Cost-effectiveness analysis of workplace-based distributed cardiopulmonary resuscitation training versus conventional annual basic life support training
Bibliographic record
Abstract
Context: Although distributed cardiopulmonary resuscitation (CPR) practice has been shown to improve learning outcomes, little is known about the cost-effectiveness of this training strategy. This study assesses the cost-effectiveness of workplace-based distributed CPR practice with real-time feedback when compared with conventional annual CPR training. Methods: We measured educational resource use, costs, and outcomes of both conventional training and distributed training groups in a prospective-randomised trial conducted with paediatric acute care providers over 12 months. Costs were calculated and reported from the perspective of the health institution. Incremental costs and effectiveness of distributed CPR training relative to conventional training were presented. Cost-effectiveness was expressed as an incremental cost-effectiveness ratio (ICER) if appropriate. One-way sensitivity analyses and probabilistic sensitivity analysis were conducted. Results: A total of 87 of 101 enrolled participants completed the training (46/53 in intervention and 41/48 in the control). Compared with conventional training, the distributed CPR training group had a higher proportion of participants achieving CPR excellence, defined as over 90% guideline compliant for chest compression depth, rate and recoil (control: 0.146 (6/41) vs intervention 0.543 (25/46), incremental effectiveness: +0.397) with decreased costs (control: $C266.50 vs intervention $C224.88 per trainee, incremental costs: -$C41.62). The sensitivity analysis showed that when the institution does not pay for the training time, distributed CPR training results in an ICER of $C147.05 per extra excellent CPR provider. Conclusion: Workplace-based distributed CPR training with real-time feedback resulted in improved CPR quality by paediatric healthcare providers and decreased training costs, when training time is paid by the institution. If the institution does not pay for training time, implementing distributed training resulted in better CPR quality and increased costs, compared with conventional training. These findings contribute further evidence to the decision-making processes as to whether institutions/programmes should financially adopt these training programmes.
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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 | 0.015 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".