Development of virtual simulation games about wound assessment and management for nurses and nursing students
Bibliographic record
Abstract
Background Nurses require competency in wound assessment and management, yet evidence indicates nurses’ knowledge in wound care remains inadequate. Additionally, nursing students may not have exposure to many wound types. We proposed virtual simulation games (VSGs) to address these gaps as VSGs are engaging and support development of clinical competence and judgment. Innovation Our team of educators, wound care experts, and nursing students co-created 4 VSGs to support wound care education using the Canadian Alliance of Nurse Educators using Simulation (CAN-Sim) VSG design process. Filming of the VSGs was completed using a Go-Pro camera, and VSGs were assembled using Articulate Storyline 360 software. Implications We created 4 VSGs related to assessment and management of pressure injuries, diabetic foot ulcers, venous leg ulcers, and surgical wounds. Usability testing led to improvements in VSG flow, functionality and text. Conclusions We developed a comprehensive wound care module that nurse educators can implement within their nursing programs. The module is available open access at: https://woundnursing.ca/ .
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".