Abstract 449: Cost Analysis of a Contextualized Cardiopulmonary Resuscitation Training for Orderlies Participating in In-Hospital Cardiac Arrests
Bibliographic record
Abstract
Introduction: Basic life support (BLS) training poses challenges for healthcare organizations. It requires numerous resources, complicates scheduling and is expensive. Orderlies typically provide chest compressions during in-hospital cardiac arrests, and are therefore recertified in cardiopulmonary resuscitation (CPR) every 2 years. However, such time lapse between CPR certifications induces skills decay. Hypothesis: Based on the American Heart Association statement on resuscitation education science published in 2018, we aimed to train all orderlies (n= 900) in less than 1 year, in order to reach proficiency in chest compressions, at a fraction of the cost. Method: A shortened (20 minutes), and adapted version of CPR training was created. This training specifically addressed high quality chest compressions, the cornerstone skill relevant to the scope of practice of orderlies in CPR. It was delivered in situ during working hours, as a mass training program that spanned over a period of 1 week. The training was provided through Mastery Learning and Deliberate Practice with direct feedback and live coaching. The cost analysis aimed to compare the costs of the new approach as compared to BLS training. Results: With this innovative approach, 30 trainers were able to train 600 employees in only 5 days. Preliminary results suggest most were able to perform high quality compressions after training. The total cost of the activity was 2 347.21 $CAD which represents 3% (2 347 of 76 200) of what it would take to train 600 people in BLS (76 200$CAD), and 13% (2 347 of 18 000) of the budget planned per year for orderlies’ BLS training at our institution (~18 000$CAD). The individual cost of our initiative was 4.13$CAD per employee. This method allowed to train employees around the clock, to cover all working shifts, thereafter preventing the removal of employees from the rolling schedule. Also, this solution allows us to repeat the experience another time this year, to train all orderlies. Conclusions: Although further research is needed to assess if skills are sustained over time and comparable based on performance outcomes, our inquiry is an innovative, sustainable and cost-effective way to train specific CPR skills to large groups.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".