Data analysis
MINOR
Data was analyzed using qualitative content analysis as described by Hsieh and Shannon [Data analysis was conducted as follows: 1) Creation of a coding scheme with deductive codes (PH), 2) meeting to review the coding scheme with the research team (PH, IS, HC (cp. acknowledgements)), 3) coding of approximately 25% of the data (including transcripts, field notes, and minutes) by PH, 4) coding of another approximately 25% of the data (including transcripts, field notes, and minutes) by AL, 5) discussion of ambiguities and open questions between PH and AL and revision of the coding scheme, 6) coding of the remaining data by AL, 7) discussion and revision of codings and coding scheme between PH and AL until consensus was reached, 8) comprehensive quality control (i.e., PH reviewed all codings initially assigned by AL and vice versa), 9) discussion of ambiguities and open questions between PH and AL and revision of codings and coding scheme, 10) meeting to review the coding scheme with the research team (PH, AL, IS, HC), 11) revision and finalization of the coding scheme and the codings by PH and AL.After the coding scheme was finalized, PH and AL independently classified each code as a major, mid-level, minor, or no influencing factor within this SDM implementation trial. The results were discussed by PH and AL until consensus was reached for each code. In the main body of this manuscript, we present only codes and respective codings, which were identified as major influencing factors in this consensus process. We defined those codes as major influencing factors that both coders (PH, AL) rated as being highly relevant to explain the implementation process in this trial. Reasons for high relevance could be high frequency of codings in process interviews, field notes, and minutes and/or the perceived impact of the influencing factor by the coders. The entire coding scheme including all CFIR constructs and all additional inductive codes is described in Additional File As a quantitative analysis step, for each code the number of codings in total, the number of coded documents, and the number and percentage of codings in which the influencing factor was classified as a barrier or a facilitator to SDM implementation (cp. meta-codes) were also included in Additional File For qualitative content analysis, we used MAXQDA software (VERBI GmbH, Berlin, Germany). Descriptive statistics of demographic data were conducted with IBM SPSS Statistics 25 (IBM Corp, Armonk, NY).