Identifying RELATIONSHIPS between CFIR Constructs using Qualitative Methods

Sarkies and colleagues published a systematic review of determinants/influences on implementation of programs designed to help patients avoid unnecessary hospitalizations. Determinants from the literature were mapped to the CFIR. This study goes beyond simply identifying determinants (which in itself, is useful; see Figure 2). They apply “relationship coding” to reveal presence and nature of relationships between constructs. Sometimes the relationships are unidirectional. For example, leaders influence the engagement of multidisciplinary teams that lead implementation. Sometimes relationships are bidirectional – or reciprocal. For example, leaders also have a reciprocal causal loop relationship with staff perceptions of confidence in their work, which in turn, has a reciprocal influence on staff relationships, which in turn, has a reciprocal relationship with patient engagement (see Figure 3). The authors use system dynamics causal loop diagramming to highlight these relationships and more. Based on their systematic review, Available Resources, Compatibility, Engagement, and Leadership Engagement are especially strong conduits within their proposed causal loop model. When coding data to identify individual constructs, it can feel like the dynamic “story” is lost, as information is “spliced” into independent constructs. These authors describe a path forward that maps out relationships to reveal complex adaptive system dynamics that are so prevalent in implementation efforts.