Journal of Psychosocial Nursing and Mental Health Services

CNE Article 

Appreciative Inquiry: A Research Tool for Mental Health Services

Julia L. Hennessy, RN, FCNANZ; Frances Hughes, RN, DNurs, ONZM, FACMHN, FNZCMHN

Abstract

Appreciative inquiry (AI) provides an alternative approach to the inquisitional style of uncovering “what went wrong and who is at fault” to instead “what can be done to make things better,” thus creating an environment that enables one to discover (investigate), dream (what could have been done instead), design (what needs to be done to bring about change), and deliver/ destiny (working with a whole of health and community approach to obtain the positive outcomes for mental health consumers). AI is transformational in nature and provides a way of viewing organizations from an enabling perspective. This article discusses the concept of AI, highlights opportunities and challenges that may be encountered, and explores the possibility of applying the AI concept to mental health research/inquiry. [Journal of Psychosocial Nursing and Mental Health Services, 52(6), 34–40.]

Ms. Hennessy is Executive Dean, Wellington Institute of Technology, Wellington, New Zealand; and Dr. Hughes is Visiting Professor, University of Sydney, Sydney, Australia.

The authors have disclosed no potential conflicts of interest, financial or otherwise.

Address correspondence to Julia Hennessy, RN, FCNANZ, Executive Dean, Wellington Institute of Technology, Private Bag 39803, Lower Hutt 5045, Wellington, New Zealand; e-mail: Julia.Hennessy@weltec.ac.nz.

Received: February 04, 2013
Accepted: December 02, 2013
Posted Online: February 05, 2014

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Abstract

Appreciative inquiry (AI) provides an alternative approach to the inquisitional style of uncovering “what went wrong and who is at fault” to instead “what can be done to make things better,” thus creating an environment that enables one to discover (investigate), dream (what could have been done instead), design (what needs to be done to bring about change), and deliver/ destiny (working with a whole of health and community approach to obtain the positive outcomes for mental health consumers). AI is transformational in nature and provides a way of viewing organizations from an enabling perspective. This article discusses the concept of AI, highlights opportunities and challenges that may be encountered, and explores the possibility of applying the AI concept to mental health research/inquiry. [Journal of Psychosocial Nursing and Mental Health Services, 52(6), 34–40.]

Ms. Hennessy is Executive Dean, Wellington Institute of Technology, Wellington, New Zealand; and Dr. Hughes is Visiting Professor, University of Sydney, Sydney, Australia.

The authors have disclosed no potential conflicts of interest, financial or otherwise.

Address correspondence to Julia Hennessy, RN, FCNANZ, Executive Dean, Wellington Institute of Technology, Private Bag 39803, Lower Hutt 5045, Wellington, New Zealand; e-mail: Julia.Hennessy@weltec.ac.nz.

Received: February 04, 2013
Accepted: December 02, 2013
Posted Online: February 05, 2014

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Service evaluations or research of mental health services provide evidence for what is not working well. The need for significant organizational change is usually “catalyzed by some form of external pressure or opportunity” (Watkins & Mohr, 2001, p. 53).

Since the 1990s, mental health services globally have undergone significant changes in the way they are delivered. The shift has been from an institutional-based model to an integrated community model. Services are required to be consumer focused, less restrictive, and predominately community based. Although the model of delivery has changed significantly, the way the model has been validated and researched remains predominately problem focused. The scrutiny under which mental health operates exposes it to a number of factors, which on first examination, do not appear to be interrelated but instead are seen as singular events. However, it is only at the time that a sentinel or catastrophic event occurs that these single events are often just one of the many “holes” that appear in the system, culminating in an adverse event.

Vincent (2011) uses Reason’s “Swiss cheese” metaphor as a way of explaining adverse events within the health system. This metaphor suggests that hazards exist within systems and, for the most part, are managed through a series of barriers by way of policies, procedures, practice, and legislation. However, each of the barriers has weaknesses or holes, which the hazard may break through at any time. This theory suggests that the holes open and close at random. The individual events on their own do not present a significant issue; however, as suggested by Perneger (2005) if by chance when all the holes become aligned, the hazards reach the patient and it is this alignment of events that causes harm. Searl, Borgi, and Chemli (2010) believe it is the systems, rather than people, that are inherently faulty. This model implies a systems failure approach within a health care setting rather than the suggestion that the individual practitioner is at fault. The model suggests that there is inherent randomness to these types of events, but that there is no intent on the part of health practitioners to cause deliberate harm. It is only when an adverse event occurs and is reviewed that it is possible to identify that one single random act would not have caused an adverse event. Rather, it is a series of often minor events that led up to a patient-harming event.

When these adverse events occur in mental health, the subsequent inquiries, which are inevitably conducted, focus on identifying blame; thus, the strong focus on recovery-based services is contradicted and requires a shift in philosophy and thinking if mental health services are to be strengthened. Appreciative inquiry (AI) offers the opportunity to view services in a more positive way while highlighting opportunities and challenges but not discounting or disregarding the need for improvement.

Appreciative Inquiry

AI originates in the area of organizational change and is based on the premise that inquiry does not have to be problem based. AI suggests determining what works well within an organization (Cooperrider & Whitney, 2003) and, by viewing what is working well, allows the telling of stories that celebrate achievements while addressing things that still need to be done or changed. AI uses a 4-D cycle (discovery, dream, design, and destiny) as a way of understanding transformational change within a positive framework and provides an alternative to the more traditional approach of research or inquiry, which is generally based on a deficit model.

AI supports growth and movement away from a scientific-focused perspective of research toward a humanistic approach (Cooperrider & Srivastva, 1987). An AI approach seeks to reveal and understand achievements while addressing areas that can be improved. “When we engage in appreciative inquiries, we focus on what makes us feel most alive, on our successes and their determinants, and on the strengths of our organization” (Collopy, 2009, p. 1). The use of an AI approach is said to be transformational and aims to seek improvement (Smythe & Payne, 2008).

Challenges With The AI Approach

Researchers adopting an AI approach focus on the good, the strong, and what is already working, as opposed to taking a traditional approach of exploring what is not right. This approach differentiates AI from other research approaches. Whitney and Trosten-Bloom (2010) argue that AI is built on the premise of grounded research by “engaging members of an organization in their own research—inquiry into the most life-giving forces in their organization, the root causes of their success, and discovery of their positive core” (p. 52). Cooperrider and Srivastva (1987) introduced AI as their way of seeing the world and then the people in it, through a lens that magnifies the positive aspects of those beings.

Detractors of AI suggest that if only the good is looked for than only the good is what will be found. It could, however, be argued that this criticism can also be applied to problem-based research, which uses a negative (problem) framework as a starting point and therefore will always find problems. The application of AI does not mean that problems are silenced; the issues are reframed and presented in terms of “how could things be better.”

An Enabling Tool for Mental Health

Mental health services are as much about human interactions and relationships as they are about health. Peplau (1995) encapsulated the humanizing aspects of mental health when she challenged nurses to think not of “patients” but to think of “persons.” Relationships, as part of any interaction, are not static; they are constantly evolving and subject to many of the influences that are part of what it means to be human. It is those interactions and developments that warrant attention. The use of AI as a research method for mental health is in itself innovative and could provide an approach that is both modern and transformational.

Synergy of Models

The constructs for AI have a high level of congruency with the current philosophies that are reported to underpin mental health services. However, to date, no published studies explore AI as a tool for examining mental health. The focus for AI within the mental health context is that it is affirming, uses positive inquiry, and is closely aligned to both strengths- and recovery-based models.

The concepts of strengths-based service delivery, tidal models, and recovery share many values, ethos, and positive practices that those involved recount with excitement and pride to indicate how the service provided works effectively (Smythe & Payne, 2008). AI supports a strengths-based ideology as it also seeks to deliver and discover from a model of empowerment.

Mental health services have historically endured a culture of blame; thus, the use of AI allows for the shift from “the study of problems to the study of success” (Preskill & Tzavaras, 2006, p. 132). Whitney and Trosten-Bloom (2010) contend that the process of AI is an evolutionary process, which has been shaped around an individual’s or organization’s understanding of positive change. AI, according to Whitney and Trosten-Bloom (2010), has been influenced and developed by social constructs, image theory, and grounded research. Carter (2006) describes AI as a way of “studying and exploring what gives life to human systems when they are at their best” (p. 50).

The asking of questions is what makes AI successful, with the focus of the inquiry unlocking potential and possibilities. Preskill and Tzavaras (2006) suggest that reframing questions using an appreciative approach allows for the collection of data about “participants’ peak experiences” (p. 77). The purpose of this approach was to “accept that respondents’ views were sequenced as one possible version of events” and that “each theme carried an inspirational title and is expressed in a positive, substantive future based goal” (Bowles & Jones, 2005, p. 285). The affirming of aspirations and goals fits within a strengths-based, recovery-focused approach of mental health services. Clossey, Mehnert, and Silva (2011) suggest that if mental health services were to adopt AI as a tool “that changes discourse and as such may be very appropriate in helping mental health organizations to create a recovery-orientated culture” (p. 265). Reed (2007) describes AI as showing “a family resemblance to different research models” (p. 45). According to Carter (2006), the use of an AI approach to research is more about “working and thinking with people rather than just about them” (p. 49). This is congruent with mental health services being about working with people, rather than something that is being provided. Watkins and Mohr (2001) describe AI as “a theory, a mindset, and an approach that leads to organizational learning and creativity” (p. 6).

Appreciative-Related Models Within a Mental Health Context: Recovery- and Strengths-Based Care

In a study undertaken by Case Consulting (2003), the National Certificate in Mental Health Support Work was found to be “consistent with a recovery approach, despite the fact that the unit standards don’t specifically reflect this” (p. 67). There is also a requirement that mental health services in New Zealand demonstrate their alignment with both recovery competencies and strengths-based models. Cooperrider (2009) argues that organizations are living systems that connect to a “full and rich omnipresence of strengths” (para. 9). It is the uncovering of this richness that provides some of the fabric for AI to be a useful tool for studies about mental health. AI supports the recovery- and strengths-based approach that mental health services strive to achieve. The concept encapsulated within AI has links to the recovery concept in mental health, as both are orientated toward identifying the positive while not discounting the negative. Many of the therapies developed and used in mental health support an appreciative approach as they seek to re-program negative thinking into positive ways of operating within society.

Barrett and Fry (2005) emphasize that bold choice of topics for study can be generative, challenge assumptions, and open up new possibilities for action. More specifically, choosing a positive, hopeful topic for inquiry will dislodge old patterns, interrupt taken-for-granted assumptions, provoke wonderment, and lead to capacity building. Whitney and Trosten-Bloom (2010) suggest that affirmative topics focus on what people want to see grow and flourish and should evoke conversations of the desired future. They then suggest that “topics:

  • Are positive. They are stated in the affirmative.
  • Are desirable. The organization wants to grow, develop, and enhance them.
  • Stimulate learning. The organization is genuinely curious about them, and wants to become more knowledgeable and proficient in them.
  • Stimulate conversations about desired futures. They take the organization where it wants to go. They link to the organization’s Change Agenda” (p. 133).

Philosophical Underpinnings Within Mental Health Services

Recovery

Another common theme in the literature, as described by Jacobson and Greenly (2001), is that the philosophical underpinnings of recovery—such as hope, connection, healing, empowerment, self-help, self-determination, resiliency, skill building, a positive culture for healing, a focus on strengths and possibilities, and education—are supported through documents from the Mental Health Commissions of New Zealand (2001) and Te Kaitataki Oranga (2007). The Mental Health Commission of Ireland (2005) suggests that “recovery is not a linear process; it is an individual process of small goals and achievable steps” (p. 11) and that “the recovery approach is more compatible with community-based models of service provision” (p. 2). Fotu and Tafa (2009) suggest that “recovery can be both a destination and a journey” (p. 164). Mary O’Hagan, Commissioner, Mental Health Commission, New Zealand, articulates some of the criticisms of recovery, as both a word and concept, from service users’ and providers’ points of view. For instance, some service users say that the word “recovery” implies being restored to a place where they were prior to their illness when in fact they believe they have been transformed by the experience. Other service users disregard the need for recovery as they believe that either they do not have an illness in the first place, or they do not find the madness undesirable. O’Hagan (2004) notes that some providers criticize recovery as being “‘esoteric nonsense’…‘hard to grasp’ and…lacking an ‘evidence base’” (p. 1). Importantly, O’Hagan discusses the difficulty that recovery originated in the United States, whereas an individualistic approach may be less useful in a more socially oriented society such as New Zealand. Not surprisingly, the lack of a consistent definition adds to the complexity of developing empirical evidence and engaging in research on the subject (Jacobson & Greenley, 2001; Liberman & Kopelowicz, 2005).

Sutcliffe (2006) suggests that although the recovery approach, and the empowerment of service users, recognizes the need to “address the historical and societal conditions which contributed to their being disempowered” (p. 38), those same constructs are being applied to mental health services. There is a belief held by some that the key to recovery is to be found in story telling. By bearing witness, or telling my story, people discover the personal truth of their own life—as opposed to the artificial, theoretical truths offered by different psychiatric professionals. The process of recovery also has its roots in narrative therapy; it is by talking about what happened, how it affected the person, and what it meant to the person, that the discussion can move toward what might need to be done to deal with, and respond to, the consumer’s experience of mental illness. Reclamation is considered by many to be the first step of the recovery process. This process establishes the setting to enable the consumer to tell his or her story. Rapp and Goscha (2006) express the view that a strengths-based focus honors skills, competencies, and talent, as opposed to viewing the deficits.

The Mental Health Commission (2007) describes recovery as “living well in the presence or absence of mental illness” (p. 86). The National Consensus Statement on Recovery issued by the U.S. Substance Abuse and Mental Health Services Administration (n.d.) defines recovery as “a journey of healing and transformation enabling a person with a mental health problem to live a meaningful life in a community of his or her choice while striving to achieve his or her full potential.”

Despite two decades of discussion and debate around the word recovery, and what it means in mental health, confusion still exists regarding its definition (Cobigo & Stuart, 2010; Davidson, O’Connell, Tondora, & Kangas, 2006; Davidson, Sells, Sangster, & O’Connell, 2005). However, there is considerable agreement that recovery in mental health may be defined in terms of an outcome (Onken, Craig, Ridgway, Ralph, & Cook, 2007), such as the ability to lead a good and satisfying life despite the illness or presence of symptoms (Deegan, 1998; Deegan & Drake, 2006). There is also a notion that recovery is a process that is described as being a nonlinear lived experience involving both self-discovery and transformation, and culminating in an understanding that symptoms of the illness are not definitive in terms of one’s self-identity (Cobigo & Stuart, 2010; Davidson et al., 2005). Cobigo and Stuart (2010) describe how the concept of recovery emphasizes the development of new personal meanings that go beyond the illness experience, social empowerment, and participation.

The Tidal Model

Another way of viewing recovery has been developed by Barker (2003) by way of the Tidal Model. This model aims to protect the ever-evolving story, language, and understanding of each individual and has been used to study outcomes with some success in several countries (Baker & Buchanan-Baker, 2006). This was the first research-based model of mental health recovery, developed originally by nurses, with the active support of individuals who were using, or had used, psychiatric services.

Many recovery approaches focus on self-management—helping people to manage their symptoms or plan to achieve wellness. Such approaches are often only useful after the individual has recovered from the major crisis or breakdown, which required mental health care initially. The Tidal Model is seen as an ebbing and flowing process, and recovery commences at the individual’s lowest point. The development of the Tidal Model represented a unique collaboration between professionals involved in delivering mental health services and individuals who needed such support. From its earliest beginnings, the Tidal Model drew on the support of user/ consumer consultants, both within the United Kingdom and internationally. These consultants helped field test the various individual and group processes of the Tidal Model, helping shape and refine them so that they became consumer-friendly. This model of collaboration continues to the present day, as adjustments are made to some of the original processes and the list of supporting activities.

The Tidal Model developed by Barker (2003) is a philosophical approach to the discovery of mental health; it is focused on empowering people to reclaim their personal story by recovering their voice. This use of one’s own language, metaphors, and stories is used to express some of the meaning of the individual’s life. This is the pathway toward recovery. There have also been tensions between recovery models and particular evidence-based practice models in the transformation of U.S. mental health services. The final report of the President’s New Freedom Commission on Mental Health (2003), with its emphasis on the recovery model, has been interpreted by some critics to state that everyone can fully recover through sheer willpower, and therefore gives false hope to those judged unable to recover and implicitly blames those individuals judged unable to recover. However, the critics have themselves been charged with undermining consumer rights and failing to recognize that the model is intended to support an individual in his or her personal journey rather than expecting a given outcome, and that it relates to both individual and social and political support and empowerment.

Applying an Appreciative Approach

Watkins, Mohr, and Kelly (2011) identify five generic processes for applying AI as a framework. They are:

  1. Focus on the positive as a core value;

  2. Inquire into stories of life-giving forces;

  3. Locate themes in the stories and select topics from the themes for further inquiry;

  4. Create shared images for a preferred future; and

  5. Innovate ways to create that preferred future (p. 82).

These processes guide both the focus and analysis of the inquiry. Themes are grouped and clustered and then analyzed using the 4-D cycle, while seeking to uncover the positive aspects of research being undertaken.

The 4-D stages are described below and provide a framework for analyzing the emerging themes. When translating current research methodology into AI terminology, Cram (2010) and Whitney and Trosten-Bloom (2010) suggest that the first stage is discovery or, as usually referred to in traditional terms, as the stage of initial inquiry. The next stage is the part of research that provides the opportunity for participants to dream about what could be. The third stage is design, which enables participants to visualize or articulate the future and the actions that will change the destiny, or as it has been referred to as delivery (Reed, 2007), completes the cycle.

Discovery: Mobilizing the entire system by engaging all stakeholders in the articulation of strengths and best practices. Identifying the best of what has been and what is. The analysis will seek to identify the insights that emerge from the data related to the positive.

Dream: Creating a clear results-oriented vision in relation to discovered potential and in relation to questions of higher purpose, such as “What is the world calling us to become?” The dream aspect of analysis is the positive spin on turning issues and problems into innovative solutions. The data are analyzed to clearly identify strategies for change.

Design: Creating possibility propositions of the ideal organization, articulating an organization design that an individual feels capable of drawing on, and magnifying the positive core to realize the newly expressed dream.

Destiny: Strengthening the affirmative capability of the whole system, enabling it to build hope and sustain momentum for ongoing positive change and high performance.

Lahaye and Espe (2010) view the structure and content of the appreciative interview as being essential for the success of any AI process. This view is supported and further developed by Cooperrider and Whitney (2005), as they suggest that the heart of the interview is the discovery phase.

The use of positive questions, which is what makes AI unique, often reminds people of accomplishments and experiences but also builds on strengths and opportunities that are seen as heartening and inspiring. Smythe and Payne (2008) suggest the AI approach to research should reach out and widen the research question, rather than narrowing it to one particular aspect, with a view to looking at what works well. Exploring through the AI lens provides a transformative approach, allowing the participants to “hear” their positive experiences while still seeking answers. It is a nonjudgmental and strengths-based approach and provides an appropriate methodology for a study of aspects of mental health services.

AI-framed questions are about the nature of, worth of, quality of, and significance of certain situations (Preskill & Tzavaras, 2006). The appreciative interviewing takes the participants through a self-reflection process and invites them to examine their successes and identify ways of improvement (Preskill & Tzavaras, 2006). Cooperrider, Whitney, and Stavros (2005) identify three stages for the interview: (a) opening questions (peak experiences), (b) questions centered around the topic (actualities), and (c) concluding questions (what the future holds). The interview process is broken into two parts. Part A is where the question must evoke a real personal experience and narrative story that helps participants determine and draw on the best learnings from the past (and present). Part B allows participants to go beyond the past to envision the best possibility of the future.

Conclusion

The model of inquiry posed and described provides a way to research and evaluate adverse events in mental health services in a manner that facilitates the narrative through listening, validating, celebrating, and revealing. The AI method creates an environment that is nonthreatening and nonjudgmental while seeking answers to the questions posed. AI has the potential to provide a balance and a way forward for research within mental health. The 4-D cycle provides a transformational framework for the collection and management of data. AI encourages all individuals to tell their stories in a way that is positive and has the potential to change the way mental health services are researched in the future.

Many truths can be discovered if the right environment is created. Using an AI approach may be the key to unlocking these many truths. If mental health services were to adopt AI as an approach to providing services, real learning could begin. Watkins and Mohr (2001) suggest that the AI approach explores the life-giving forces and that the data collected will “help illuminate” (p. 75) the understanding and provide “a mutual learning process” (p. 76).

AI creates an environment that gives permission for the truth to be told and therefore heard. The truth is based on one’s own world view and ensures the story is heard. It also creates an environment that is responsive to change, taking away the blame thereby allowing the learning to occur. The building blocks of learning form by engaging in positive conversations, which in turn help build hope that has a shared image, dreams, and visions of a preferred future (Watkins & Mohr, 2001).

AI provides a way for viewing mental health services from a continuous improvement model, which allows the good to be acknowledged and the “better” to be celebrated. The challenge for many services and practitioners will be to let go of the usual practices used for inquiries and to be bold in adopting a new way to view what could be and what needs to be to make things better.

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  • Vincent, C. (2011). The essentials of patient safety. Retrieved from http://www1.imperial.ac.uk/resources/5D671B2E-1742-454E-9930-ABE7E4178561/vincentessentialsofpatient-safety2012.pdf
  • Watkins, J.M. & Mohr, B.J. (2001). Appreciative inquiry: Change at the speed of imagination. San Francisco, CA: Jossey-Bass/Pfieffer.
  • Watkins, J.M., Mohr, B.J. & Kelly, R. (2011). Appreciative inquiry: Change at the speed of imagination (2nd ed.). San Francisco, CA: Pfieffer. doi:10.1002/9781118256060 [CrossRef]
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Keypoints

Hennessy, J.L. & Hughes, F. (2014). Appreciative Inquiry: A Research Tool for Mental Health Services. Journal of Psychosocial Nursing and Mental Health Services, 52(6), 34–40.

  1. Appreciative inquiry (AI) provides an approach to evaluating and researching mental health services in a manner that is nonthreatening.

  2. AI incorporates many of the principles from the strengths-based and recovery models.

  3. AI provides the opportunity for mental health services to recognize and enhance things that are working well while not discounting what needs to change.

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