Transcript text collected under these broad headings was then reread and a second round of inductive sub-codes developed in response to new and emerging themes in the data (Corbin and Strauss 1990).
Two researchers (NB, LC) verified the interview transcripts by independently listening to the audio transcript while simultaneously reading the transcript text.
To classify interview transcript text, we used qualitative content analysis, which focuses on reducing text into manageable segments through application of inductive and/or deductive codes.
Meaning units i.e. parts of the original transcript text that are related to the aim of the study are selected from the interview transcript.
To explore the meaning of participants' responses, each code and category was interpreted in the context of the meaning of the broader transcript text.
Transcript text was organized within tables by theme, intervention and type of physician to compare and interpret the factors influencing LN staging practice.
The resulting data was thematically coded and analyzed using a social constructionist approach, developing codes from the data [ 29, 30] in an inductive process, and using QSR International's NVivo 10 software to apply codes to transcript text [ 31].
First, we used text management software (ATLAS.ti) to mark contiguous blocks of transcript text that pertained to the major topical domains of interest (how HIV status was disclosed, trigger event, reason for disclosure, short and long-term response to disclosure) within each social target group of the disclosure (spouse, family, friends, others).
In the course of reading and re-reading transcripts, text passages that appeared to be relevant with regard to the research questions were extracted and coded, i.e. labeled as a term preferably close to the text passage itself.
When department officials considered raising the quota of free messages, they asked the wireless company for a transcript of texts from Sergeant Quon and another employee.
