MedElevator Войти в исследования
← Поиск публикаций

DOI 10.1016/j.ajog.2023.07.043

Медицинская библиотека · American journal of obstetrics and gynecology · 2023

Enhancing colposcopy training using a widely accessible digital education tool in China.

Chen Mingyang, Xue Peng, Li Qing, Shen Yu, Ye Zichen, Wang Huike, Cui Xiaoli, Zhao Tiantian, Li Gaoqin, Seery Samuel, Wang Yanzhu, Lin Qiufen, Zhang Wenhua, Zhang Xun, Jiang Yu, Qiao Youlin

СтатьяПоддержка исследования: вне правительства СШАПеревод в очереди

Аннотация

BACKGROUND

Colposcopy is a cornerstone of cervical cancer prevention; however, there is a global shortage of colposcopists. It is challenging to train a sufficient number of colposcopists through in-person methods, which hinders our ability to adequately diagnose and manage positive cases. A digital platform is needed to make colposcopy training more efficient, scalable, and sustainable; however, current online training programs are generally based on didactic curricula that do not incorporate image analysis training. In addition, long-term assessments of online training are not readily available. Therefore, innovative digital training and an assessment of its effectiveness are needed.

OBJECTIVE

This study aimed to evaluate the short- and long-term effects of DECO (an online Digital Education Tool for Colposcopy) on trainees' colposcopy competencies and confidence.

STUDY

DESIGN

DECO can be used both on laptops and smartphones and comprises 4 training modules (image interpretation; terminology learning; video teaching; and collection of guidelines and typical cases) and 2 test modules. DECO was tested through a pre-post study between September and November 2022. Participants were recruited in China, and DECO training lasted 12 days. Trainees initially learned basic theory before completing training using 200 image-based cases. Pretest, posttest, and follow-up testing included 20 distinct image-based questions, and was conducted on Days 0, 13, and 60. Primary outcomes were competence and confidence scores. Secondary measures were response distributions for colposcopic diagnoses, biopsies, and DECO training satisfaction. Multilevel modeling was used to determine improvement from baseline to posttraining and follow-up for the outcomes of interest.

RESULTS

Among 402 participants recruited, 96.8% (n=389) completed pretesting, 84.1% (n=338) posttesting, and 75.1% (n=302) follow-up testing. Colposcopic competence and confidence increased across this study. Diagnostic scores improved on average from 55.3 (53.7-56.9) to 70.4 (68.9-71.9). The diagnostic accuracy for normal/benign lesions, low-grade squamous intraepithelial lesions, and high-grade squamous intraepithelial lesions or worse increased by 16.9%, 13.1%, and 16.9%, respectively. Mean confidence scores increased from 48.1 (45.6-50.6) to 56.2 (54.5-57.9). These improvements remained evident 2 months after training. Trainees were also satisfied with DECO overall. Most found DECO to be scientific (82.5%), easy to use (75.2%), and clinically useful (98.4%), and would recommend it to colleagues (93.2%).

CONCLUSION

DECO is a useful, acceptable digital education tool that improves colposcopy competencies and confidence. DECO could make colposcopy training more efficient, scalable, and sustainable because there are no geographic or time limitations. Therefore, DECO could be used to alleviate the shortage of trained colposcopists around the world.

Для цитирования: Chen Mingyang, Xue Peng, Li Qing, Shen Yu, Ye Zichen, Wang Huike, Cui Xiaoli, Zhao Tiantian, Li Gaoqin, Seery Samuel, Wang Yanzhu, Lin Qiufen, Zhang Wenhua, Zhang Xun, Jiang Yu, Qiao Youlin. Enhancing colposcopy training using a widely accessible digital education tool in China.. American journal of obstetrics and gynecology. 2023. DOI: 10.1016/j.ajog.2023.07.043.

Предметные рубрики MeSH

Подготовка и перевод публикации
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…

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.