描述医疗专业和告知有抱负的医生:一个数据驱动的方法
Michael Balas1, Rachelle M Scheepers2, Zsolt Zador3
1Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
Advances in health sciences education : theory and practice
|September 12, 2023
概括
这项研究聚合了医疗专业,使用医生数据来揭示不同的实践概况. 这些发现有助于有抱负的医生通过突出实践环境的相似性来做出明智的职业选择.
科学领域:
- 医学教育 医学教育
- 医疗保健服务研究 医疗服务研究
- 数据科学在医学中的数据科学
背景情况:
- 对医学学生来说,明智的职业决策至关重要.
- 了解医疗领域之间的相互关系有助于选择专业.
- 数据驱动的方法可以客观地绘制医疗专业领域的地图.
研究的目的:
- 根据工作环境的相似性,将医疗专业聚集在一起.
- 为医学领域之间的相互关系提供一个不偏见的视角.
- 为了指导医学学生在他们的专业选择.
主要方法:
- 来自加拿大健康信息数据库 (2018-2019) 的合并数据.
- 在 25 个医学专业和 36 个变量上使用了隐形形状分析 (LPA).
- 采用克鲁斯卡尔-瓦利斯测试来确定显著的差异化变量.
主要成果:
- 确定了七个不同的医学专业集群 (个人资料).
- 在个人资料中发现了收入,工作环境和支付风格的显著差异.
- 人口统计数据,职业满意度和工作与生活平衡满意度在个人资料之间没有显著差异.
结论:
- 隐性概况分析成功将25个医学专业分组成有意义的概况.
- 这些概况反映了共同的主题和指标变量 (例如,程序与临床).
- 已识别的个人资料为有抱负的医生选择医学专业提供了宝贵的指导.
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