在定性研究中以现象为导向的生活世界为主导的数据生成 - - 挑战方法的缺失或不充分的选择
Helena Dahlberg1, Christopher Holmberg1, Karin Dahlberg1
1Institute of Health and Care Sciences, University of Gothenburg, Gothenburg, Sweden.
概括
高质量的数据生成对于有效的定性研究至关重要,特别是在医疗保健领域. 专注于现象和参与者的敏感性确保了超越标准化方法的有意义的见解.
科学领域:
- 定性研究方法论 定性研究方法论
- 科学哲学的科学哲学
- 卫生研究伦理 卫生研究伦理
背景情况:
- 定性研究在很大程度上依赖于生成数据的质量.
- 标准化方法可能无法捕捉复杂现象的细微差别.
- 伦理考虑是最重要的,特别是在医疗保健机构.
研究的目的:
- 强调在定性研究中高质量的数据生成的重要性.
- 倡导那些阐明重大现象的方法.
- 引导有效的定性研究实践的发展.
主要方法:
- 借鉴科学哲学作为认识论论的理由.
- 利用在医疗保健领域的定性研究方面丰富的经验.
- 倡导以现象为导向和以生活世界为导向的方法.
主要成果:
- 适当的数据生成方法与正在研究的现象保持一致.
- 对现象特征的彻底探索对于有机方法的选择至关重要.
- 所有的方法都有局限性;上下文决定了合适性.
结论:
- 方法决策不应该基于方便,而是基于对参与者和研究背景的敏感性.
- 整合人权考虑对道德定性健康研究至关重要.
- 一种以生命世界为导向,以现象为导向的方法产生了洞察力和道德上的合理结果.
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