在个性化心理健康护理中,统计途径走向道德大数据
Dominikus David Biondi Situmorang1, Rina Nurhudi Ramdhani1, Pepi Nuroniah1
1Department of Guidance and Counseling, Faculty of Education, Universitas Pendidikan Indonesia, Bandung, Indonesia.
Journal of psychiatric and mental health nursing
|November 8, 2025
概括
将道德统计方法纳入心理健康护理教育是负责任的大数据使用的关键. 这种方法确保数据驱动的创新促进公平,人性化的患者护理.
科学领域:
- 心理健康 数据科学 数据科学
- 应用统计学应用统计学
- 医疗信息学 医疗信息学
背景情况:
- 在个性化心理健康护理中,对大数据的伦理考虑越来越重要.
- 在心理健康数据使用的伦理义务和可衡量的方法之间存在差距.
- 统计素养是开发数据驱动创新的关键,以实现公平的心理健康护理.
研究的目的:
- 概述在心理健康实践中实施大数据的伦理统计策略.
- 展示健全的统计方法如何与P4医学 (预测,预防,个性化,参与) 的原则保持一致.
主要方法:
- 为辅导员提出受应用统计学概念影响的统计指导.
- 建议基于回归的预测建模方法用于透明的风险因素分析.
- 建议进行多变量,多级,非参数/强大的统计分析,以实现包容性数据处理.
主要成果:
- 在心理健康数据科学中,可以同时实现道德和方法严谨.
- 拟议的统计方法提高了预测建模的透明度和公平性.
- 多变量和非参数方法支持个人和系统层面的包容性数据分析.
结论:
- 将伦理统计教育纳入护理和咨询培训可以促进数据素养.
- 系统层面的干预,如道德大数据指南,是验证预测技术所需的.
- 在心理健康领域,负责任的大数据实践需要道德警和方法论严谨.
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