Simulation-Based Learning Supported by Technology to Enhance Critical Thinking in Nursing Students: Protocol for a Scoping Review
Bibliographic record
Abstract
BACKGROUND: Critical thinking is a crucial skill in the nursing profession, so teaching strategies and methodology must be carefully considered when training and preparing nursing students to think critically. Studies on simulation-based learning supported by technology are increasing in nursing education, but no scoping reviews have mapped the literature on simulation-based learning supported by technology to enhance critical thinking in nursing students. OBJECTIVE: The proposed scoping review aims to systematically map research on the use of simulation-based learning supported by technology to enhance critical thinking in nursing students. METHODS: The proposed scoping review will use the framework established by Arksey and O'Malley and will be reported according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) extension for scoping reviews. A systematic, comprehensive literature search was performed in the LILACS, ERIC, MEDLINE, EMBASE, PsycINFO, and Web of Science databases. Pairs of authors independently selected the articles by screening titles, abstracts, full-text papers, and extract data. The data will be analyzed and thematically categorized. RESULTS: The development of a comprehensive and systematic search strategy was completed in June 2021. The database searches were performed in July 2021, and the screening of titles and abstracts was completed in September 2021. Charting the data began in February 2022. Analysis and synthesis will be performed sequentially, and the scoping review is expected to be complete by May 2023. CONCLUSIONS: The results of this proposed scoping review may identify gaps in the literature and provide an overview of research on the topic of simulation-based learning supported by technology to enhance critical thinking in nursing students. The research may identify nursing students' reported barriers and enablers for learning critical thinking skills through simulation-based learning supported by technology, and the results may help educators enhance their educational approach through knowledge of students' firsthand experiences and further development of successful teaching strategies in nursing education. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/36725.
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 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 | 0.104 | 0.098 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.013 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.064 | 0.015 |
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".