Data management

A key challenge in qualitative research is to manage and make sense of the large volumes of data collected (Bloomberg and Volpe, 2012). In the section below I set out how I managed, organised and analysed the data described above in order to generate and interpret my findings. I followed the same key principles of data management, coding, and thematic analysis to generate findings to answer all three of the research questions posed in this study. However, I initially addressed

research questions one and two separately from research question three.

Observation data

As noted above, the period of observation was determined by the need to continue collecting quantitative data for the MDT Study until we reached a predefined sample size of 330 patient discussions per team. I collected qualitative data for the duration of this period, which meant that I had full audio recordings and field notes from 122 cancer MDT meetings. This equated to around 175 hours of audio, and nearly 1,500 individual patient discussions.

Given this volume of data, it was not practical to transcribe all the meetings

verbatim. As other researchers have described, quiet talk and background noise can obscure dialogue (Bucholtz, 2000). I also had to contend with overlapping speech, when members of the MDT either talked over each other, or in some instances, conducted entirely separate conversations between different groups at the same time.

Another challenge in this context related to the fact that a key area of interest for my research was to explore the contributions and participation of low status individuals. By their very nature, these interactions were at times difficult to capture in a written transcript because they tended to be less vocal than higher

84 status members of the team. For example, when CNSs or junior doctors whispered to other colleagues rather than voicing their opinions out loud, or where they would get the attention of another team member immediately before the meeting started to share information or get a key point across. This information was not always picked up by the recording and it was therefore not possible to transcribe these sorts of interactions.

To deal with these challenges, I used a combination of approaches to manage and analyse the qualitative observation data. This included using my field notes, transcribing a selection of audio data, and working directly from the audio files.

Each of these approaches has advantages and disadvantages. By using all three I aimed to overcome the disadvantages that any one approach might pose when used on its own (Tessier, 2012).

Figure 2: Advantages and disadvantages of qualitative data recording methods (adapted from (Tessier, 2012))

Observation field notes

I used my field notes to capture interactions that were not easily recorded or transcribed, for example, non-verbal cues. My field notes were also useful because they captured my initial thoughts while they were still fresh (Tessier, 2012). They

85 also gave me a manageable overview of the entire dataset, which I was able to use to identify specific instances of interest to return to on the audio or transcripts.

Observation transcripts

As mentioned above, it was not feasible to transcribe all 122 meetings verbatim.

Instead, I used a process of selective transcription (Emerson et al., 1995, Frykholm and Groth, 2011). This meant I had to decide how many and which meetings (or parts of meetings) to transcribe. When considering how much transcription to undertake, I took as my starting point my existing levels of familiarity with the observation data I had collected. As part of the MDT Study I had previously:

 attended 118 out of 122 meetings in person

 written up field notes for each meeting within 24 hours of attending

 re-listened to the audio recording of all meetings at least once

 selectively transcribed, coded and analysed a subset of meetings for the MDT Study (64 of the 122)

 participated in analytic conferences with five other researchers. This involved listening to audio files from a selection of meetings and discussing the coding and analysis process.

My main concern therefore was to capitalise on my familiarity with this extensive dataset, while at the same time, giving myself the opportunity to look at these data in a new light, and with different questions in mind. When I began analysis for my PhD therefore, I started by transcribing verbatim one meeting from each of the four teams. This had two purposes. First, it was an opportunity for me to re-immerse myself in the data, which I had collected 18 months previously. Secondly, by having a full transcript from each team I was better able to consider differences in

participation and influence throughout each discussion and for the duration of an entire meeting. For example, the detailed review of each full transcript enabled me to follow through cases from initial presentation to subsequent discussion and the final decision. This provided me with opportunities in my analysis that review of my

86 field notes alone could not – for example, to explore the features of case

presentations that led to input from lower status members versus those that did not.

I chose to transcribe meetings from the first month in which I had observed each team, when my field notes were the richest. This enabled me to produce a more detailed transcript as I could refer to my field notes throughout the process (for example, where it was not clear from the audio who said what). I also chose

meetings that I felt best reflected the ‘norm’ for each team, for example, where key members were in attendance and processes were followed as usual.

In addition to the verbatim transcript of four meetings, I also used a process of selective transcription (Emerson et al., 1995, Frykholm and Groth, 2011). Selective transcription was an approach we had used as part of the MDT Study, to enable us to manage the large volume of qualitative data collected (Raine et al., 2014a). I utilised the same approach for my PhD, although for the purposes of this study I used different criteria to select sections for transcription.

In order to address research question three, I theoretically sampled cases from the full dataset where a CNS or StR had contributed to discussion. This was possible because these individuals in all four MDTs spoke up much less frequently than others. To identify these cases I coded my field notes for any reference to these contributions during the MDT meetings. I then re-listened to the relevant section of audio file and transcribed those sections of audio that illustrated the lower status contribution, with the following exceptions. I did not transcribe references from my field notes that were:

 general observations only (e.g. ‘the CNS is the most likely to mention patient preferences, although she often does this very quietly’)

 about a lack of participation (e.g. ‘StR did not present any cases today’)

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 related to processes other than decision making (e.g. those that focused on teaching and learning)

 there were also a small number of cases that could not be transcribed because the audio quality was poor (e.g. when someone was talking very quietly) – in these cases I relied on my field notes as it was easier to hear during the meeting itself than when listening back to the audio.

This generated a total of 88 selective transcripts across the four teams, with 43 discussions involving CNSs and 30 selective transcripts with discussions involving StRs. 15 selective transcripts contained input from a combination of lower status groups (Table 6). These selective transcriptions enabled me to undertake a closer analysis of discussions that were most pertinent to research question three across the full observation dataset (Frykholm and Groth, 2011).

Table 6: Selective transcripts by team and lower status group

Team CNS only StR only Both CNS and StR Total

Gynae 10 5 2 17

Haem 1 4 10 6 20

Haem 2 5 13 2 20

Skin 24 2 5 31

Total 43 30 15 88

Working directly from the audio file

I used a software package called ExpressScribe to listen to the audio files from the meetings. This software allowed me to move back and forth through the files quickly and easily. Using my field notes as reference, I was able to return to specific dialogues of interest and to listen to these sections repeatedly. I could then

transcribe these sections, add more detail to my field notes, or write analytic memos. This approach enabled me to retain much of the contextual data, such as tone of voice, which can get lost in the process of transcription (Crichton and Childs, 2005). I also found that listening to the audio files helped me to remember specific events more vividly (Crichton and Childs, 2005).

88 Interview data

I also used field notes, transcripts and the original audio files to analyse data from the professional and patient interviews. However, the professional and patient interviews were ‘easier’ to transcribe because they involved only two individuals (the participant and me). In addition, they were conducted in quiet rooms which provided ideal conditions for audio recording (in contrast to the large and often noisy rooms where MDT meetings took place). As a result, I used professional transcription agencies with experience of health research and medical terminology to transcribe all 26 interviews verbatim. I reviewed each of the completed

transcripts against the audio file in order to check the quality of the transcription, and to ensure that all identifying information had been removed to protect the anonymity of participants (McLellan et al., 2003, MacLean et al., 2004). Re-listening to the audio files in this way also gave me an opportunity to further familiarise myself with the data (Patton, 2002).

Organising the observation and interview data

I imported all my field notes and transcripts into Nvivo V.10. This software enabled me to manage the large volume of data more easily.