The Integration of Virtual Simulation Gaming into Nursing Resuscitation Education: A Pilot Randomised Controlled Trial
Bibliographic record
Abstract
Background The Heart and Stroke Foundation of Canada (HSFC) reports that up to 40,000 Canadians experience cardiac arrest per year (HSFC, 2016). Of these 40,000, 13.7 to 22.3 per cent achieve return of spontaneous circulation but only 10.5 per cent survive to discharge (Benjamin et al., 2019; Girotra et al., 2012). Global health authorities have set a target of doubling rates of survival from cardiac arrest by 2020 (Diercks, Al-Khatib, & Link, 2016). Meeting this target will rely heavily on well-educated and highly skilled nurses. Virtual simulation gaming (VSG) is a promising educational tool that helps faculty convey resuscitation education in an engaging and effective way (Borg Sapiano, Sammut, & Trapani, 2018). With the goal of helping students learn how to care for patients in cardiac arrest, a resuscitation-oriented VSG was created. The objective of this thesis was to examine the effect of VSG on nursing students’ during a resuscitation-oriented clinical simulation. Research Question In senior-level undergraduate nursing students undergoing resuscitation education, does VSG pre-simulation preparation, when compared to traditional pre-simulation preparation, result in greater student performance during a resuscitation-oriented clinical simulation, as evaluated through the use of a 12-item performance checklist? Method Twenty (20) senior-level undergraduate nursing students were recruited to participate in a pilot randomised controlled trial. The trial compared student performance during a resuscitation-oriented clinical simulation. Students were provided either the HSFC’s Basic Life Support (BLS) and Advanced Cardiovascular Life Support (ACLS) guidelines or a resuscitation-oriented VSG in combination with the HSFC’s BLS and ACLS guidelines. Results A Mann-Whitney U test reported significantly greater overall performance by the intervention group (Median [Mdn] = 12) than the control group (Mdn = 8) during a resuscitation-oriented clinical simulation, as evaluated through a 12-item performance checklist (U = 12, p = .003). Conclusion The results of this work indicate that inclusion of a VSG as an adjunct pre-simulation preparation tool for resuscitation education could have a positive impact on students’ performance during clinical simulation.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".