As described by Palinkas and colleagues (2011), data from qualitative interviews will be coded for analyses [
44]. Using a methodology of ‘Coding Consensus, Co-occurrence, and Comparison’ outlined by Willms [
45] and rooted in grounded theory (
i.e., theory derived from data and then illustrated by characteristic examples of data) [
46], interview transcripts will be analyzed. First, investigators will prepare short descriptive ‘memos’ to document initial impressions of topics and themes and their relationships and to define the boundaries of specific codes (
i.e., the inclusion and exclusion criteria for assigning a specific code) [
47]. Then, the empirical material contained in the transcripts will be independently coded by the project investigators to condense the data into analyzable units. Segments of text ranging from a phrase to several paragraphs will be assigned codes based on
a priori (
i.e., from the interview guide) or emergent themes (also known as open coding) [
48]. Codes will be assigned to describe connections between categories and between categories and subcategories (
i.e., axial coding) [
49]. Each text will be independently coded by at least two investigators. Disagreements in description of codes will be resolved through discussion between investigators and enhanced definition of codes. The final list of codes will consist of themes, issues, accounts of behaviors, and opinions that relate to implementation. Two investigators then will separately review transcripts to determine the level of agreement in the codes applied; agreement ranging from 66 to 97 percent depending on level of coding (general, intermediate, specific) indicates good reliability in qualitative research [
50]. Based on these codes, the software QSR NVivo [
51] will be used to generate a series of categories arranged in a treelike structure connecting text segments grouped into separate categories or ‘nodes’. These nodes and trees will be used to examine the association between different
a priori and emergent categories and to identify the existence of new, previously unrecognized categories. Finally, the different categories will be further condensed into broad themes using a format that places implementation failures within the framework of the organizational and system characteristics [
46]. The themes and their relationships to one another then will be organized to create a heuristic model of implementation process that can be used to develop and test hypotheses related to underlying processes of the SIC.