一个计算验证健康概念成熟度水平问卷问卷
Arthur Trognon1,2,3, Islem Habibi2, Hamza Altakroury2
1Association Innov'Autonomie, Ecole Des Mines, Nancy, France.
Frontiers in psychology
|February 11, 2026
概括
这项研究引入了一种新的计算方法来验证健康概念的成熟度,使用机器学习来分析专家演讲和问卷. 该方法对评估健康创新有希望,但需要对某些因素进行改进.
科学领域:
- 医疗保健创新 医疗保健创新
- 医疗信息学 医疗信息学
- 心理测量 心理测量 心理测量
背景情况:
- 医疗保健市场需要以用户为中心的设计集成.
- 生活实验室促进共同创建和评估健康创新.
- 评估健康概念的成熟度对于产品和服务的开发至关重要.
研究的目的:
- 开发和验证一个健康概念成熟度水平 (CMLH) 问卷.
- 用机器学习引入健康概念的计算语义有效性.
- 为了评估CMLH问卷的心理特征.
主要方法:
- 开发了由178项组成的CMLH问卷.
- 有注释的专家演讲行为与健康概念成熟度水平有关.
- 应用CatBoost和神经网络用于计算语义验证.
- 从文本中识别CMLH因素的评估模型性能.
主要成果:
- 用真实因素训练的模型在识别CMLH标准方面表现优于使用随机因素的模型.
- 机器学习模型展示了从语音行为中辨别因素的能力.
- 该CMLH问卷显示了证据的收和内容有效性.
- 确定了"计划"和"需求"因素之间的重叠.
结论:
- 计算语义有效性为健康创新中的心理测量验证提供了一种新的方法.
- CMLH问卷是评估健康概念成熟度的验证工具.
- 需要进一步改进CMLH模型,以解决因子重叠的问题.
- 计算方法显示了验证其他心理测量工具的潜力.
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