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定性研究和定量数据的密集三角测量提高随机试验招募效果:QuinteT 方法。

Intensive Triangulation of Qualitative Research and Quantitative Data to Improve Recruitment to Randomized Trials: The QuinteT Approach.

机构信息

1 University of Bristol, Bristol, United Kingdom.

出版信息

Qual Health Res. 2019 Apr;29(5):672-679. doi: 10.1177/1049732319828693. Epub 2019 Feb 22.

Abstract

Randomized controlled trials (RCTs) can provide high quality evidence about the comparative effectiveness of health care interventions, but many RCTs struggle with or fail to complete recruitment. RCTs are built on the principles of the experimental method, but their planning, conduct, and interpretation can depend on complex social, behavioral, and cultural factors that may be best understood through qualitative research. Most qualitative studies undertaken alongside RCTs involve interviews that produce data that are used in a supportive or supplicatory role, but there is potential for qualitative research to be more influential. In this article, we describe the research methods underpinning the "QuinteT" (Qualitative Research Integrated Within Trials) approach to understand and address RCT recruitment difficulties. The QuinteT Recruitment Intervention (QRI) brings together multiple qualitative strategies and quantitative data and uses triangulation to understand recruitment issues rapidly. These nuanced understandings are used to inform the implementation of collaborative actions to improve recruitment.

摘要

随机对照试验(RCTs)可以提供关于医疗干预措施相对有效性的高质量证据,但许多 RCT 都在努力或未能完成招募。RCT 建立在实验方法的原则之上,但它们的规划、实施和解释可能取决于复杂的社会、行为和文化因素,这些因素最好通过定性研究来理解。大多数与 RCT 同时进行的定性研究都涉及访谈,这些访谈产生的数据在支持或辅助作用中使用,但定性研究有可能更具影响力。在本文中,我们描述了“QuinteT”(定性研究融入试验)方法的研究方法,以了解和解决 RCT 招募困难的问题。QuinteT 招募干预措施(QRI)汇集了多种定性策略和定量数据,并使用三角测量法快速了解招募问题。这些细微的理解被用来为改进招募的协作行动提供信息。

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