teaching cardiology in nursing. METHODS AND ANALYSIS: This scoping review will be conducted according to the Joanna Briggs Institute methods, and the results will be reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews checklist. Eight databases will be searched: MEDLINE (NCBI/PubMed), Cumulative Index to Nursing and Allied Health Literature, Web of Science, Latin American and Caribbean Literature in Health Sciences, Spanish Bibliographic Index of Health Sciences, Database of Nursing, EMBASE and Google Scholar from inception to 31 July 2024. This study will include any existing peer-reviewed literature and grey literature. There will be no time or language restrictions. Two reviewers will screen and select the articles independently, and when there are differences, they will be resolved with a third opinion. When appropriate, broad themes and categories derived from the review questions will be accompanied by other illustrative formats (eg, tables or graphs, word clouds and infographics). ETHICS AND DISSEMINATION: This research project does not require ethical committee approval. The study is part of a cooperative research project between researchers from the Federal University of Piauí, Northeast of Brazil, and Queen's University, Ontario, Canada, to develop and seek evidence of content validity of a VS game about valvular heart disease. The protocol and review will be published in peer-reviewed journals. REGISTRATION DETAILS: Open Science Framework (https://doi.org/10.17605/OSF.IO/S3UMH).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.113 | 0.091 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.013 | 0.010 |
| Bibliometrics | 0.019 | 0.012 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.083 | 0.019 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".