DOI 10.3171/2023.10.spine23273
Innovative growth and development of a neurological surgery residency cadaveric spine-simulation training program: a single-institution experience.
Аннотация
OBJECTIVE
Cadaveric and dry 3D model-based simulation training is a valuable educational tool for neurosurgical residents. Such simulation training is an opportunity for residents to hone technical skills and decision-making and enhance their neuroanatomy knowledge. The authors describe the growth and development of the Oregon Health & Science University Department of Neurological Surgery resident-focused, hands-on, spine-simulation surgery courses and provide details of course evaluations, layout, and setup.
METHODS
A four-part spine surgical simulation series, including two human cadaveric and two dry 3D model-based courses, was created to provide resident spine procedure training. Residents participated in the spine simulation series (2017-2021) and completed annual course curriculum and anonymous post-course evaluations. Evaluations included both Likert scale items and free-text responses. Responses to Likert scale items were analyzed in Python. Free-text responses were quantified using the Valence Aware Dictionary for Sentiment Reasoner. Descriptive statistics were calculated and plotted using Python's seaborn and matplotlib library modules.
RESULTS
The analysis included 129 spine (occipitocervical, thoracolumbar, and spine model fusion I and II) simulation course evaluations. Likert responses demonstrated high average responses for evaluation questions (4.67 ± 0.90 and above). The average compound sentiment value was 0.58 ± 0.28.
CONCLUSIONS
This is the first time Likert responses and sentiment analysis have been used to demonstrate how neurosurgical residents positively value a hands-on spine simulation training. Simulation is an essential component of neurosurgical resident education training. The authors encourage other neurosurgical education programs to develop and leverage spine simulation as a teaching tool.
Предметные рубрики MeSH
Подготовка и перевод публикации
Обработка статьи · этапы, ошибки и управление
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Тип публикации указан по данным источника. Уровень доказательности автоматически не присваивается.