Qualities of Effective Vital Anaesthesia Simulation Training Facilitators Delivering Simulation-Based Education in Resource-Limited Settings
Notice bibliographique
Résumé
BACKGROUND: Lack of access to safe and affordable anesthesia and surgical care is a major contributor to avoidable death and disability across the globe. Effective education initiatives are a viable mechanism to address critical skill and process gaps in perioperative teams. Vital Anaesthesia Simulation Training (VAST) aims to overcome barriers limiting widespread application of simulation-based education (SBE) in resource-limited environments, providing immersive, low-cost, multidisciplinary SBE and simulation facilitator training. There is a dearth of knowledge regarding the factors supporting effective simulation facilitation in resource-limited environments. Frameworks evaluating simulation facilitation in high-income countries (HICs) are unlikely to fully assess the range of skills required by simulation facilitators working in resource-limited environments. This study explores the qualities of effective VAST facilitators; knowledge gained will inform the design of a framework for assessing simulation facilitators working in resource-limited contexts and promote more effective simulation faculty development. METHODS: This qualitative study used in-depth interviews to explore VAST facilitators' perspectives on attributes and practices of effective simulation in resource-limited settings. Twenty VAST facilitators were purposively sampled and consented to be interviewed. They represented 6 low- and middle-income countries (LMICs) and 3 HICs. Interviews were conducted using a semistructured interview guide. Data analysis involved open coding to inductively identify themes using labels taken from the words of study participants and those from the relevant literature. RESULTS: Emergent themes centered on 4 categories: Persona, Principles, Performance and Progression. Effective VAST facilitators embody a set of traits, style, and personal attributes (Persona) and adhere to certain Principles to optimize the simulation environment, maximize learning, and enable effective VAST Course delivery. Performance describes specific practices that well-trained facilitators demonstrate while delivering VAST courses. Finally, to advance toward competency, facilitators must seek opportunities for skill Progression.Interwoven across categories was the finding that effective VAST facilitators must be cognizant of how context, culture, and language may impact delivery of SBE. The complexity of VAST Course delivery requires that facilitators have a sensitive approach and be flexible, adaptable, and open-minded. To progress toward competency, facilitators must be open to self-reflection, be mentored, and have opportunities for practice. CONCLUSIONS: The results from this study will help to develop a simulation facilitator evaluation tool that incorporates cultural sensitivity, flexibility, and a participant-focused educational model, with broad relevance across varied resource-limited environments.
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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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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.
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