Does Pre‐Clerkship physician assistant POCUS training improve Knowledge and confidence in Clerkship?
Notice bibliographique
Résumé
Introduction With the technological progress of different types of portable Ultrasound machines, there is a growing demand for all health care providers to perform bedside Ultrasonography, also known as Point of Care Ultrasound (POCUS). This technique is becoming extremely useful as part of the Clinical Skills/Anatomy teaching in the undergraduate Medical field curriculum. Teaching/training health care providers how to use these portable Ultrasound machines can complement their physical examination findings and help in a more accurate diagnosis, which leads to a faster diagnosis and better patient outcomes. In addition, using portable Ultrasound machines can add more safety measurements to every therapeutic/diagnostic procedure when it is done under an Ultrasound guide. Ultrasound is one of the different imaging modalities that health care providers depend on to reach their diagnosis, while also being the least invasive method Aim To assess the effect of pre‐clerkship POCUS training on their knowledge and confidence of POCUS training during their clerkship Method The research we report in this manuscript is a preliminary qualitative study. It provides the template for future models for teaching hands on Ultrasound for all health care providers in different learning institutions. The McMaster Physician Assistant program is a two‐year course; we introduce POCUS training to the first and second year Physician Assistant curriculum. We have a total of 24 Physician Assistant students at each level of the program; at each level we divide them into three equal groups, supervised by a tutor. Each group uses one portable General Electric Ultrasound machine, which is projected onto a large plasma screen. We dim the room lights to get better quality screen images. Our session lasts for 90 minutes, the first 20 minutes being an introduction to how to use the machine and probe orientation, as well as some anatomy landmarks. Every student will have the chance to scan their peers at least one time during our session. Our objective is a pure “hands on” scanning of the neck and the abdomen performed by the students. With the correlations to their anatomy background knowledge, they were able to identify normal Thyroid Glands and major neck vessels, Liver, abdominal Aorta, inferior vena cava, Gall Bladder, and the Kidneys. Result A questionnaire was handed to the second year (clerkship) Physician Assistant students to evaluate their hands on ultrasound session experience. And the effect of their previous POCUS training at pre‐clerkship level in enhancing more confidence on their most recent training. Answers were collected and data was analyzed into multiple graphs (as illustrated in this poster). Discussion and Conclusion These results illustrate the importance of the prior POCUS training for Physician Assistant students at their pre‐clerkship level, to build up more confidence in their scanning ability, improve the orientation of their ultrasound images, and to better understand the relation of the probe’s position to the corresponding images during their clerkship POCUS training. Support or Funding Information Education program anatomy, McMaster University
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 | 0,008 | 0,048 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 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 ».