Understanding the Cognitive Demands, Skills, and Assessment Approaches for Endotracheal Intubation: Cognitive Task Analysis
Notice bibliographique
Résumé
BACKGROUND: Proper airway management is an essential skill for hospital personnel and rescue services to learn, as it is a priority for the care of patients who are critically ill. It is essential that providers be properly trained and competent in performing endotracheal intubation (ETI), a widely used technique for airway management. Several metrics have been created to measure competence in the ETI procedure. However, there is still a need to improve ETI training and evaluation, including a focus on collaborative research across medical specialties, to establish greater competence-based training and assessments. Training and evaluating ETI should also incorporate modern, evidence-based procedural training methodologies. OBJECTIVE: This study aims to use the cognitive task analysis (CTA) framework to identify the cognitive demands and skills needed to proficiently perform a task, elucidate differences between novice and expert performance, and provide an understanding of the workload associated with a task. The CTA framework was applied to ETI to capture a broad view of task and training requirements from the perspective of multiple medical specialties. METHODS: A CTA interview was developed based on previous research into the tasks and evaluation methods of ETI. A total of 6 experts from across multiple medical specialties were interviewed to capture the cognitive skills required to complete this task. Interviews were coded for main themes, subthemes in each category, and differences among specialties. These findings were compiled into a skills tree to identify the training needs and cognitive requirements of each task. RESULTS: The CTA revealed that consistency in equipment setup and planning, through talk or think-aloud methods, is critical to successfully mastering ETI. These factors allow the providers to avoid errors due to patient characteristics and environmental factors. Variation among specialties derived primarily from the environment in which ETI is performed, subsequent treatment plans, and available resources. Anesthesiology typically represented the most ideal cases with a large potential for training, whereas paramedics faced the greatest number of constraints based on the environment and available equipment. CONCLUSIONS: Although the skills tree cannot perfectly capture the complexity and detail of all potential cases, it provided insight into the nuanced skills and training techniques used to prepare novices for the variability they may find in practice. Importantly, the CTA identified ways in which challenges faced by novices may be overcome and how this training can be applied to future cases. By making these implicit skills and points of variation explicit, they can be better translated into teachable details. These findings are consistent with previous studies looking at developing improved assessment metrics for ETI and expanding upon their work by delving into methods of feedback and strategies to assist novices.
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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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 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) | 0,002 | 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 ».