Teaching high quality paediatric basic life support to laypeople: The development and evaluation of a virtual simulation game
Bibliographic record
Abstract
Background: Self-directed training has been recognized as a reasonable alternative to traditional instructor-led formats to teach laypeople Basic Life Support (BLS). Virtual tools can facilitate high-quality self-directed resuscitation education; however, their role in teaching paediatric BLS remains unclear due to limited empiric evaluation and suboptimal design of existing tools. Aim: We describe the development and evaluation of a virtual simulation game (VSG) designed to teach high-quality paediatric BLS using a self-directed, online format with integrated deliberate practice and feedback. Methods: We conducted a pilot prospective single-arm cohort study examining the VSG's impact on laypeople's paediatric BLS self-efficacy, attitudes, and knowledge as well as learner reactions. Data was collected using online surveys immediately after VSG completion and was analysed using descriptive statistics. Results: Fifty-five participants (median age 32 years, 76% female, 11% active certification in paediatric BLS) evaluated the VSG. Participants reported high self-efficacy, willingness to perform paediatric BLS, and high perceived knowledge after VSG completion. Fifty (91%) achieved a passing score (≥13/15) on the paediatric BLS knowledge assessment. Learner reactions were favourable with 98% of participants agreeing that VSG educational content was clear and helpful. Mean System Usability Scale score was 81.1 (standard deviation 12.6) with a Net Promoter Score of 32 indicating high levels of usability and likelihood to recommend to others. Conclusions: The VSG was well-received by laypeople with positive effects observed on paediatric BLS self-efficacy, attitudes, and knowledge. Future studies should examine the impact of VSGs on skill performance through standalone or blended learning approaches.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".