Self-motivated learning with gamification improves infant CPR performance, a randomised controlled trial
Bibliographic record
Abstract
Background: Effective paediatric basic life support improves survival and outcomes. Current cardiopulmonary resuscitation (CPR) training involves 4-yearly courses plus annual updates. Skills degrade by 3-6 months. No method has been described to motivate frequent and persistent CPR practice. To achieve this, we explored the use of competition and a leaderboard, as a gamification technique, on a CPR training feedback device, to increase CPR usage and performance. Objective: To assess whether self-motivated CPR training with integrated CPR feedback improves quality of infant CPR over time, in comparison to no refresher CPR training. Design: Randomised controlled trial (RCT) to assess the effect of self-motivated manikin-based learning on infant CPR skills over time. Setting: A UK tertiary children's hospital. Participants: 171 healthcare professionals randomly assigned to self-motivated CPR training (n=90) or no refresher CPR training (n=81) and followed for 26 weeks. Intervention: The intervention comprised 24 h a day access to a CPR training feedback device and anonymous leaderboard. The CPR training feedback device calculated a compression score based on rate, depth, hand position and release and a ventilation score derived from rate and volume. Main outcome measure: The outcome measure was Infant CPR technical skill performance score as defined by the mean of the cardiac compressions and ventilations scores, provided by the CPR training feedback device software. The primary analysis considered change in score from baseline to 6 months. Results: Overall, the control group showed little change in their scores (median 0, IQR -7.00-5.00) from baseline to 6 months, while the intervention group had a slight median increase of 0.50, IQR 0.00-33.50. The two groups were highly significantly different in their changes (p<0.001). Conclusions: A significant effect on CPR performance was demonstrated by access to self-motivated refresher CPR training, a competitive leaderboard and a CPR training feedback device.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".