Methodology

Introduction

In previous chapters, the purpose of the study was described and the literature reviewed. In the continuation, this chapter will present the methodology used in this study: a set of methods and tools I will use to collect and analyze data and deliver conclusions. A methodology is the activity “of choosing, reflecting upon, evaluating and justifying the methods you use” (Wellington, 2015, p. 33).

The theoretical framework used in this research is described first since the theoretical background can heavily influence the selection of methods, how methods are used, and data interpretation. I will use the theoretical framework as a lens to observe and analyze the world. As described in the literature review, theories are important contributors of all our intellectual endeavors; therefore, they should be well-defined, whether they are well-constructed and publicly evaluated concepts or personal hunches, fears, or beliefs.

The methods rooted in that theoretical framework, data collection, analysis, and interpretation practices are presented. Since this research uses data from multiple sources, data triangulation and the validation of findings are described.

The ethical issues and protection of research participants and critical elements of research are described in the last section of this chapter.

The completion of this chapter will serve as the preparation for research activity and data collection.

Theoretical Framework

Importance of ontology and epistemology. Our personal, professional, and societal perceptions and interpretations of the world around us and our interpretation of the educational process have a crucial impact on our intellectual endeavors and research; therefore, knowing them may help interpret and evaluate findings (Cleland & Durning, 2015; Guba & Lincoln, 1994). That is especially important in the context of the Continuing Professional Development (CPD) of health-care professionals in the United States, where, as it is described in the literature review, the culture of quantitative, positivist research is very strong and where qualitative, interpretative, social research methodologies are perceived with suspicion. Positivists argue that educational research and social science should follow positivist methods used in the natural science and deliver “hard” quantitative data (Wellington, 2015).

Feminist research tradition. On the other hand, health-care professions that historically have the majority of their constituents as females (e.g., nurses) are arguably more prone to feminist research (Hall & Stevens, 1991) that promotes collaboration, equality, subjectivity, emancipation, and egalitarian qualitative research (Cohen, Manion, & Morrison, 2007). It is focused on the feelings and experiences of individuals in their unique social and historical context (Holloway & Wheeler, 2013).

Stereotypes. Arguably, that bipolarization is exacerbated by historic stereotypes where the medical profession is male dominated while nursing is a typically female occupation and where relationships between doctors and nurses were typically described as dominant-subservient relationships with man-woman stereotypes (Carpenter, 1993; Gjerberg & Kjølsrød, 2001; Vasey & Mitchell, 2015).

Influence of stereotypes. Although in recent decades, we can see a significant shift from that male-female norm, cultures of professions are still heavily influenced by those stereotypes (Braun, O’Sullivan, Dusch, Antrum, & Ascher, 2015). For example, Vasey and Mitchell (2015) note that not so long ago, in a teaching hospital theater room in 2008, it was stated, “There are only two types of women in surgery—those who shouldn’t be surgeons and those who shouldn’t be women.”

Clash of medical civilizations. That internal cultural and ontological debate is exacerbated with external forces. For example, in his paper on the clash of medical civilizations, (McKenna, 2012) argues that the neoliberal movement has converted the U.S. health-care system into a highly competitive business and has contributed to the creation of a hidden medical curriculum that promotes hierarchy and focuses solely on biomechanical elements of disease while discouraging students from social critique.

Ultimately, the contexts in which this research is conducted is marked by strong ontological and epistemological conflicts; therefore, a clear definition of points this research will take will ease the understanding of how data are analyzed.

Ontology. Social constructivism is my dominant worldview. It assumes that meanings are created in social interaction. They are constructed on individual and group/organizational/professional levels and influenced by numerous historical, cultural, and technological factors.

Epistemology. The social constructivism learning theory (Curran, Fleet, & Kirby, 2010; Vygotsky, 1978) is associated with that worldview. I will use it as a lens to analyze the potential challenges of Quality Improvement Education and Interprofessional Learning (QIE/IPL).

Social constructivism assumes that groups actively construct knowledge through social interaction internally among team members and as a team interacting with the external world. In that process, they create a group culture, a collection of shared artifacts and mental models. Ultimately, according to a social constructivist view, society exists simultaneously as subjective and objective reality (Andrews, 2012).

Phenomenography. The social constructivist worldview allows me to focus “on the participants’ views of the situation being studied” (Creswell, 2009). It suggests open-ended questioning to find out what people think or do in their daily lives. A phenomenographic approach is a good tool for that task, providing insight into the more subjective side of the world and insight on how the phenomena of QIE and IPL are preceded at this moment. Since QIE/IPL is a social construct happening in a very complex environment, that insight can help us understand the status, trends, and ways we can address those trends.

Case study. On the other hand, although IPL and QIE have become quite well-known concepts, most interviewees have not had a chance to practice them. They have not experienced them as something real and objective. To better understand the objective aspects of the phenomenon, the case study focused on how their professions and their professional associations are tackling that issue I used.

Methodology of Choice

The methodology of choice is a qualitative, interpretive, multiple-case case study (Yin, 2003) that encompasses phenomenographic analysis. Activity theory is used as a lens to analyze interrelations among multiple elements in this complex system.

Nesting Methods

Figure 1. Nested and interconnected elements of the research designed to address the complexity of health-care learning

Roles. Phenomenography will help me map the territory by analyzing how people perceive that phenomenon. The case study will help analyze the most important elements of the system and craft “the big picture.” Knowing that the system is extremely complex and very dynamic, with multiple stakeholders and multiple contradictions, it is essential to select a toolset to analyze relationships and processes in such a system. Complex metatheory and activity theory are selected as a match to that task. Complex metatheory will serve as a lamp that exposes multiple interconnected elements of this dynamic system, including interactions among different methods and methodologies (Bleakley & Cleland, 2015). Activity theory will, as a lens, help us analyze interactions among different elements of the system.

The image on the right illustrates how methods and theories are nested in this research.

Research design and learning questions. The interaction among those elements of research design will contribute to all three questions; however, it is fair to say that specific elements of research frameworks are associated with specific research questions. Those connections are the following:

• Phenomenography will mainly address question 1: How are QIE/IPL and technologies and policies that shape QIE/IPL perceived by four groups involved in perioperative teams: anesthesiologists, surgeons, anesthesiologist assistants, and nurse anesthetists?
• The case study has the dominant role to address question 2: How is technology-enhanced collaborative learning used and perceived in the context of QIE/IPL and perioperative teams?
• Activity and complexity theories provide a general context for the whole study and are essential tools for the last research question: How are professional cultures and contextual factors related to collaborative learning influencing the implementation of technology-enhanced QIE/IPL?

Case Study

Multiple-case design is the approach of choice (Yin, 2003). QIE/IPL-related practices and learning technology that can support QIE/IPL used by each profession are analyzed as independent cases. The four cases are

• the American Society of Anesthesiologists (ASA),
• the American Association of Nurse Anesthetists (AANA),
• the American College of Surgeons (ACS), and
• the American Academy of Physician Assistants (AAPA).

An alternative solution was to use a single case with embedded multiple units of analysis. That approach would be appropriate if we had collaboration and shared programs among two or more of the specialties in place. In that situation, the case would be shared QIE/IPL-related practices and learning technology that can support QIE/IPL of all professions together. Activities specific to each profession would be embedded units of analysis.

Since now I am not aware of a formal interprofessional collaboration on QIE/IPL, the QIE/IPL activities in each specialty were analyzed as separate entities.

The case study data sources were interviews with staff and physician members and a professional association’s website and published literature. Two interviews (nonphenomenographic) were scheduled with representatives of the three involved professions. The representatives of the American Association of Nurse Anesthetists were not able to participate. Phenomenographic interviews also served as a source of data over and above standard phenomenographic research.

Phenomenographic Analysis

Phenomenographic analysis focused on how CPD professionals, clinicians (anesthesiologists, nurse anesthetists, surgeons, and anesthesiologist assistants), and their respective leaders perceive QIE/IPL; and technology that supports those practices is the central part of the case study. Phenomenography appears to be the optimal method for this approach because although at this point QIE/IPL is in its early stage, it is a very hot topic, and human perceptions are the dominant factor.

The additional reasons are the following:

1. Attempts to implement QIE/IPL in the U.S. health-care system have a long but troubling history, and drivers influencing the implementation of QIE/IPL create a very complex picture. The phenomenographic approach is recognized as a good tool to analyze changes in such a complex system (Bunniss & Kelly, 2010; Stenfors‐Hayes, Hult, & Dahlgren, 2013).
2. Understanding the perceptions of groups involved in the learning and teaching process can enable us to address current and emerging challenges in that dynamic environment (Richardson, 2005).
3. QIE/IPL is ultimately a social endeavor (Hean, Craddock, & O’Halloran, 2009).

Phenomenography and medical education. During the past two decades, phenomenography proved to be very useful in medical education (Stenfors‐Hayes et al., 2013). It provides insight into the different ways that people perceive phenomena in the world around them and how those perceptions relate one to another (Marton, 1981; Marton & Booth, 1997); therefore, it can serve as a lens to analyze a specific research question and direct how research is carried out. In a medical setting, phenomenographic research is valuable for topics like clinical practice, communication, health-care learning, and especially the processes and outcomes of learning (Larsson & Holmström, 2007; Richardson, 1999). According to Stenfors‐Hayes et al. (2013), phenomenography can serve as a link among three important elements this research is tackling—research, organizational change, and educational development. That feature can be especially valuable in the context where, as the Macy (2013) expert team concluded, huge changes affecting the U.S. medical system are not linked effectively with changes affecting the CPD of health-care professionals in the United States.

Alternatives. Phenomenography was chosen over phenomenology because QIE/IPL is, in this context, an emerging concept; therefore, we can expect numerous, sometimes contradicting ways in which QIE/IPL is perceived (Larsson & Holmström, 2007). Furthermore, the research is interested in the differences of how the phenomena are perceived among four different professions, and phenomenography is an optimal tool for that task.

Two additional approaches that I considered but did not include as the first choice are action research and realist evaluation. As described below, I considered those methods as potential augmentation of the research framework, and they may help me address possible changes, for example, if the leadership of the ASA decided to start an interprofessional program during this research. While not used in this paper, the methodologies below can be very beneficial to the studies that may follow.

The action research method is a collaborative study focused on solving a problem through a cyclical and reflective process built around the following:

• Research and planning>>action>>collecting and analyzing evidence>>reflecting>>research and planning

Through those cycles, the researcher actively works with the participants to prepare and implement changes (Boet, Sharma, Goldman, & Reeves, 2012; Coghlan & Brannick, 2014). Focusing on QIE/IPL as an emerging concept, which the ASA has considered implementing, suggests that action research may be a method of choice.

On the other hand, the collaboration between the participants and the researcher is a potential source of political and ethical challenges affecting researchers and participants (Williamson & Prosser, 2002). Since the changes of CPD are described primarily as a political process (Balmer, 2013; Cervero & Moore Jr., 2011), using action research as a dominant methodology would be too risky an approach. It can affect the researcher and the participants, and political forces may stop what is needed for action research.

The realist method is a theory-driven approach used for the evaluation of complex social intervention in the field of health care (Marchal, van Belle, van Olmen, Hoerée, & Kegels, 2012). Some research asks simplistic questions like “Does IPL work?” The realist method attempts to answer if something works under specific social circumstances. It asks how and why something works. It attempts to dig deeper and find what works for whom, under what circumstances, and to what extent. It tries to determine how to improve or reduce the impact of what is being studied (Wong, Greenhalgh, Westhorp, & Pawson, 2012). The realist method may be useful for this research since the implementation of QIE/IPL is a very complex process influenced by numerous societal factors and affecting a variety of different stakeholders. On the other hand, per Wong et al., the realist method will be especially beneficial if we have the new intervention and a rich source of qualitative data in place. Hopefully, in a few years, we will have a wealth of data on QIE/IPL, but that is not the case now.

Complexity and Activity Theory

It is complex. For quite a long time, we have been rediscovering that the majority of learning for health-care professionals happens in the work environment and that it is socially constructed (Engeström, 2001; Fenwick, 2014). That is especially true for QIE and IPL; however, we have been experiencing challenges implementing learning designed for that context. Therefore, very often, our QIE and IPL attempts would result in retreat to the comfort zone of the traditional content-focused learning modalities. For example, the Accreditation Council for Continuing Medical Education annual report revealed that in 2015, only 0.7% of accredited learning activities were performance/quality improvement activities (ACCME, 2016). Arguably, the lack of tools that can help us analyze how health-care learning systems work is the reason why we are prone to forget the importance of workforce learning until the next research rediscovers that fact again.

Complexity and activity theories can help us address that challenge by providing insight on how learning embedded in a complex system works and how various factors of such a system interact.

Activity Theory

Lens to analyze complexity. Activity theory (AT, sometimes called cultural historical activity theory or CHAT) is the third element of the research framework. AT is a descriptive sociopsychological framework taking into account all elements of a complex activity/work system (Johnston & Dornan, 2015). It explains divisions between the material and the mental, history and present, theory and praxis, and—for interprofessional education, especially troubling issue—the individual and the group (Stetsenko, Arievitch, & Blunden, 2014). Examples of such an activity system may be teams like a perioperative surgical home team or organizations such as the ASA. AT can help us analyze interactions among professionals in the system—in our case, that can be doctors, nurses, and patients—and their learning shaped by interpersonal, cultural, economic, political, and historical aspects (Foot, 2014); therefore, activity theory can serve as a lens to analyze collective, culturally mediated, and object-oriented human activities in such a complex and dynamic system (Barab, Evans, & Baek, 2004; Jonassen & Rohrer-Murphy, 1999).

Activity theory is probably the most complicated, and for some readers, it can be the most abstract tool in my research toolset; therefore, I will describe it in more detail.

Under the umbrella of complexity. With communities of practices and actor-network theory, activity theory is nested under the umbrella of complexity, a metamethodology described below (Bleakley & Cleland, 2015; Jonassen & Land, 2012).

Ontology. Activity theory, same as phenomenography, rejects positivist approach and Cartesian dualism. Cartesian dualism is the idea that our body and our mind are two separate entities and that our mind can objectively analyze everything happening in the “real world” without being affected by real-world activities (Baker & Morris, 2005). AT is rooted in the idea that our perception of the world is interwoven with our physical existence in it; therefore, AT provides a map to understand the main drivers that may influence our interaction with and perception of the world (Roth & Lee, 2007), a map to help us understand processes, individuals, and teams used to manage change and learning in daily (clinical) practice (Engeström, 2001).

History. Activity theory has had a dynamic and troubling evolution. It is rooted in Vygotsky’s sociocultural psychology (Verenikina, 2010) and Marxist’s praxis-focused dialectical materialism (DeVane & Squire, 2012). It was crafted during the 1920s and the early 1930s in Soviet Russia. Dialectical materialism assumes that progress is built through a clash between opposites (Spirkin, 1983). Sociocultural psychology/theory explains that teaching and learning (and QI projects) are embedded in the cultural and historical context of learners’ daily practices and intimately connected with the way learners interact with peers, teachers, and society.

Political context – complex network society. The Western opinion of early Soviet Russia is usually based on recollections of Cold War and the relatively small economical, intellectual, and cultural impact socialistic Russia had on the world. Early Soviet Russia, located between the dictatorship of the czar and the dictatorship of the Communist Party led by Lenin, may give a different picture. During the 1920s, Russia seemed to be a place of significant intellectual production (Johnston & Dornan, 2015). The country was going through massive and complex reforms: the imperial political structures crashed and they were replaced informal personal networks (Easter, 1996); the belief that the new system will be better that the old monarchy was crushed by failed reforms, subsequent famines that took many millions of lives (Brooks & Gardner, 2004), and the rising, cruel dictatorship of the new government. Arguably, the country was well networked and on the edge of chaos; and that, as explained under complexity theory, created a great context for innovation. One important innovation initiated during that time is AT.

Suppression in SSSR. During early postrevolutionary Soviet Russia, the first generation of activity theory was created by Vygotsky and collaborators. Because of the Cold War and the suppression of dialectical materialist psychology in SSSR (Bickley, 1977), AT was not well known to the West until the 1980s (Engeström, Miettinen, & Punamäki, 1999).

The development of activity theory went through three generations. Each generation created a more complex extension of the previous one. Each generation has a bigger, more inclusive, and more complex unit of analysis; therefore, each generation is worth mentioning for a better understanding of AT.

The first generation of activity theory is focused on the interaction between individuals and the world. That interaction is never direct. We need psychological tools to communicate our thoughts and/or technical tools to physically communicate with the world. The most common psychological tool is language. Technical tools are physical artifacts we use in our daily life. The pen, the fork, and the scalpel are just a few examples. We use those tools to achieve a specific objective. Therefore, the map of the first generation of AT looks like this (Engeström et al., 1999):

Image 2: First generation of AT

The second generation of AT extends a notion of context by adding rules, division or labor, and community to the picture. Those are integral elements of each activity system, and they may have a crucial impact on how we interact with the world. The second generation of the AT map is presented in the image below. The lines with arrowheads symbolize connections and contradictions, which make the system dynamic and create a fuel for progress.

Image 3. The second generation of AT

The second generation of AT can be used, for example, to analyze how an operation room team interacts in the attempt to achieve, for example, object: patient safety. The inherited limitation in that analysis may be the differences among groups in that team (e.g., surgery, anesthesia, management); each one is with their own unique culture, and rules and roles are not recognized. That limitation is addressed in the third-generation AT.

The third generation of activity theory is specifically interesting for this research because it is focused on how different activity systems interact (Engeström, 2001). Each profession (anesthesiologists, nurse anesthetists, surgeons, etc.) and patient or the public can be analyzed as a separate activity system. The third generation of activity theory can help us understand how those systems interact during work or QIE/IPL activities.

AT and health care. Activity theory is often used to analyze complex interactions among and inside health-care activity systems (Bardram & Doryab, 2011; de Feijter, de Grave, Dornan, Koopmans, & Scherpbier, 2011; Engestrom, 2000; Skipper, Musaeus, & Nøhr, 2016). That link between activity theory and health care was noticeable since the early beginning. For example, the paper introducing the third generation of activity theory (Engeström, 2001) uses interaction among health-care activity systems (hospital, patient’s family) as the main example.

An example of the third-generation AT diagram is presented below:

Figure. Two interacting activity systems are the minimal model for the third generation of activity theory (Source: Engeström, 2001). Each profession can be analyzed as a separate activity system. The outcomes (Object₂) of each profession interact, creating the outcome of collaboration—Object₃.

In our perioperative model, the interaction between the patient’s anesthesiology and surgery activity systems may look like this:

Figure. The operating room as an activity system for acute patient care (Adaptation of: Engestrom, 2000; Kerosuo, Kajamaa, & Engeström, 2010)

AT is a system-based design. Instead of being a predictive theory, activity theory can serve better as metatheory or a framework we can use to understand cultural and historical aspects of relations in complex social systems (Iivari & Linger, 1999). Since it is focused on activity systems, a concept of collective and socially and object-mediated human activity, AT can bridge the gap between individual actors and very complex, socially constructed, and technology-enhanced reality. For example, AT has proven to be a powerful tool for researching how people adapt and learn in the workplace (Engeström, 2001; Engeström, Virkkunen, Helle, Pihlaja, & Poikela, 1996).

Expansive learning. Finally, the last important element of AT is expansive learning. Expansive learning theory was created as an application of activity theory to showcase how workforce learning and innovation arise. AT showcases multiple voices, multiple drivers, and multiple contradictions among them. When those voices and contradictions merge and create something new, innovatively, that is called expansive learning (Engeström, 1987). In the other words, expansive learning is a “constant comparative process or adaptation” (Johnston & Dornan, 2015).
Challenges and opportunities of expansive learning. While expansive learning as a complex model may be hard to grasp and may not be the best solution for industrial, standardized learning products delivery (Hean et al., 2009), it serves as a promising model to explain and improve learning in complex systems such as health care. We are well aware that in an increasingly complex world, our attempts to have complete control or chunk our world into smaller, isolated, “manageable” entities play a central cause of numerous failures (Dorner, Nixon, & Rosen, 1990).

Change laboratory. To address the challenges of learning in complex systems, Virkkunen and Newnham (2013) have created The Change Laboratory: A Tool for Collaborative Development of Work and Education. The Change Laboratory method is built on AT and the theory of expansive learning. In this model, the outcomes are developed by participants while they create expansive answers to contradictions (read: QI challenges) in their activity system (read: local context). That is significantly different from the traditional learning development approach, whereby teachers or instructional designers create a learning activity for learning objectives they have selected and activities happen in time and space separated from daily practice.

Complexity Theory

The big, final piece of methodology is complexity theory. It serves as a metatheory that provides insight on how multiple dynamic elements of a complex system interact.

About complex health-care education, the very formalized word of surgery and perioperative medicine and its cousin, the world of medical education is complex (Bleakley & Cleland, 2015; Plsek & Wilson, 2001). It is created as a dynamic network of multiple teams—anesthetic, surgical, laboratory, medical technology, radiology, pathology, management, and physiotherapy—shaped by five dominant cultures. In addition to management, education, and research, the three cultures described by Bleakley and Cleland, the cultures of health-care professions and QI play significant roles. An addition to that very dynamic complex is the empowered patients with their beliefs, knowledge (or lack of knowledge), and expectations (Wald, Dube, & Anthony, 2007). Therefore, to make a positive and sustainable change in that system, we must understand how complexity works.

Complexity has two important sides. It is harder to analyze it, but it can be very beneficial for innovation.

It is complex. From one side, analyzing and managing complex systems is much harder than analyzing simple or complicated systems. That is why when we have a choice between a complex system and a simple or complicated system, our preference will go toward a simpler one (image below). Senge (2006, p. 73) explains it: “The reality is made of circles, but we see straight lines.” In other words, reality is complex, but we see only a simple and complicated process. Very often we will deconstruct a complex system on a set of simple processes and attempt to analyze those processes as separate entities. While on short run, those attempts may provide valuable information, at the end we may understand elements of the system, but we do not understand the system. Fortunately, as described in this chapter, we have tools to analyze complex systems; therefore, we can use Prochaska’s words: “The days of searching for simple solutions to complex problems should be behind us” (Prochaska & DiClemente, 1986, p. 4).

Human preferences. Illustration by Wiley Miller (used with permission)

Complexity and innovation. On the other hand, complexity provides a framework for innovation. Complex systems, especially highly complex systems on the edge of chaos, have the highest potential for creativity. Looking through the lens of activity theory, dynamic complex systems have the highest amount of contradictions, which drive change and innovation. Kauffman (1996) convincingly argues that the edge of chaos, a space between order and complete randomness, is a context where serendipity has the highest potential. The same context is forcing us to critically analyze our actions and impact on the world around us, increasing our potential for learning and discovery. That explains why some of the most valuable contributions Russia made to the world’s science and culture (especially avant-garde) happened during 1920 (Von Hagen, 1996).

Treating complex as complicated. On the other hand, we usually experience challenges in that innovative side of complexity. Glouberman and Zimmerman (2002) use examples of Brazil, France, and Canada health-care systems to point out that health-care systems are complex; therefore, emerging health-care challenges should be treated as complex issues. Unfortunately, most health-care fixes perceive health-care systems as merely complicated systems. Thus, those complicated interventions serve primarily as a waste of time, opportunity, and resources and contribute to the deterioration of health care.

A vivid example and scale of such a misconception was given by Mr. Donald Trump on February 24, 2017, when he noted, “Nobody knew that health care could be so complicated” (Howell, 2017). That showcases that after seven years of intensive debate over U.S. health care, part of policymakers and the person who acts as the president of the United States perceive health care merely as a complicated system. Consecutive to that misconception, the American Health Care Act of 2017, a U.S. Congress bill aimed to replace the Patient Protection and Affordable Care Act (ACA) known as Obamacare, failed to deliver on some of the main promises made by the administration. Therefore, the U.S. Congressional Budget Office estimated that instead of “insurance for everybody” (Costa & Goldstein, 2017), the bill promises twenty-four million more uninsured Americans by 2016 (CBO, 2017); instead of lower health-care costs, insurance can rise significantly, especially for older Americans (Sarlin, 2017). As result, the law did not pass in its original form.

The drive back to the simple zone. The image below illustrates that drive from complexity to a “simple, evidence-based zone” (O’Riordan et al., 2011). Very often, we end up in the evidence-based zone simply by neglecting complexity and ignoring everything that is not certain or generally accepted. In a stage of high uncertainty like the U.S. health-care system is experiencing recently, the drive to go to a safe zone can be much stronger, blocking the possibility to make the needed change (Engel, 1997).

Figure. Stacey matrix (adopted from O’Riordan et al. (2011)

Fortunately, the forces pushing in the other direction toward complexity are becoming stronger and better supported by science.

The evolution of dominant epistemologies has contributed to the increased awareness of learning complexities. Traditional learning theories like behaviorism and cognitivism were focused on the individual student, usually perceived as a completely autonomous entity separated from the rest of the world and her possibility to “absorb” knowledge (Siemens, 2005). Learning was perceived as a simple, longitudinal, and content transmission (Jonassen & Land, 2012). During the last four decades, our perception of learning has transformed. Now dominant constructivist and sociocultural learning theories perceive learning as an active, social process where, instead of knowledge reproduction, students collaborate on knowledge production, helping one another access and evaluate distributed knowledge. This is a significantly more complex process that involves socialization, identity formation (Cruess, Cruess, & Steinert, 2015), and (for CME/CPD context) new course delivery formats (Curran et al., 2010).

Personal and professional epistemologies are an important element of the complexity mosaic. They are processes in which individuals and groups do or do not construct knowledge from learning experiences. Those epistemologies are shaped by learners’ ambitions, interests, capacities, identities, and social structures (Billett, 2009). Because of significant variations of personal and group epistemologies, the impact on a learning event, as a society-constructed activity, can significantly vary.

References

1. ACCME. (2016). Accreditation Council for Continuing Medical Education (ACCME®) 2015 Annual Report. Retrieved from Chicgo:
2. Andrews, T. (2012). What is social constructionism? The grounded theory review, 11(1), 39-46.
3. Baker, G. P., & Morris, K. J. (2005). Descartes’ Dualism (3 ed.). Great Britan            Routledge.
4. Balmer, J. T. (2013). The transformation of continuing medical education (CME) in the United States. Advances in medical education and practice, 4, 171.
5. Barab, S. A., Evans, M. A., & Baek, E.-O. (2004). Activity theory as a lens for characterizing the participatory unit. Handbook of research on educational communications and technology, 2, 199-213.
6. Bardram, J., & Doryab, A. (2011). Activity analysis: applying activity theory to analyze complex work in hospitals. Paper presented at the Proceedings of the ACM 2011 conference on Computer supported cooperative work.
7. Bickley, R. (1977). Vygotsky’s contributions to a dialectical materialist psychology. Science & Society, 191-207.
8. Billett, S. (2009). Personal epistemologies, work and learning. Educational Research Review, 4(3), 210-219.
9. Bleakley, A., & Cleland, J. (2015). Sticking with messy realities: how ‘thinking with complexity’can inform healthcare education research. In J. Cleland & S. J. Durning (Eds.), Researching Medical Education (Vol. 1, pp. 81-92). Chichester, West Sussex, PO198SQ, UK E: John Wiley & Sons,.
10. Boet, S., Sharma, S., Goldman, J., & Reeves, S. (2012). Review article: medical education research: an overview of methods. Canadian Journal of Anesthesia/Journal canadien d’anesthésie, 59(2), 159-170.
11. Braun, H. J., O’Sullivan, P. S., Dusch, M. N., Antrum, S., & Ascher, N. L. (2015). Improving interprofessional collaboration: Evaluation of implicit attitudes in the surgeon – nurse relationship. International Journal of Surgery, 13, 175-179. doi:10.1016/j.ijsu.2014.11.032
12. Brooks, K., & Gardner, B. (2004). Russian agriculture in the transition to a market economy. Economic Development and Cultural Change, 52(3), 571-586.
13. Bunniss, S., & Kelly, D. R. (2010). Research paradigms in medical education research. Medical Education, 44(4), 358-366.
14. Carpenter, M. (1993). The subordination of nurses in health care: towards a social divisions approach. Gender, work and medicine, 95-130.
15. CBO, The Congressional Budget Office. (2017). CONGRESSIONAL BUDGET OFFICE,  COST ESTIMATE – American Health Care Act. Retrieved from CBO.gov:
16. Cervero, R. M., & Moore Jr., D. E. (2011). The Cease Smoking Today (CS2day) initiative: A guide to pursue the 2010 IOM report vision for CPD. Journal of Continuing Education in the Health Professions, 31(S1), S76-S82.
17. Cleland, J., & Durning, S. J. (2015). Researching medical education. The Association for the Study of Medical Education: John Wiley & Sons.
18. Coghlan, D., & Brannick, T. (2014). Doing action research in your own organization: Sage.
19. Cohen, L., Manion, L., & Morrison, K. (2007). Research Methods in Education (6th edition). New York: Routledge.
20. Costa, R., & Goldstein, A. (2017). Trump vows ‘insurance for everybody’ in Obamacare replacement plan. The Washington Post Politics.  Retrieved from https://www.washingtonpost.com/politics/trump-vows-insurance-for-everybody-in-obamacare-replacement-plan/2017/01/15/5f2b1e18-db5d-11e6-ad42-f3375f271c9c_story.html?utm_term=.fd946a176f7a
21. Creswell, J. W. (2009). Research design: Qualitative, Quantitative, and mixed methods approaches. London: SAGE.
22. Cruess, R. L., Cruess, S. R., & Steinert, Y. (2015). Amending Miller’s Pyramid to Include Professional Identity Formation. Academic medicine: journal of the Association of American Medical Colleges.
23. Curran, V., Fleet, L., & Kirby, F. (2010). A comparative evaluation of the effect of internet-based CME delivery format on satisfaction, knowledge and confidence. BMC Medical Education, 10(1), 10.
24. de Feijter, J. M., de Grave, W. S., Dornan, T., Koopmans, R. P., & Scherpbier, A. J. (2011). Students’ perceptions of patient safety during the transition from undergraduate to postgraduate training: an activity theory analysis. Advances in health sciences education, 16(3), 347-358.
25. DeVane, B., & Squire, K. D. (2012). Activity Theory in the Learning Technologies. In D. Jonassen & S. Land (Eds.), Theoretical foundations of learning environments (Second ed., pp. 269–296). New York, NY: Routledge.
26. Dorner, D., Nixon, P., & Rosen, S. (1990). The Logic of Failure [and Discussion]. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 327(1241), 463-473.
27. Easter, G. M. (1996). Personal networks and postrevolutionary state building: Soviet Russia reexamined. World Politics, 48(04), 551-578.
28. Engel, P. G. H. (1997). The Social Organization of Innovation: A Focus on Stakeholder Interaction: Royal Tropical Institute.
29. Engestrom, Y. (2000). Activity theory as a framework for analyzing and redesigning work. Ergonomics, 43(7), 960-974.
30. Engeström, Y. (1987). Learning by Expanding: An Activity-theoretical Approach to Developmental Research: Orienta-Konsultit Oy.
31. Engeström, Y. (2001). Expansive learning at work: Toward an activity theoretical reconceptualization. Journal of education and work, 14(1), 133-156.
32. Engeström, Y., Miettinen, R., & Punamäki, R. L. (1999). Perspectives on Activity Theory: Cambridge University Press.
33. Engeström, Y., Virkkunen, J., Helle, M., Pihlaja, J., & Poikela, R. (1996). The change laboratory as a tool for transforming work. Lifelong Learning in Europe, 1(2), 10-17.
34. Fenwick, T. (2014). Sociomateriality in medical practice and learning: attuning to what matters. Medical Education, 48(1), 44-52.
35. Foot, K. A. (2014). Cultural-historical activity theory: Exploring a theory to inform practice and research. Journal of Human Behavior in the Social Environment, 24(3), 329-347.
36. Gjerberg, E., & Kjølsrød, L. (2001). The doctor–nurse relationship: how easy is it to be a female doctor co-operating with a female nurse? Social Science & Medicine, 52(2), 189-202.
37. Glouberman, S., & Zimmerman, B. (2002). Complicated and complex systems: what would successful reform of Medicare look like? Retrieved from
38. Guba, E. G., & Lincoln, Y. S. (1994). Competing paradigms in qualitative research (3 ed. Vol. 2). CA, USA: Sage.
39. Hall, J. M., & Stevens, P. E. (1991). Rigor in feminist research. Advances in Nursing Science, 13(3), 16-29.
40. Hean, S., Craddock, D., & O’Halloran, C. (2009). Learning theories and interprofessional education: A user’s guide. Learning in Health and Social Care, 8(4), 250-262.
41. Holloway, I., & Wheeler, S. (2013). Qualitative Research in Nursing and Healthcare: Wiley.
42. Howell, T. (2017). Trump: ‘Nobody knew that health care could be so complicated’. The Washington Times.
43. Iivari, J., & Linger, H. (1999). Knowledge work as collaborative work: A situated activity theory view. Paper presented at the Systems Sciences, 1999. HICSS-32. Proceedings of the 32nd Annual Hawaii International Conference on.
44. Johnston, J., & Dornan, T. (2015). Activity theory: mediating research in medical education. In J. Cleland & S. J. Durning (Eds.), Researching Medical Education (pp. 93-104). The Association for the Study of Medical Education: John Wiley & Sons.
45. Jonassen, D. H., & Land, S. (2012). Theoretical foundations of learning environments (Vol. 2). New York, NY 10017: Routledge.
46. Jonassen, D. H., & Rohrer-Murphy, L. (1999). Activity theory as a framework for designing constructivist learning environments. Educational Technology Research and Development, 47(1), 61-79.
47. Kauffman, S. (1996). At home in the universe: The search for the laws of self-organization and complexity: Oxford university press.
48. Kerosuo, H., Kajamaa, A., & Engeström, Y. (2010). Promoting innovation and learning through change laboratory: an example from Finnish health care. Central European Journal of Public Policy, 4(1), 110-131.
49. Larsson, J., & Holmström, I. (2007). Phenomenographic or phenomenological analysis: Does it matter? Examples from a study on anaesthesiologists’ work. International Journal On Qualitative Studies On Health And Well-being, 2(1), 55-64. doi:10.1080/17482620601068105
50. Macy, Josiah Macy Jr. Foundation. (2013). Transforming Patient Care:  Aligning Interprofessional Education with Clinical Practice Redesign. Paper presented at the Macy Conference on Transforming Patient Care: Aligning Interprofessional Education with Clinical Practice Redesign, January 2013.
51. Marchal, B., van Belle, S., van Olmen, J., Hoerée, T., & Kegels, G. (2012). Is realist evaluation keeping its promise? A review of published empirical studies in the field of health systems research. Evaluation, 18(2), 192-212.
52. Marton, F. (1981). Phenomenography — describing conceptions of the world around us. Instructional science, 10(2), 177-200.
53. Marton, F., & Booth, S. (1997). Learning and awareness. Mahwah, NJ, US: Lawrence Erlbaum Associates, Publishers.
54. McKenna, B. (2012). The Clash of Medical Civilizations: Experiencing “Primary Care” in a Neoliberal Culture. Journal of Medical Humanities, 33(4), 255-272. doi:10.1007/s10912-012-9184-6
55. O’Riordan, M., Dahinden, A., Aktürk, Z., Ortiz, J. M. B., Dağdeviren, N., Elwyn, G., . . . Struk, P. (2011). Dealing with uncertainty in general practice: an essential skill for the general practitioner. Quality in primary care, 19(3), 175-181.
56. Plsek, P. E., & Wilson, T. (2001). Complexity, leadership, and management in healthcare organisations. BMJ: British Medical Journal, 323(7315), 746.
57. Prochaska, J. O., & DiClemente, C. C. (1986). Toward a comprehensive model of change Treating addictive behaviors (pp. 3-27): Springer.
58. Richardson, J. T. E. (1999). The concepts and methods of phenomenographic research. Review of Educational Research, 69(1), 53-82.
59. Richardson, J. T. E. (2005). Students’ approaches to learning and teachers’ approaches to teaching in higher education. Educational Psychology, 25(6), 673-680.
60. Roth, W.-M., & Lee, Y.-J. (2007). “Vygotsky’s neglected legacy”: Cultural-historical activity theory. Review of Educational Research, 77(2), 186-232.
61. Sarlin, B. (2017). Experts: The GOP Health Care Plan Just Won’t Work. POLITICS  > CONGRESS.  Retrieved from http://www.nbcnews.com/politics/congress/experts-gop-health-care-plan-just-won-t-work-n730361
62. Senge, P. M. (2006). The Fifth Discipline: The Art & Practice of the Learning Organization. USA: Doubleday.
63. Siemens, G. (2005). Connectivism: A learning theory for the digital age. International Journal of Instructional Technology and Distance Learning, 2(1).
64. Skipper, M., Musaeus, P., & Nøhr, S. B. (2016). The paediatric change laboratory: optimising postgraduate learning in the outpatient clinic. BMC Medical Education, 16, 42. doi:10.1186/s12909-016-0563-y
65. Spirkin, A. G. (1983). Dialectical Materialism: Progress Publishers.
66. Stenfors‐Hayes, T., Hult, H., & Dahlgren, M. A. (2013). A phenomenographic approach to research in medical education. Medical Education, 47(3), 261-270.
67. Stetsenko, A., Arievitch, I., & Blunden, A. (2014). Vygotskian collaborative project of social transformation: History, politics, and practice in knowledge construction. In A. Blunden (Ed.), Collaborafive projects: An interdisciplinary study (pp. 217-238). Boston, USA: Brill.
68. Vasey, C. E., & Mitchell, R. A. (2015). Gender perceptions in surgery: is it really a level playing field? ANZ journal of surgery, 85(12), 898-901.
69. Verenikina, I. M. (2010). Vygotsky in twenty-first-century research. Paper presented at the Proceedings of World Conference on Educational Multimedia, Hypermedia and Telecommunications, Chesapeake, VA: AACE. http://ro.uow.edu.au/edupapers/1022/
70. Virkkunen, J., & Newnham, D. S. (2013). The Change Laboratory: A tool for collaborative development of work and education: Sense Publishers.
71. Von Hagen, M. (1996). Toward a cultural and intellectual history of Soviet Russia in the 1920s: some preliminary directions for a reevaluation of politics and culture. Revue des études slaves, 68(2), 283-302.
72. Vygotsky, L. S. (1978). Mind and society: The development of higher mental processes: Cambridge, MA: Harvard University Press.
73. Wald, H. S., Dube, C. E., & Anthony, D. C. (2007). Untangling the Web—The impact of Internet use on health care and the physician–patient relationship. Patient education and counseling, 68(3), 218-224.
74. Wellington, J. (2015). Educational research: Contemporary issues and practical approaches: Bloomsbury Publishing.
75. Williamson, G. R., & Prosser, S. (2002). Action research: politics, ethics and participation. Journal of Advanced Nursing, 40(5), 587-593.
76. Wong, G., Greenhalgh, T., Westhorp, G., & Pawson, R. (2012). Realist methods in medical education research: what are they and what can they contribute? Medical Education, 46(1), 89-96.
77. Yin, R. K. (2003). Designing case studies. In R. K. Yin (Ed.), Case study research: design and method (pp. 19-56). London: Sage.