Cadaveric‐based Airway Management Instructional Videos to Supplement Traditionally Taught Patient Care Skills for Emergency Healthcare Providers
Notice bibliographique
Résumé
Effective administration of emergency medical care relies on the knowledge and skills of highly trained healthcare practitioners. As the scope of emergency medical care practice expands in the pre‐hospital and hospital environments, approaches to emergency healthcare education must continue to evolve; comprehensive understanding of human anatomy becomes more important than ever. The University of Guelph (Human Anatomy Program) and Fanshawe College (Schools of Health Science and Public Safety) are collaborating to develop a cadaveric‐based educational resource to facilitate the teaching of techniques and clinical skills associated with intubation procedures: endotracheal tube with stylet (ET‐S), endotracheal tube with bougie introducer (ET‐B), and intubation through a laryngeal mask airway (ET‐LMA). When presented in conjunction with, or following, ‘traditional’ anatomy and patient care procedures, the overall intent is to encourage critical thought relative to health science theory and current professional practice. We tested the efficacy of cadaveric‐based‐digital modules focused on the anatomy and skills associated with intubation procedures on first‐year students enrolled in the Respiratory Therapy Program at Fanshawe College. Participants entered the study with traditional ‘classroom knowledge’ of ET‐S and ET‐B; participants had no prior knowledge of ET‐LMA. Participants' knowledge of relevant anatomy was assessed pre‐ and post‐intervention. For procedures, participants learned and practiced using low fidelity task trainers, with either traditional teaching material (i.e., control group) or audiovisual modules (i.e., experimental group). Following ET procedures, participants' competencies were tested. Pre‐intervention assessment of anatomical knowledge did not differ (0.77 ± 7.73%, p = 0.921), whereas post‐intervention assessment of anatomical knowledge differed between the two groups (24.21 ± 7.73%, p = 0.004). Specifically, when test scores for anatomical knowledge assessment were compared pre‐ and post‐intervention, the experimental group improved their scores by 26.59 ± 6.14% ( p = 0.001); the control did not improve post‐intervention (1.60 ± 3.16%, p = 0.621) (Figure 1). When combined with classroom‐based instruction, cadaveric‐based training enhanced students' intubation skills for ET‐S and ET‐B procedures (ET‐S: 17.86 ± 5.65%, p = 0.004; ET‐B: 12.86 ± 5.83%, p = 0.036). Interestingly, post‐intervention intubation skills did differ between experimental and control groups for the procedure in which students had no prior knowledge, the ET‐LMA procedure (ET‐LMA: 1.96 ± 5.53%, p = 0.76) (Figure 2). Student feedback suggested that cadaveric‐based learning improved their understanding and capacity to visualize anatomy, promoting an enhanced understanding of procedures. Procedural techniques were enhanced only when a cadaveric‐based module was associated with classroom‐based instruction, suggesting that the most beneficial use of this tool is to supplement traditional instructional practices. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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 | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,032 | 0,002 |
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 ».