Developing virtual gaming simulations for complex clients with substance use through international collaborations
Bibliographic record
Abstract
Background The aim of this paper was to describe how six nurse educators and three nursing students from Canada, Northern Ireland and Finland developed a virtual gaming simulation (VGS) on a client with a complex medication profile to fill a gap in an undergraduate nursing curriculum. The VGS navigates learners to engage in a scenario with a client admitted for an acute exacerbation of chronic obstructive pulmonary disease. Method The international collaboration occurred through continuous dialogue and reflective practice to ensure the inclusion of country-specific practices and laws. Lessons Learned The international collaboration allowed educators and students to take a unified approach to address country specific best practices, such as medication administration and the intricacies of cannabis legality. A theoretical lens enhanced the development and structure of the VGS. The student voice provided a holistic perspective. Conclusion International collaborations with nurse educators and students can enhance the VGS design process by facilitating diverse perspectives. This VGS invited learners to engage in a clinical scenario to learn about the importance of providing person-centered care to a client with a complex profile.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".