开发和验证加拿大加快在线数据科学培训,为健康科学社区提供数据科学培训
Reda Goudrar1, Maxine Joly-Chevrier1, Lise De Cloedt2
1Faculty of Medicine, Université de Montréal, Montreal, Quebec, Canada.
Digital health
|May 19, 2025
概括
这项研究为医疗保健学员开发了一门可访问的R编程课程,大大提高了他们的数据科学技能. 培训提高了临床研究能力,甚至在完成后三个月.
科学领域:
- 数字健康数字健康
- 数据科学教育数据科学教育
- 临床研究 临床研究
背景情况:
- 数字健康需要数据科学能力,但大学课程往往缺乏全面的培训.
- 可访问的平台对于弥合医疗保健专业人员在数据分析和编程教育方面的差距至关重要.
研究的目的:
- 开发和评估一个免费的,加速的R编程课程,面向没有编程经验的医疗保健学员.
- 评估R编程课程对参与者临床研究数据科学技能的影响.
主要方法:
- 一个为期10天的七个视频囊R编程课程被交付给医疗保健学员.
- 测试前后测试设计评估了培训后立即参加者的技能,并在三个月后进行后续跟踪.
- 来自蒙特利尔学术界的100名医疗保健学员参加了培训;使用混合效应模型评估技能.
主要成果:
- 84%的参与者在10天的时间内完成了课程.
- 平均测试分数显著增加,从4.5/10预测到8.4/10后训练 (p < .001).
- 技能在三个月 (6.8/10) 与基线相比仍然显著提高 (p < .001).
结论:
- 加快的R编程课程有效地提高了医疗保健学员的数据科学技能.
- 这项培训解决了正式数据科学教育中的关键差距.
- 该课程有可能提高医疗保健专业人员的独立研究和数据分析能力.
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