Desarrollo de las nuevas categorías teóricas

It is important to use qualitative research as a means of exploring digital health related issues to understand the contextual perspective of the community issues and their needs (Sweeny, Pritchard & Yao 2010). However, qualitative research studies can present challenges, because they generate large volumes of narrative data to be analysed. Using a thematic framework approach recognises the requirement for a transparent, systematic, and rigorous method for management and analysis of the large amount of qualitative data collected.

Since its development in the late 1980s by Jane Ritchie and Liz Spencer (Ayres, Kavanaugh & Knafl 2003; DeSantis & Ugarriza 2000), the thematic framework approach has been used as a method for management, description and explanation of

qualitative data, for systematically collected narratives. It is now widely used in health research and more recently in digital health research (Ritchie & Lewis 2003).

A thematic framework approach was chosen for this research because of its data analysis process. It provides a flexible, reflexive tool, adaptable for use with many qualitative methodologies, which has the aim of generating themes. It is not aligned with any particular philosophical, epistemological, or theoretical approach (Smith & Firth 2011). A thematic framework, as an analytical approach, maintains that a social world exists independent of individual understanding and is accessible in qualitative research via the understandings of partners, which are further interpreted by the researcher (Gale et al. 2013; Ritchie & Lewis 2003; Ward et al. 2013).

Through a process of summarisation, the thematic framework approach allows for in- depth management, organisation, and exploration of the data, resulting in a flexible but rigorous output. The process used in this research involved three phases: data management, data description, and data explanation. This was achieved in five discrete stages: familiarisation, identifying a thematic framework, indexing, charting and mapping, and interpretation. All phases and stages interconnected to form a methodological and rigorous thematic framework. Although the stages can be undertaken in a linear fashion where all data are collected before analysis begins, this research used a non-linear, iterative strategy where data collection and analysis were concurrent. The thematic framework approach effectively provided a transparent, flexible, and systematic audit trail that enhanced the rigour of the analytical processes and the validity of the findings (Gale & Sultan 2013; Pope, Ziebland & Mays 2000; Swallow et al. 2014).

The rationale for adopting the concept of a thematic framework approach was that it provided a straightforward, comprehensive review of the partners’ and groups’ narratives, their original accounts and their reflections. The findings, interpretation and discussion can be clearly identified and related back to the original data (Gale et al. 2013; Lacey & Luff 2009; Leal et al. 2015; Ritchie & Lewis 2003; Smith & Firth 2011; Swallow, Newton & Van Lottum 2003; Ward et al. 2013). The thematic data analysis aimed to accurately and innovatively organise the data, identify repetitions and extract themes from the data while ensuring:

 a clear and credible story line;

 interest and information;

 explanation of how and why the conclusions were reached (Berkowitz 1997).

The use of iteration in the data analysis also facilitated the researcher’s reflexive process, taking into account the effect of the personality and presence of the researcher in the research community (Hansen 2006a). This was regarded as essential for producing insight and developing meaning. Reflexive iteration, or visiting and revisiting the data and connecting them, was crucial to sparking insight, developing meaning, and emerging insights, which then progressively led to a refined focus and understanding (Berkowitz 1997). The research community’s experiences of and engagement with MyHR were captured, indexed as labels, charted to categories and finally mapped to themes. Once identified, the themes were interrogated as individual entities, and then the interrelationships were explained to ensure complete analysis. Using a thematic framework approach to analyse the data allowed innovation in its implementation while demonstrating logic (Berg 2004; Patton 2002; Srivastava & Hopwood 2009).

Using a thematic framework approach provided the data analysis with structure and established a rigorous process for managing data. Its flexibility allowed for creativity and assisted with adherence to the principles of CBPR (Houghton et al. 2015). Table 13, adapted from Houghton et al. (2015), displays the similarities between different data analysis approaches to qualitative research. However, unlike entirely inductive and interpretive approaches such as grounded theory, a thematic framework approach can be shaped by existing, a priori ideas and is less focused on producing a

new theory (Ward et al. 2013).

Table 13. Stages of data analysis

Stages of thematic data analysis Approach to

data analysis (Houghton et al. 2015)

Stages of analysis (Lincoln & Guba 1985) Analysis strategies (Morse 1994) The framework approach to data analysis (Miles & Huberman 1994) Purpose

1 Unitising Comprehending Broad coding Familiarisation

Absorption in raw data, listing key ideas and recurrent themes

What are the research community saying that is relevant to the question?

2 Categorising Synthesising Pattern coding _{Memo coding}

Identifying and constructing and initial thematic framework

Refining the list to create meaningful analysis.

Under what headings can the research community experiences and engagement be organised?

3 Filling in the _patterns Theorising Distilling and _ordering Indexing and _sorting

Annotation and labelling of data. Bringing together pieces of data into a recognisable group of concepts.

What parts of the data are about the same thing and belong together?

4 Member _checks Re-contextualising Developing _propositions Reviewing data _extracts

Formalising the

synthesis into a coherent set of explanations.

What other ways of organising the data are possible?

5 Data summary _{and display}

Generating a written précis for each theme and what each person is saying in the research

What in essence is the research community saying about each theme

The research used a thematic framework approach for the data analysis because it could be shaped by a priori ideas: the research objectives, design, group (Appendix

G) and interview guides (Appendix H), and emergent questions and topics. From initial management through to explanatory accounts the iterative, interconnected stages:

 provided descriptions and processes that guided the transparent analysis;

 provided a framework for a rigorous comprehensive investigation that strengthened and validated systematic data analysis and understanding;

 provided analysis driven by the research community’s original accounts and

thoughts;

 allowed for a complete review of the collected data.

Phases and stages of data analysis

The thematic framework approach used for the data analysis, depicted in Figure 8, has been adapted from Swallow, Newton and Van Lottum (2003), and as previously mentioned, although they are depicted as linear, interconnected phases, in practice the data management, description, and explanation were iterative. The analysis

systematically approached the phases of data management, description, and explanation in five iterative stages: familiarisation, identifying a thematic framework, indexing, charting, mapping, and interpretation (Spencer, Ritchie, Ormston, et al. 2014). This is described in the following sections.

PHASE 1 - DATA MANAGEMENT Stage-1 Familiarisation Transcription Immersion in all data

Listening to taped interviews and reading and rereading participant’s interviews and focus group transcripts

Listing key ideas and recurrent themes

Stage-2 Identifying a thematic framework

Drawing on a priori and emergent issues raised by participants Drawing on analytical themes arising from the recurrence of views and experiences

Providing a mechanism for labelling data in manageable bites or themes into a framework for subsequent retrieval

Stage 3 Indexing (labels)

Applying the analytical framework

Q3.1 Tell me about your family 3.1.1 Carer status

3.1.1.2 Informal carer 3.1.2 Family Health 3.1.3 Family Heritage 3.1.4 Family History

3.1.6 Role for PCEHR (MyHR) in family

Transcript examples I'm not a registered carer but I do care for my father who lives on his own and is legally blind but he looks after himself most of the time but I do go down and take him where he needs to go to appointments and things like that and make sure he is safe yes (CH6).

I'm going to talk to my parents and John's mother and my daughter-in-law and get them to register for the PCEHR. I think it makes sense I usually write them out (CH13). PHASE 2 - DATA DESCRIPTION Stage 4. Charting (categories) & mapping (themes)

View of quirkos© “flower

PHASE 3 - DATA