Evaluating the Effect of a Pressure Injury Virtual Simulation Game on Undergraduate Nursing Students’ Knowledge
Bibliographic record
Abstract
With the ageing population living longer and the prevalence of chronic diseases, pressure injuries are a serious health concern. The literature has identified some long-standing gaps in undergraduate nursing wound care education and its impact on undergraduate nursing students learning. With virtual simulation games (VSGs) having positive outcomes as experiential learning tools, we conducted a study to evaluate the effect of a pressure injury virtual simulation game on undergraduate nursing students’ knowledge. In this pilot study, we employed a one-group pre-test-post-test quasi-experimental design among 19 undergraduate nursing students at Queen’s University, Kingston, Ontario. A modified version of the Pieper-Zulkowski Pressure Ulcer Knowledge Test was used as a tool to assess students’ knowledge about pressure injuries (PIs) before and after the virtual simulation game. In the VSG, participants took on the role of a wound care nurse as they completed a comprehensive assessment and treatment of a coccyx pressure injury (PI). At the end of the game, consenting students participated in a virtual debriefing session. We hypothesized that the VSG would significantly increase the knowledge of the assessment and treatment of PIs among undergraduate nursing students. The dependent t-test comparing the students’ pre-test and post-test knowledge scores showed a statistically significant improvement in the knowledge scores from a pre-test mean score of 9.63 to a post-test mean score of 11.53. However, the independent t-test showed no statistically significant difference in students’ knowledge scores between the lower-year and upper-year undergraduate nursing student groups. At the end of the study, the following themes emerged from the virtual debriefing sessions: self-reflection, active learning, knowledge acquisition, knowledge application, holistic patient care, taking the initiative to acquire or advance one’s knowledge, and recognizing the abnormal. VSGs increase nursing students’ knowledge and help them make appropriate wound care decisions in their clinical placements. As virtual simulation evolves, we need to continue to generate more robust, well-designed studies with larger sample sizes to support the use of VSGs in nursing education.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".