Agua Potable

The literature reviewed in Chapter 2 highlighted a tendency in research and policy to focus on understanding why practice is problematic rather than examining the processes at play when caring works well. I wanted to take an alternative approach.

Mills, Bonner and Francis (2006) elaborate on the potentially negative relational

consequences of critical social science which include the silencing of marginal voices, the creation of social hierarchy, and the creation of a culture of negativity where deficit language becomes the norm.

As a result of the perceived drawbacks of focusing on the negative, a number of research methodologies have emerged that focus on a positive approach to the development of knowledge and change. These include future focused research (Walsh et al. 2008) and AI (AI) (Cooperider & Srivastva 1987).

AI has been defined as:

A theory and practice for approaching change from a holistic framework. Based on the belief that human systems are made and imagined by those who live and work within them, AI leads systems to move toward the generative and creative images that reside in their most positive core - their values, visions, achievements and best practices. (Watkins & Mohr 2001, pp. 262).

The purpose of AI therefore is “to generate knowledge within social systems and to use this knowledge to promote democratic dialogue that leads to a congruence between values and practices” (Kavanagh et al. 2008, p. 43).

AI aims to work towards emancipatory transformation (Grant & Humphries 2006; Reason & Bradbury 2001). The approach focuses on exploring with people what is valuable in what they do and how this can be built on, rather than on problems (Cooperrider, Whitney & Stavros 2003). One of the advantages in the use of this

approach is that it allows good practice to be defined. The literature reviewed in Chapter 2 has already highlighted that although there is a body of knowledge that identifies the defining attributes of good caring as perceived by patients, staff and families there is little data that develops understanding of the processes that are required to enable this to happen in the context of everyday practice.

The four principles for AI are that inquiry begins with appreciation, and is applicable, provocative and collaborative (Cooperrider & Srivastva 1987). The basic process of AI is to begin with a grounded exploration of the "best of what is" (discovery phase), then through visioning and debate collaboratively articulate "what might be" (dream phase), working together to develop "what could be"(design phase) and collectively

experimenting with "what can be"(destiny phase), (Moore 2008). In the design phase, cycles of change are developed and are then implemented in the destiny phase. Some appreciative inquirers have suggested that the destiny phase has moved away from a set of concrete activities or action plans to a more open process where the focus is on empowering, improving and making adjustments towards ongoing change (Egan & Lancaster 2005).

Although AI does not have a prescribed set of methods, those that are most commonly highlighted in the literature are interviews and affirmative questioning, to collect and celebrate the good news stories of a community or organisation. Authors also support use of creative methodologies that would encourage a range of different voices to be heard. In data analysis the emphasis is on co-analysis (Reed 2007). Much of the literature

describing the process in AI advocates one or two away days to discuss issues. This is often not possible in the busy world of health care where it is a challenge to release ward staff for mandatory training. Thus an approach that works carefully with the principles underpinning AI, and at the same time adopts a pragmatic stance in the context of the care setting was important in this study. How this was achieved is considered later.

In AI the topic may or may not be predetermined at the start of the inquiry depending on the context of the work to be undertaken (Cooperrider & Srivastva 1987). In this study the topic was already decided – that of compassionate care. However within this topic there were a number of sub-themes that participants could choose to focus on. So, for example, in this site they talked about their desire to know more about the patient, develop team working and be able to say to others what it is they do well. Work in the field needs to be dynamic, responding to the specific contexts where the project takes place (Coghlan & Brannick 2005). It was not therefore possible to identify at the start of the study what the interventions would be, but rather these were informed by the

exploratory work and the context.

Although Hosking (2002) would argue that there should be no attempt at achieving consensus under the principle of valuing multiple realities, there is a need to know what is universally valued within the unit if decisions about what people put energy into taking forward are to be achieved.

Within this AI study I acted as an appreciative inquirer. Questions that focused on what was working and how we could develop these aspects, rather than on problems were important. My role was described in Chapter 1 as that of an „insider‟ in that I was employed by the NHS and was to be considered part of the ward team. An advantage of the insider role is that it allows access to certain situations and information not normally made available to the outsider researcher and, by being a co-worker, participants are more likely to share the reality of their experiences (Meyer 1999).

In synthesising the literature that describes AI (Egan & Lancaster 2005; Cooperrider & Srivastva 1987; Grant & Humphries 2006; Moore 2008; Reed 2007) I have drawn out key principles that underpin working as an appreciative inquirer. These are illustrated in Table 4.

Table 4 -Principles of working as an appreciative inquirer

Principles of working as an appreciative inquirer

 Working with the principles of what works well rather than what are the problems

 Adopting a facilitative approach that encouraged participation and collaboration

 Enabling ownership – which sometimes means re-inventing the wheel

 Asking curious questions that are essentially non-judgmental in order to get at the heart of what is going on

 A commitment to real-time feedback to develop learning in a deliberate way

 Recognising and working with relationships between staff, patients and families in the context of practice

 Focusing on local everyday happenings

 Allowing the specific detail of the change to emerge over time and in response to the local environment

 Using creative and inclusive approaches to engage with people

 Developing knowledge in and for practice

 Analysing and reporting on the processes of inquiry which in itself is seen as an intervention for change

 Supporting people to take local actions forward, evaluate these and share experiences across the organisation

AI has been used extensively in organisational development. Positive benefits of this approach include improved co-operation between workers, establishing confidence of individuals and teams, and refocusing the types of questions being asked by the people working in the organisation (Bushe & Kassam 2005; Egan & Lancaster 2005).

In relation to organisational benefits, there is evidence thatAI helps to create self

reinforcing learning communities, which not only allows the person to improve their own practice, but by supporting people to articulate the positive, enables them to expand their capacity to see and encourage positive strengths in others. It is argued that this can provide a powerful core that supports the development of a real sense of identity for the unit or organisation (Ludema et al. 2003).

There is increasing evidence that AI as an approach is being used as a change

methodology in nursing (Carter et al. 2007; Reed & Holmberg 2007; Reed et al. 2002) but as yet its effectiveness has not been systematically examined and presented. There continues to be a lack of published research evaluating AI across all disciplines (Van der Haar & Hosking 2004).

The lack of systematic evaluation of AI may be due to the fact that it has not necessarily been an approach embraced by academics; rather its development sits firmly with the organisational development world where the emphasis may not be on rigorous evaluation. One could question whether systematic evaluation is indeed appropriate to the paradigm of relational constructivism and approach of AI. It may be that continuous evaluation throughout is more appropriate and needs to be storied as part of the process.

Bushe and Kassam (2005) in a meta analysis of cases that applied AI, found that all 20 cases achieved change in social processes, but that only seven cases achieved

transformational change in „how people thought‟ and developed what was considered to be new knowledge. Kavanagh et al. (2008) suggest interpreting these results with caution however, since the people who were writing the cases were also the consultants to the organisations. That said, it raises questions about the sustainable impact of AI and the

potential importance of raising the profile of evaluation within AI so that it is recognised as a valid approach to health care research. I return to this point later in this Chapter when discussing the attributes of action research (AR).