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
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,394 | 0,481 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,004 | 0,008 |
| Bibliométrie | 0,010 | 0,006 |
| Études des sciences et des technologies | 0,004 | 0,003 |
| Communication savante | 0,006 | 0,005 |
| Science ouverte | 0,008 | 0,011 |
| Intégrité de la recherche | 0,004 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».