Kristina M. Cordasco MD, MPH, MSHS, Sonya E. Gabrielian MD, MPH, Jenny Barnard BA, Taylor Harris PhD, MA, Erin P. Finley PhD, MPH
{"title":"在推广复杂的循证实践的同时修改一揽子实施方案的结构化方法。","authors":"Kristina M. Cordasco MD, MPH, MSHS, Sonya E. Gabrielian MD, MPH, Jenny Barnard BA, Taylor Harris PhD, MA, Erin P. Finley PhD, MPH","doi":"10.1111/1475-6773.14313","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Objective</h3>\n \n <p>To describe <i>a</i> structured, iterative, data-driven approach for modifying implementation strategies for a complex evidence-based practice during a nationwide scale-up initiative.</p>\n </section>\n \n <section>\n \n <h3> Data Sources and Study Setting</h3>\n \n <p>We scaled-up implementation of Critical Time Intervention (CTI)—an evidence-based case management model—across 32 diverse community-based Veterans Affairs (VA) “Grant and Per Diem” case management (GPD-CM) agencies that serve homeless-experienced Veterans transitioning to independent living. Primary data were collected using qualitative methods.</p>\n </section>\n \n <section>\n \n <h3> Study Design</h3>\n \n <p>We embarked on a scale-up initiative while conducting a pragmatic randomized evaluation using a roll-out design, comparing two versions of a CTI implementation package tailored to VA's GPD-CM program. We iteratively assessed contextual factors and implementation outcomes (e.g., acceptability); findings informed package modifications that were characterized using the Framework for Reporting Adaptations and Modifications to Evidence-based Implementation Strategies.</p>\n </section>\n \n <section>\n \n <h3> Data Collection Methods</h3>\n \n <p>We conducted semi-structured interviews with Veterans, GPD-CM staff, and liaising VA clinicians; periodic reflections with liaising VA clinicians and implementation team members; and drew upon detailed meeting notes. We used rapid qualitative methods and content analysis to integrate data and characterize modifications.</p>\n </section>\n \n <section>\n \n <h3> Principal Findings</h3>\n \n <p>After each scale-up wave—in response to variations in agency-level characteristics— we made iterative modifications to the implementation package to increase CTI adoption and fidelity across the diverse contexts of our scale-up sites. Modifications included adding, deleting, integrating, and altering the package; core package components were preserved.</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>Implementation packages for complex evidence-based practices undergoing scale-up in diverse contexts may benefit from iterative modifications to optimize practice adoption with fidelity. We offer a structured, pragmatic approach for iteratively identifying data-driven, midstream implementation package adjustments, for use in both VA and non-VA scale-up initiatives. Our project demonstrates the importance of assessing for and making modifications in a scale-up initiative, as well as the trade-offs of projects having simultaneous formative and summative evaluation aims.</p>\n </section>\n </div>","PeriodicalId":55065,"journal":{"name":"Health Services Research","volume":"59 S2","pages":""},"PeriodicalIF":3.1000,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11540582/pdf/","citationCount":"0","resultStr":"{\"title\":\"A structured approach to modifying an implementation package while scaling up a complex evidence-based practice\",\"authors\":\"Kristina M. Cordasco MD, MPH, MSHS, Sonya E. Gabrielian MD, MPH, Jenny Barnard BA, Taylor Harris PhD, MA, Erin P. Finley PhD, MPH\",\"doi\":\"10.1111/1475-6773.14313\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Objective</h3>\\n \\n <p>To describe <i>a</i> structured, iterative, data-driven approach for modifying implementation strategies for a complex evidence-based practice during a nationwide scale-up initiative.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Data Sources and Study Setting</h3>\\n \\n <p>We scaled-up implementation of Critical Time Intervention (CTI)—an evidence-based case management model—across 32 diverse community-based Veterans Affairs (VA) “Grant and Per Diem” case management (GPD-CM) agencies that serve homeless-experienced Veterans transitioning to independent living. Primary data were collected using qualitative methods.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Study Design</h3>\\n \\n <p>We embarked on a scale-up initiative while conducting a pragmatic randomized evaluation using a roll-out design, comparing two versions of a CTI implementation package tailored to VA's GPD-CM program. We iteratively assessed contextual factors and implementation outcomes (e.g., acceptability); findings informed package modifications that were characterized using the Framework for Reporting Adaptations and Modifications to Evidence-based Implementation Strategies.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Data Collection Methods</h3>\\n \\n <p>We conducted semi-structured interviews with Veterans, GPD-CM staff, and liaising VA clinicians; periodic reflections with liaising VA clinicians and implementation team members; and drew upon detailed meeting notes. We used rapid qualitative methods and content analysis to integrate data and characterize modifications.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Principal Findings</h3>\\n \\n <p>After each scale-up wave—in response to variations in agency-level characteristics— we made iterative modifications to the implementation package to increase CTI adoption and fidelity across the diverse contexts of our scale-up sites. Modifications included adding, deleting, integrating, and altering the package; core package components were preserved.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusions</h3>\\n \\n <p>Implementation packages for complex evidence-based practices undergoing scale-up in diverse contexts may benefit from iterative modifications to optimize practice adoption with fidelity. We offer a structured, pragmatic approach for iteratively identifying data-driven, midstream implementation package adjustments, for use in both VA and non-VA scale-up initiatives. 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A structured approach to modifying an implementation package while scaling up a complex evidence-based practice
Objective
To describe a structured, iterative, data-driven approach for modifying implementation strategies for a complex evidence-based practice during a nationwide scale-up initiative.
Data Sources and Study Setting
We scaled-up implementation of Critical Time Intervention (CTI)—an evidence-based case management model—across 32 diverse community-based Veterans Affairs (VA) “Grant and Per Diem” case management (GPD-CM) agencies that serve homeless-experienced Veterans transitioning to independent living. Primary data were collected using qualitative methods.
Study Design
We embarked on a scale-up initiative while conducting a pragmatic randomized evaluation using a roll-out design, comparing two versions of a CTI implementation package tailored to VA's GPD-CM program. We iteratively assessed contextual factors and implementation outcomes (e.g., acceptability); findings informed package modifications that were characterized using the Framework for Reporting Adaptations and Modifications to Evidence-based Implementation Strategies.
Data Collection Methods
We conducted semi-structured interviews with Veterans, GPD-CM staff, and liaising VA clinicians; periodic reflections with liaising VA clinicians and implementation team members; and drew upon detailed meeting notes. We used rapid qualitative methods and content analysis to integrate data and characterize modifications.
Principal Findings
After each scale-up wave—in response to variations in agency-level characteristics— we made iterative modifications to the implementation package to increase CTI adoption and fidelity across the diverse contexts of our scale-up sites. Modifications included adding, deleting, integrating, and altering the package; core package components were preserved.
Conclusions
Implementation packages for complex evidence-based practices undergoing scale-up in diverse contexts may benefit from iterative modifications to optimize practice adoption with fidelity. We offer a structured, pragmatic approach for iteratively identifying data-driven, midstream implementation package adjustments, for use in both VA and non-VA scale-up initiatives. Our project demonstrates the importance of assessing for and making modifications in a scale-up initiative, as well as the trade-offs of projects having simultaneous formative and summative evaluation aims.
期刊介绍:
Health Services Research (HSR) is a peer-reviewed scholarly journal that provides researchers and public and private policymakers with the latest research findings, methods, and concepts related to the financing, organization, delivery, evaluation, and outcomes of health services. Rated as one of the top journals in the fields of health policy and services and health care administration, HSR publishes outstanding articles reporting the findings of original investigations that expand knowledge and understanding of the wide-ranging field of health care and that will help to improve the health of individuals and communities.