Development of In-situ Simulation Lab for Training Gynecology Residents in Basic Laparoscopic and Hysteroscopic Operative Skills
Notice bibliographique
Résumé
INTRODUCTION: Mounting evidence suggests that practice on simulators leads to improved operative skills and patient safety. With restrictions on resident work hours resulting in less exposure to procedures, simulation is the key to developing operative skills during residency and beyond. Residency programs struggle with implementing a simulation program due to timing and availability of residents. Despite having a large centralized simulation space at our institution, we identified lack of dedicated gynecologic simulation curriculum and simulator accessibility as our greatest barriers to utilizing simulation training in gynecology resident education. We sought to design a space within the resident work area dedicated to gynecologic simulation training with specific curriculum and objectives for each work station based on residency year level. METHODS: We created four workstations in a room within the Ob/Gyn clinic, in close proximity to the resident offices. Two virtual reality simulators, the LapVR (CAE, Montreal, Canada) ($84,996.00) and Simbionix Hystsim (3D Systems, (formerly Simbionix), CO, USA) ($95,741.10), were acquired from our institution's simulation center and placed in this training space to allow for enhanced resident access. The two other work stations consisted of an FLS trainer box and monitor ($1580) and another low fidelity laparoscopic box trainer and monitor ($450). Specific objectives for each station with corresponding evaluation checklists were written for each residency year level. Dedicated time to meet the written objectives was given to residents each week during their benign gynecology rotation. Supervision and assistance with task completion was provided by staff mentors assigned during those shifts. RESULTS: Residents who had this simulation lab available to them during their gynecology rotation participated in a minimum of seven hours of simulation time in addition to the time they spent on their own in the lab. These residents felt this was a meaningful increase in the amount of laparoscopic and hysteroscopic simulation exposure by having access to this in-situ GYN Simulation Training Laboratory with a defined gynecologic curriculum. Multiple staff members also took advantage of the simulation lab to practice their skills as well. CONCLUSIONS: We created an in-situ Gyn Simulation Training Lab that allowed for both improved accessibility by the residents and ease of implementation of simulation curriculum into pre-existing resident didactic time. It is our opinion that the time residents spend engaged in surgical simulation will improve surgical skills and confidence thereby enhancing patient safety. Additionally, the creation of this in situ simulation lab assists in meeting the Accreditation Council for Graduate Medical Education (ACGME) requirements for incorporation of simulation into OB/GYN resident education.
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 | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,017 | 0,004 |
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 ».