Innovation Adaptability

Adaptability relies on a definition of the core components (elements that cannot be changed) versus the adaptable periphery (elements that can be changed) of the innovation itself (Fixsen, 2007; Greenhalgh, Robert, et al., 2004). A component analysis can be performed to identify the core components versus adaptable periphery (Carroll et al., 2007), but often the distinction is one that can only be discerned through trial and error as the innovation is disseminated more widely and adapted for a variety of contexts (Mendel et al., 2008). The tension between the need to achieve full and consistent implementation across multiple contexts while providing flexibility to adapt the innovation as needed is real and must be balanced, which is no small challenge (Perrin et al., 2006; von Thiele Schwarz et al., 2019).

The original CFIR elaborated further on this construct, stating that information about the hard core and soft periphery can be used to assess “fidelity” as an implementation outcome (Denis et al., 2002). The hard core may be defined by a research protocol or “black-box” packaging, while the soft periphery consists of factors that vary from setting to setting. For example, a computerized report system may have a hard core that users cannot change but these core components might be accessed from different launch points, depending on workflows of local settings. Greenhalgh et al. describe aspects of adaptability under “fuzzy boundaries” and “potential for reinvention” (Greenhalgh, Robert, et al., 2004 p 596-597). An innovation that can be easily modified to adapt to the setting is positively associated with implementation (Gustafson et al., 2003; Leeman et al., 2007; E. Rogers, 2003).

Inclusion Criteria

Include statements regarding the (in)ability to adapt the innovation to implementation context, e.g., complaints about the rigidity of the protocol. Suggestions for improvement can be captured in this code but should not be included in the rating process, unless it is clear that the participant feels the change is needed or else the program cannot be adapted.

Exclusion Criteria

Exclude or double code statements that the innovation did not need to be adapted to Compatibility.

Additional qualitative coding guidelines that are aligned with the Updated CFIR will be added in the near future.

Inclusion Criteria

Include statements regarding the (in)ability to adapt the innovation to implementation context, e.g., complaints about the rigidity of the protocol. Suggestions for improvement can be captured in this code but should not be included in the rating process, unless it is clear that the participant feels the change is needed or else the program cannot be adapted.

Exclusion Criteria

Exclude or double code statements that the innovation did not need to be adapted to Compatibility.

Additional qualitative coding guidelines that are aligned with the Updated CFIR will be added in the near future.

Carroll, C., Patterson, M., Wood, S., Booth, A., Rick, J., & Balain, S. (2007). A conceptual framework for implementation fidelity. Implement Sci, 2(1), 40.

Denis, J.-L., Hébert, Y., Langley, A., Lozeau, D., & Trottier, L.-H. (2002). Explaining Diffusion Patterns for Complex Health Care Innovations: Health Care Management Review, 27(3), 60–73. https://doi.org/10.1097/00004010-200207000-00007

Fixsen, D. L. (2007). Implementation Research: A Synthesis of the Literature. University of South Florida, Louis de la Parte Florida Mental Health Institute.

Greenhalgh, T., Glenn Robert, Paula Bate, Olympia Kyriakidou, Fraser Macfarlane, & Richard Peacock. (2004). How to Spread Good Ideas (p. 424). National Co-ordinating Centre for NHS Service Delivery and Organisation R & D

Gustafson, D. H., Sainfort, F., Eichler, M., Adams, L., Bisognano, M., & Steudel, H. (2003). Developing and testing a model to predict outcomes of organizational change. Health Serv Res, 38(2), 751–776.

Leeman, J., Baernholdt, M., & Sandelowski, M. (2007). Developing a theory-based taxonomy of methods for implementing change in practice. J Adv Nurs, 58(2), 191–200.

Lewis, C. C., Mettert, K. D., Stanick, C. F., Halko, H. M., Nolen, E. A., Powell, B. J., & Weiner, B. J. (2021). The psychometric and pragmatic evidence rating scale (PAPERS) for measure development and evaluation. Implementation Research and Practice, 2, 263348952110373. https://doi.org/10.1177/26334895211037391

Lewis, C. C., Mettert, K., & Lyon, A. R. (2021). Determining the influence of intervention characteristics on implementation success requires reliable and valid measures: Results from a systematic review. Implementation Research and Practice, 2, 263348952199419. https://doi.org/10.1177/2633489521994197

Mendel, P., Meredith, L. S., Schoenbaum, M., Sherbourne, C. D., & Wells, K. B. (2008). Interventions in organizational and community context: A framework for building evidence on dissemination and implementation in health services research. Adm Policy Ment Health, 35(1–2), 21–37.

Perrin, K. M., Burke, S. G., O’Connor, D., Walby, G., Shippey, C., Pitt, S., McDermott, R. J., & Forthofer, M. S. (2006). Factors contributing to intervention fidelity in a multi-site chronic disease self-management program. Implement Sci, 1, 26.

Rogers, E. (2003). Diffusion of innovations: 5th ed. Free Press.

von Thiele Schwarz, U., Aarons, G. A., & Hasson, H. (2019). The Value Equation: Three complementary propositions for reconciling fidelity and adaptation in evidence-based practice implementation. BMC Health Services Research, 19(1), 868. https://doi.org/10.1186/s12913-019-4668-y