Abstract
BackgroundLogic models are commonly used in evaluations to represent the causal processes through which interventions produce outcomes, yet significant debate is currently taking place over whether they can describe complex interventions which adapt to context. This paper assesses the logic models used in healthcare research from a complexity perspective. A typology of existing logic models is proposed, as well as a formal methodology for deriving more flexible and dynamic logic models.AnalysisVarious logic model types were tested as part of an evaluation of a complex Patient Experience Toolkit (PET) intervention, developed and implemented through action research across six hospital wards/departments in the English NHS. Three dominant types of logic model were identified, each with certain strengths but ultimately unable to accurately capture the dynamics of PET. Hence, a fourth logic model type was developed to express how success hinges on the adaption of PET to its delivery settings. Aspects of the Promoting Action on Research Implementation in Health Services (PARIHS) model were incorporated into a traditional logic model structure to create a dynamic “type 4” logic model that can accommodate complex interventions taking on a different form in different settings.ConclusionLogic models can be used to model complex interventions that adapt to context but more flexible and dynamic models are required. An implication of this is that how logic models are used in healthcare research may have to change. Using logic models to forge consensus among stakeholders and/or provide precise guidance across different settings will be inappropriate in the case of complex interventions that adapt to context. Instead, logic models for complex interventions may be targeted at facilitators to enable them to prospectively assess the settings they will be working in and to develop context-sensitive facilitation strategies. Researchers should be clear as to why they are using a logic model and experiment with different models to ensure they have the correct type.
Highlights
Logic models are commonly used in evaluations to represent the causal processes through which interventions produce outcomes, yet significant debate is currently taking place over whether they can describe complex interventions which adapt to context
Scholars influenced by complexity science have argued that the Medical Research Council (MRC) guidance is appropriate only for complicated interventions that work roughly the same way in different settings
Incorporate differences of opinion while we recognise that the type 4 logic models we propose will be less suitable for forging agreement among stakeholders than traditional logic models, accommodating differences of opinion may be more suitable for complex interventions given that the potential for disagreement increases with more complex problems [33]
Summary
We have proposed a typology of logic models, including strengths and weaknesses, to help researchers select between different logic model types in intervention research. We have outlined a formal methodology for developing more dynamic logic models than those which currently exist, incorporating aspects of the PARIHS model into a traditional logic model structure. These “type 4” logic models are capable of expressing interaction between interventions and context but some change to how logic models are used is required. We propose that type 4 logic models should be developed and refined through rigorous qualitative research rather than consensus-building exercises. They should seek to guide future users of complex interventions to help them develop context-sensitive facilitation strategies.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
