We reviewed the merging at each stage to observe how the statements were clustered and stopped the analyses when agglomeration best represented the data. We used the maximum and minimum numbers of clusters created by stakeholders during the sort and rate task (range = 14 to 4) as the start and end point for investigating PI3K inhibitor the cluster merging as the analyses progressed. We generated a stress value to measure how well the final concept map represented data; the target was a value between 0.21 and 0.37 (Kane and Trochim, 2007). Two investigators MW, MA then independently applied a name to clusters based on the statements that fell within each cluster; consensus on the final cluster name was reached through discussion.

Following this, we created the final concept map; and go-zones, which comprised statements that rated above average on both perceived importance and feasibility to implement. From the brainstorming phase participants generated 441 statements, which we synthesized to 58 statements. Sixteen stakeholders (N = 16) from the core representative group participated in the sorting and rating phase (two participants completed the sorting task only, one completed the rating task only, and 13 completed both the sorting and rating task). The point map generated from the multidimensional scaling analysis yielded a stress value of 0.23, which

acceptably represented the data and fell within typical concept mapping values (Kane GS-1101 order Thiamine-diphosphate kinase and Trochim, 2007 and Rosas and Kane, 2012). Each statement was represented by a point, with similar ideas represented

by points located closer together. The statements were then statistically partitioned or clustered into like ideas or concepts through cluster analysis. We identified a 7-cluster solution that best represented the data (Fig. 2). Smaller clusters, those with less shaded area inside the cluster border, or clusters with a high density of statement reflected a closely related concept whereas larger clusters with fewer statements reflected a broader concept. For example, clusters 1, 2, and 3 had a high density of statements within the cluster border. This indicated that participants commonly placed these statements together and shared a common theme. Clusters contained between 4 and 16 statements (Table 2) and are presented in the order grouped by the cluster analysis. We provide bridging values, a measure of the degree to which a statement was sorted with its neighbors, along with mean values for each cluster. The average cluster bridging values for clusters 1, 2, and 3 were low (range = 0.08 to 0.16). Thus, the statements in these clusters were commonly sorted together and reflected a shared concept. We present rating scores for each statement, grouped by cluster as per their order in the hierarchical cluster analysis (Table 2). Participants scored each statement on two constructs related to implementation; (1) relative importance, and (2) feasibility to implement.