Games to improve the clinical skills of nursing students: A systematic review of current evidence (Preprint)
Bibliographic record
Abstract
BACKGROUND As medical education evolves, incorporating innovative teaching methods is crucial for developing nursing students' critical thinking and problem-solving skills. Game-based learning (GBL) has gained popularity, engaging students through immersive experiences and allowing personalized learning. OBJECTIVE This systematic review aims to investigate the impact of educational games on nursing education outcomes. METHODS In this study, the authors systematically searched the four public databases (PubMed, Embase, Scopus, and Web of Science) to investigate the role of educational games in improving the clinical skills of nursing students. This paper is based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist 2020. We also checked the bias risk of selected studies by the Newcastle-Ottawa Scale (NOS) bias assessment tool. RESULTS In this study, a total of 801 articles were initially retrieved using a specified search strategy, with 39 articles remaining after applying inclusion and exclusion criteria. These articles, published between 2018 and 2023, spanned various countries and focused on diverse learning objectives, including diagnostic reasoning and cognitive skills enhancement, utilizing multiple game platforms. While many games utilized web-based platforms, few were conducted in person and some were developed in smartphone app formats. CONCLUSIONS GBL is transforming nursing education by enhancing student engagement and clinical skills through immersive experiences. Despite its advantages, GBL faces challenges such as development costs and the effect of expertise reversal. Future research should focus on multilingual studies and long-term assessments to evaluate its impact on nursing competencies and patient outcomes.
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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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".