An Investigation of Nursing Students’ Experience With an Evidence-Based Practice Serious Game: A Transversal Study
Bibliographic record
Abstract
Background: Integrating evidence-based practice (EBP) into nursing education is a complex challenge, as traditional teaching methods often lack experiential learning opportunities. Serious games have emerged as promising tools to enhance engagement, critical thinking, and skill acquisition in health care student education. EviGame, a serious game designed to introduce nursing students to EBP, fosters decision-making, autonomy, and structured feedback within an interactive learning environment. Understanding how students engage with such games and the factors that influence their experiences is essential for exploring their learning dynamics. Purpose: This exploratory cross-sectional study aims to a) investigate nursing students’ learning experiences with EviGame using the Game Experience Questionnaire – Core Module (GEQ–CM) (IJsselsteijn et al., 2013) and b) explore the relationships between overall GEQ–CM scores and individual factors such as age, gender, prior gaming experience, fatigue, and stress levels. Methods: The study was conducted in October 2022 with second-year undergraduate nursing students at a Swiss university. Participants played EviGame and subsequently completed the GEQ–CM, assessing seven dimensions of game experience: competence, immersion, flow, positive affect, negative affect, tension/annoyance, and challenge. Additional demographic and psychometric data were collected. Data analysis included descriptive statistics, Spearman’s rank correlation, and Mann–Whitney U tests. Results: Among the 146 students enrolled in the course, 43 participated in the study, with 37 completing the questionnaire. Students reported high levels of competence (M = 3.28) and positive affect (M = 3.18), suggesting confidence and enjoyment in the game. The low challenge score (M = 2.39) indicates that the game lacked sufficient difficulty for some learners. Significant positive correlations were observed between the overall GEQ–CM score and both age (rs = 0.4881; p = 0.0022) and stress level (rs = 0.6316; p < 0.001). Women demonstrated higher overall engagement than men (z = 2.961; p = 0.0031). Prior gaming experience did not yield significant effects on results. Conclusion: The study indicates that EviGame is generally well received by nursing students, particularly regarding competence and positive affect. However, the low challenge score underscores the need for adaptive scenarios and dynamic difficulty adjustments to sustain engagement and cognitive involvement. The study also highlights the influence of age and stress on students’ experiences, with women reporting higher engagement levels. These insights provide valuable guidance for refining serious games in nursing education. Further research should assess the long-term effects on clinical reasoning and the integration of serious games in nursing curricula.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".