Serious games in nursing education: A systematic review of current evidence
Bibliographic record
Abstract
• Serious Games have become increasingly popular in nursing education as a way to enhance learning and improve clinical skills. • Serious games have the potential to transform nursing education by providing an engaging and interactive learning experience. • By immersing learners in realistic clinical scenarios, Serious Games can enhance critical thinking, problem-solving, and decision-making skills. • To maximize Serious Games impact, it is critical to carefully design and implement these games, align them with specific learning objectives, and evaluate their effectiveness. Serious games (SGs) are a new concept in education that focuses on improving the effectiveness of teaching methods to provide a digital area for learning. We aimed to review current evidence of using SGs applications in nursing education. Data extraction was performed following two steps of screening/selection and then applying inclusion/ exclusion criteria. PRISMA checklist and the Newcastle-Ottawa Scale were utilized in the review. A total of 41 articles from 2015 to 2024 were included in this study. Results showed that nurse educators have attempted to use innovative game-based approaches to improve students’ knowledge, decision-making, practical skills, and teamwork. The nurses who participated and played these games often demonstrated a meaningful increase in their knowledge or exam scores when compared to a control group of peer nurses who underwent a routine traditional education or other modalities of digital platforms like online webinars. Serious games have the potential to transform nursing education by providing an engaging and interactive learning experience. By immersing learners in realistic clinical scenarios, these games can enhance critical thinking, problem-solving, and decision-making skills.
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 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".