Simulation-Based Training and Its Use Amongst Practicing Paramedics and Emergency Medical Technicians: An Evidence-Based Systematic Review
Notice bibliographique
Résumé
Objectives: This systematic review (SR) describes how simulation-based training (SBT) is utilized by paramedics and emergency medical technicians (EMTs). Data sources: PubMed, CINAHL, Cochrane CENTRAL, Scopus, Web of Science, and Google Scholar were searched from 2010 to 2021. Review methods: Standard SR methodology was utilized according to PRISMA guidelines. Eligibility criteria included English studies conducted in the United States or Canada published and published between 2010 and 2021. Study designs were somewhat heterogeneous and included quantitative, qualitative, and mixed-methods projects. The specific populations included paramedics and EMTs. Results: 595 articles were initially identified and reviewed, 25 of which met our inclusion criteria. Of them, the most common SBT areas of focus documented in the literature was general assessment and treatment (7 studies) and airway management (7 studies). The majority of the studies were conducted in a mobile simulation lab (6 studies), simulation centers (5 studies), and ambulances (5 studies). Many of the studies report simulations involving using manikins alone and a combination of manikins and simulated patients. Overall, 21 studies documented the use of high-fidelity simulation. 16 studies involved paramedics only, 8 involved both paramedics and EMTs, and one study involved only EMTs. Most of the impact of SBT appeared to be on objective measures such as performance, procedural success, and ability to identify errors, as well as subjective metrics such as perceived improvement in knowledge and skill. The degree of sustained impact of SBT on skill retention was not frequently reported, and direct enhancement in patient outcomes such as length-of-stay, or mortality were not documented in any of the studies. Conclusions: Paramedics and EMTs provide critically important, often lifesaving, prehospital care. However, the opportunities to enhance their skills are limited by several factors; most notably their undergraduate and certificate educational requirements, which are much ... (truncated)
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,030 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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 tête enseignante, 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 ».