Department of Family Medicine, University of Colorado, Aurora, CO, 80045, USA.
Adult and Child Center for Outcomes Research and Delivery Science (ACCORDS), University of Colorado, Aurora, CO, USA.
Implement Sci. 2022 Jul 29;17(1):51. doi: 10.1186/s13012-022-01218-3.
Interventions are often adapted; some adaptations may provoke more favorable outcomes, whereas some may not. A better understanding of the adaptations and their intended goals may elucidate which adaptations produce better outcomes. Improved methods are needed to better capture and characterize the impact of intervention adaptations.
We used multiple data collection and analytic methods to characterize adaptations made by practices participating in a hybrid effectiveness-implementation study of a complex, multicomponent diabetes intervention. Data collection methods to identify adaptations included interviews, observations, and facilitator sessions resulting in transcripts, templated notes, and field notes. Adaptations gleaned from these sources were reduced and combined; then, their components were cataloged according to the framework for reporting adaptations and modifications to evidence-based interventions (FRAME). Analytic methods to characterize adaptations included a co-occurrence table, statistically based k-means clustering, and a taxonomic analysis.
We found that (1) different data collection methods elicited more overall adaptations, (2) multiple data collection methods provided understanding of the components of and reasons for adaptation, and (3) analytic methods revealed ways that adaptation components cluster together in unique patterns producing adaptation "types." These types may be useful for understanding how the "who, what, how, and why" of adaptations may fit together and for analyzing with outcome data to determine if the adaptations produce more favorable outcomes rather than by adaptation components individually.
Adaptations were prevalent and discoverable through different methods. Enhancing methods to describe adaptations may better illuminate what works in providing improved intervention fit within context.
This trial is registered on clinicaltrials.gov under Trial number NCT03590041 , posted July 18, 2018.
干预措施通常需要进行调整；一些调整可能会带来更有利的结果，而另一些则可能不会。更好地了解调整及其预期目标，可以阐明哪些调整会产生更好的结果。需要改进方法以更好地捕捉和描述干预措施调整的影响。
我们使用多种数据收集和分析方法来描述参与一项复杂的、多成分糖尿病干预措施的混合有效性实施研究的实践所进行的调整。用于识别调整的方法包括访谈、观察和促进者会议，这些方法产生了转录本、模板化笔记和现场笔记。从这些来源中收集到的调整被简化和组合；然后，根据报告证据基础干预措施（FRAME）的调整和修改的框架对其组成部分进行分类。用于描述调整的分析方法包括共现表、基于统计的 k-均值聚类和分类分析。
我们发现，（1）不同的数据收集方法会引出更多的总体调整，（2）多种数据收集方法有助于理解调整的组成部分和原因，（3）分析方法揭示了调整组成部分以独特模式聚类在一起的方式，从而产生调整“类型”。这些类型可能有助于理解调整的“谁、什么、如何和为什么”如何组合在一起，并用于分析与结果数据，以确定调整是否会产生更有利的结果，而不是单独根据调整组成部分。
通过不同的方法可以发现调整的普遍性和可发现性。改进描述调整的方法可能会更好地阐明在提供改进的干预措施适应背景方面的作用。
该试验在 clinicaltrials.gov 上注册，试验编号为 NCT03590041，于 2018 年 7 月 18 日发布。
