快速定性分析方法及其在实施科学中的应用

Rapid Qualitative Analysis Methods and Their Application in Implementation Science

  • 摘要: 实施科学(implementation science,IS)旨在系统分析和解决从证据到实践的现实差距与情境的影响因素,需开展定性研究采集相关结局,但传统定性分析存在耗费时间和精力等问题,无法迅速提供实施科学研究所需的关键资料。快速定性分析方法(rapid qualitative analysis,RQA)通过半结构化访谈,采用即时资料浓缩与矩阵分析等技术,可有效缩短定性资料收集与数据处理周期。RQA通过快速识别结构性障碍、促进因素和目标群体行为特征等健康社会决定因素,为公共卫生决策、解释复杂社会现象及研究项目的过程和效果评估提供实时依据。尽管RQA难以基于扎根理论等开展深入的理论分析,但高效性、灵活性使其成为大规模、时效性研究的优选工具,因此在实施科学的研究中广泛应用。本文梳理了RQA的核心概念和常用技术手段,以及其与传统定性分析的差异,探讨RQA在干预优化、过程评估及实施结局评估中的应用,并结合具体案例阐明其在实施科学领域的应用价值。未来可探索RQA与人工智能及大数据等技术的融合,弥合科研成果转化为实践的差距,在资源有限或时间紧迫的情境下高效开展实施科学研究,为加速循证实践提供便捷和科学的方法学技术支持。

     

    Abstract: Implementation science(IS) aims to systematically analyze and address the real-world gaps from evidence to practice and the influencing factors of the context. It is necessary to carry out qualitative research to gather relevant implementation outcomes. Nevertheless, traditional qualitative analysis has issues such as consuming a great deal of time and energy, and it is unable to promptly provide the crucial data required for implementation science research. The Rapid Qualitative Analysis (RQA) method, through semi-structured interviews and the adoption of techniques such as immediate data condensation and matrix analysis, can effectively shorten the cycle of qualitative data collection and data processing. RQA can promptly identify social determinants of health such as structural barriers, facilitators, and the behavioral characteristics of target groups. Itprovides a real-time basis for public health decision-making, the interpretation of complex social phenomena, and the process and effectiveness evaluation of research projects. Although RQA is difficult to conduct in-depth theoretical analysis based on grounded theory, its efficiency and flexibility make it the preferred tool for large-scale and time-sensitive research. Thus, it has been widely applied in implementation science research. This paper sorts out the core concepts and commonly used technical methods of RQA, as well as the differences between RQA and traditional qualitative analysis. It also explores the applications of RQA in intervention optimization, process evaluation, and implementation outcome evaluation. By integrating specific cases, this paper clarifies its application value in the field of implementation science. In the future, it is advisable to explore the integration of RQA with technologies such as artificial intelligence and big data, in order to bridge the gap between the transformation of scientific research achievements into practice. Under circumstances of limited resources or tight time constraints, RQA can be used to efficiently conduct implementation science research, providing convenient and scientific methodological and technical support for accelerating evidence-based practice.

     

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