Distributed practice for cardiopulmonary resuscitation (CPR) training: improving educational efficiency and cost-effectiveness in clinical settings
Bibliographic record
Abstract
Cardiac arrest is a major health problem; high-quality cardiopulmonary resuscitation (CPR) is one of the most important determinants of survival and survival with good neurological outcomes of the victims. Despite annual training, healthcare providers struggle to conduct guideline compliant CPR during the management of cardiac arrests. Increased likelihood of survival from cardiac arrest depends upon the integration of medical science, educational efficiency and local implementation (of science and education). There is some evidence to suggest that the use of distributed practice (i.e. separating the training into small portions dispersed over time) and real-time feedback (on compression depth, rate, and recoil) can improve CPR quality in healthcare providers and medical trainees. The aim of this research is to explore the efficacy and cost-effectiveness of distributed CPR training with real-time feedback relative to current CPR training practices. To accomplish this, the following work was completed: (1) designing a randomized trial to compare a new CPR training program incorporating workplace-based distributed CPR practice and real-time feedback with a group receiving conventional Heart and Stroke Foundation of Canada (HSFC) Basic Life Support (BLS) course; (2) describing the key components of, and approaches to economic evaluation in the context of simulation-based medical education; and (3) exploring the cost-effectiveness of distributed training program relative to conventional training to inform the decision whether or not to adopt the new CPR training program. This research shows that (1) workplace-based distributed CPR training significantly improves the acquisition and retention of CPR skills in practicing acute care providers and (2) this training method results in decreased training costs and increased learning outcomes in our local context. This research provides evidence to support the educational efficiency of distributed CPR training and informs the decision on implementation of this educational strategy by addressing the cost-effectiveness. Importantly, this research is the first study that comparing distributed CPR training with conventional training and longitudinally analyzing the CPR performance to address skill retention. Furthermore, this research represents the first economic evaluation studies in resuscitation training.
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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 | 0.022 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".