Games to Improve the Clinical Skills of Nursing Students: Systematic Review of Current Evidence
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 aimed to investigate the impact of educational games on outcomes of clinical nursing skills. Methods: In this study, the authors systematically searched the 4 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 PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines. We also checked the bias risk of selected studies by the Newcastle-Ottawa Scale (NOS) bias assessment tool. Results: In this study, 801 articles were initially retrieved using a specified search strategy, with 38 remaining after applying inclusion and exclusion criteria. The final included studies published between 2017 and 2023 spanned various countries and focused on diverse learning objectives. A broad range of learning objectives, such as developing diagnostic reasoning, enhancing knowledge and cognitive skills, and improving training methods, can be supported by a game-based platform. We also showed that while many games used web-based platforms, few were conducted in person, and some were developed in app formats for smartphones. 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. Developing standardized assessment tools will help unify definitions and improve the comparability of research findings, ultimately enhancing the evidence base for GBL's effectiveness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".