Using the Human Patient Simulator to Educate Students of Veterinary Medicine
Notice bibliographique
Résumé
INTRODUCTION: The human patient simulator has proved to be an effective educational device for teaching physicians and paramedical personnel. METHODOLOGY: To determine whether veterinary medicine students would benefit from similar educational sessions, 90 students each took a turn being the patient's clinician as real-life scenarios were played out on the simulator. The students induced and maintained anesthesia on their patient and monitored vital signs. Several critical events were presented for the students to diagnose and treat as they occurred. All students submitted a written evaluation of the course upon completion. The last 40 students were randomly divided into two groups of 20 students each. The students in Group I experienced the simulator before their clerkship examination, and those in Group II took the examination before their simulator experience. RESULTS: The students rapidly gained confidence in treating their simulated patient. This carried over to the clinical setting, where they appeared to be more confident when anesthetizing live patients. The simulator experience brought together much of the previous didactic material that they had been exposed to so they could appreciate its clinical relevance. The overwhelming response to the simulator experience was positive. The students in Group I had a significantly higher score on the clerkship examination dealing with concepts reviewed by simulation than those in Group II, who engaged in self-study instead of the simulation exercise (p < 0.001). CONCLUSION: We conclude that the human patient simulator was a valuable learning tool for students of veterinary medicine. It was exciting for the students to work with, made them deal with "real-life" scenarios, permitted them to learn without subjecting live patients to complications, enabled them to retrace their steps when their therapy did not correct the simulated patient's problems, and facilitated correlation of their basic science knowledge with clinical data, thus accelerating their ability to handle complex clinical problems in healthy and diseased patients.
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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 | 0,002 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».