Using the Evidence-Development-Validation-Consensus (EDVC) Approach to Develop and Validate maxSIMdrone: A Training Program for Healthcare Professionals to Provide Cardiac Arrest Care Using Drones
Bibliographic record
Abstract
Introduction The challenges of delivering cardiac arrest (CA) courses in rural and remote (R&R) locations worldwide have been further exacerbated by the COVID-19 pandemic. However, it is important to note that this problem has always existed. The implementation of social distancing measures to combat the pandemic has had a significant impact on healthcare and medical education, particularly in relation to the training of students, laypeople (LP), and healthcare professionals (HCPs) in CA care. The combination of pandemic restrictions and pre-existing difficulties faced in R&R locations and large cities has disrupted the provision of comprehensive medical education. The suspension of basic life support and defibrillation (BLSD) courses during the pandemic may have negatively affected pre-hospital care for CA. However, it is essential to acknowledge that challenges in delivering these courses in R&R areas predate the pandemic. Materials and methods A 2021 epidemiological study in the Brazilian Amazon identified CA as the primary cause of death, followed by COVID-19. This highlights the importance of providing BLSD courses and training to emergency medical service (EMS) personnel in R&R locations. Even during a pandemic. Researchers from Ontario Tech University and Memorial University School of Medicine developed a drone with a simulation scenario to train HCPs in automated external defibrillators (AED) operation and guide LP in safe use through BLSD protocols. A literature review showed that different training methods yielded similar outcomes. Based on these findings, the evidence-development-validation-consensus (EDVC) hybrid approach was used to develop and validate an online training program using a learning management system (LMS) as a model. Results Teaching HCPs and LP in R&R locations, such as northern Canada and the Brazilian Amazon, presents challenges due to limited resources and internet access. One potential solution lies in the utilization of remote online LMS that facilitate the administration, documentation, tracking, reporting, automation, and delivery of educational courses and training programs. The literature review indicated that mixed training approaches, including face-to-face, online, and hybrid formats, produced similar outcomes in learning assessment, self-confidence, performance, skills, and knowledge acquisition. These findings support the viability of using LMS as a model to develop and validate a course where drones deliver AEDs and provide training to HCPs and LP in R&R locations. A comprehensive training program should encompass cognitive, affective, and psychomotor learning domains, addressing various skills and knowledge aspects. Conclusion This research study develops and validates LMS teaching methods to support a training program for HCPs and LP in using AEDs delivered by drones. The program combines design-based research and consensus development methods, such as design thinking and think-aloud observations. Drones are used to provide AEDs and develop simulation scenarios for training in R&R locations. The hybrid approach ensures a valid and evidence-based training program. The study presents the EDVC approach used to enhance the maxSIMdrone training program, enabling effective out-of-hospital CA care. The program incorporates participant feedback and improves knowledge and techniques in AED use. It has the potential to improve patient outcomes in resource-limited R&R locations.
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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 | 0.394 | 0.481 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".