There are several matters that suggest the use of a configurational comparative method to research the complexity of safety climate and views of reporting and disclosure of medication error. Firstly, it has been identified in the previous chapter that the health system is undergoing constant change (Section 2.3). That is, it is not a static system. Secondly, the nature of the relationship between safety climate, reporting and disclosure is complex with differences found amongst different workplace settings and work roles (Section 2.16).
Complexity science recognises each of these. There is an acknowledgment that systems are dynamic and changing (Lewin, 1999; Mitchell, 2009). There is also recognition that organisations adapt to the constantly changing environment (Dekker, 2011; McMillan, 2008). Some of these complex adaptive systems perform at a high level within that complexity and are resilient to change (Hollnagel, 2014). Patient safety, including medication error, is a serious problem for the health system (Australian Commission for Safety and Quality in Health Care, 2013b). However, the majority of care that consumers or patients receive is safely delivered without incident or harm (Hollnagel et al., 2013). In order to better manage safety and ensure safe patient care it is necessary to improve understanding of episodes of safe care.
Therefore to achieve the aim of this research which is to describe the complexity of safety climate of nurses working in rural clinical settings, the use of a complexity science framework is more suitable than one based upon cause-effect science. Configurational methods assist in the identification of causal complexity
(conjunctural causality) (Ragin, 1987; Schneider & Wagemann, 2012). They also recognise more than one causal pathway may be present for a specific outcome. Thus, use of a configurational comparative method would be beneficial for this
81 research through informing the research aim and contributing to knowledge of the complex health system through providing sense-making of how safety climate is related to views of reporting and disclosure of error.
Development of 4th and 5th research sub-questions
It has been noted that simple/complicated approaches to research assist in identifying difference but do not explain them (Ragin & Amoroso, 2011). For this research method acknowledging complexity is required and this may be addressed by the use of CCM. Therefore, CCM may assist in informing the main research question of how is safety climate related to views of reporting and disclosure of medication error amongst nurses working in rural clinical settings?
What is not so clear is how this may be achieved. Therefore a fourth research sub- question for this research is: how is the understanding of the relationship between workplace safety climate and views of reporting medication error changed through the use of a configurational comparative method? That is, if a CCM approach is used for this research there is a need to identify how it contributes to generating knowledge of the complex health care system.
This research sub-question assumes use of CCM will lead to a change of understanding through this contribution to knowledge. Once this change is identified there are implications in relation to the main research question. That is, if the understanding of how safety climate is related to nurses’ views of reporting and disclosure of medication error is changed then there may be implication for both the management of medication as well as medication error. Therefore a fifth research sub-question for this research is: what could this mean for the management of medication error?
With the research aim, question and sub-questions established, the following chapter will provide more detail of how a configurational comparative method has been used for this research.
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4
Research design and method
Safety and error are complex areas. Organisations are complex in terms of how they operate hence understanding what should be done to ensure that safe patient care occurs is difficult to research. However, there is still much that is unknown about how complex organisations work.
It has been suggested that a shift in focus is needed away from concentrating on what has gone wrong in a few cases (Safety I) to what has gone right in most cases (Safety II) (Hollnagel, 2014). This means a greater focus upon understanding how some organisations develop the capacity to ensure what should happen does. Those that achieve this are regarded as resilient.
Complexity science is emerging as a paradigm for a better understanding of how organisations work in respect to organisational culture and safety. Rather than focusing on evidence, complexity science acknowledges the need for sense-making
and recognises that different things work in different settings and situations (Martin & Félix-Bortolotti, 2010).
The aim of this research is to describe the complexity of safety climate of nurses working in rural clinical settings. This aim was developed through examination of the literature in relation to safety climate and error reporting and disclosure, and will be informed through the research question which is how is safety climate related to views of reporting and disclosure of medication error amongst nurses working in rural clinical settings?
The notion of complexity is central to CCM and therefore such methods offer an innovative means for the future research of organisational complexity. Through case-based analysis of sub-set relations it is possible to consider a variety of conditions and their impact on a particular outcome(Ragin, 1987; Rihoux & Ragin, 2009; Schneider & Wagemann, 2012). In using this approach it is possible to identify
84 more than one set of conditions that may lead to an outcome, or several similar sets of conditions that may lead to very different outcomes which reflects the key
concepts of conjunctural causality and equifinality (Schneider & Wagemann, 2012). There is a growing number of studies where a CCM research design has been applied (Rihoux & Marx, 2013; Thiem & Dusa, 2013). This includes publications of applications of a CCM in high ranking journals,including one recently (Trujillo & Woulfin, 2014).
The potential for use of CCM in researching the complexity in health care was introduced in the previous chapter. For this research it has been used for the
summarising of data as well as for theory development (Berg-Schlosser et al., 2009). Further details about the specific research design that has been applied to this research and the methods of analysis will now be outlined in in this chapter. The research design is based upon the funnel of complexity which outlines three phases that should be undertaken when applying a CCM research design (Rihoux & Lobe, 2009). Detail of this how this process has been applied to this research is presented in Figure 4-1.
The first of these phases is before the analytic moment where theory and case knowledge informs the research (Rihoux & Lobe, 2009; Schneider & Wagemann, 2010). The detail of the research design, data collection and preparation of research data for analysis using a CCM will also be presented in this section. This includes the selection of cases as well as the conditions and outcomes of interest for the
research.
It is recommended that another form of analysis be used alongside CCM (Schneider & Wagemann, 2010). Therefore a variable-based analysis using principal
components analysis (PCA) and inferential statistics was undertaken. Use of each of these approaches assisted in addressing the first three research sub-questions with the former also adopted as a means of determining the conditions for analysis using
85 fsQCA. More detail of how these analyses were used will also be outlined in this chapter.
Figure 4-1:Funnel of complexity as applied in this research (adapted from Rihoux & Lobe, 2009) Once this has been presented the second phase during the analytic moment will be outlined (Rihoux & Lobe, 2009). Analysis of case-based data will be explained
including the preparation of data through calibration of sets, use of Boolean algebra and software, and production of solution terms (Berg-Schlosser & De Meur, 2009; Schneider & Wagemann, 2010, 2012). Definitions and explanations of these and other key terms for analysis of necessary and sufficient conditions, the production of a truth table, discussion of truth table analysis and handling of contradictions and
86 This research involves the comparison of several different outcomes. Outlined in this chapter is the consistent and transparent approach to analysis that has been developed to allow comparison between these different outcomes.
Following the analysis the third phase after the analytic moment occurs. During this phase results from the analysis will be presented and discussed in terms of how they inform existing case and theoretical knowledge. This final phase will be mostly undertaken in the following chapters, however an outline of how results will be presented is provided at the end of this chapter.
The recommended set of standards of good practice have also been used to inform this research (Schneider & Wagemann, 2010). These provide a guide to the
researcher as to how to undertake configurational methods.
Theoretical knowledge has underpinned the aims of this research. The research sub- questions also reflect both theoretical and case knowledge and inform the
outcome, conditions and cases that will be used for analysis using fuzzy set
qualitative comparative analysis (fsQCA). Before the research design is discussed an overview of analysis with CCM is required.
Overview of CCM analysis
Configurational comparative methods are used to research the complexity of configurations of conditions of interest and the presence or absence of a particular outcome of interest (Berg-Schlosser et al., 2009; Ragin, 1987, 1992; Ragin &
Amoroso, 2011; Schneider & Wagemann, 2012). These approaches have their foundations in fuzzy set theory and are also referred to as set-theoretic as they allow for the study of sub-set relations (Berg-Schlosser & De Meur, 2009; Schneider & Wagemann, 2012). An outcome is the main focus of a study, the variable to be explained by conditions (Rihoux & Ragin, 2009) or the phenomenon of interest (Schneider & Wagemann, 2012). A condition is described as something that may affect an outcome (Rihoux & Ragin, 2009) or may explain it (Schneider &
87 That is, within a given data set there are two sub-sets. One is the set of cases where the outcome of interest is present and the other is the set of cases where the outcome is not present. The cases in each of these sub-sets are studied with consideration as to whether configurations of conditions of interest are present or absent.
There are different forms of CCM. The two most common are crisp set qualitative comparative analysis (csQCA) and fuzzy set qualitative comparative analysis (fsQCA) (Rihoux & Marx, 2013; Thiem & Dusa, 2013). The detail of how each has been applied in relation to this research appears later in this chapter. First, it is important consider the different approaches to analysis in order to assist in understanding the nature of CCM research design.
To undertake an analysis using csQCA or fsQCA data needs to be transformed or
calibrated (Ragin, 2009; Schneider & Wagemann, 2010, 2012). Each of these methods of analysis differ in approaches to calibration and this will be explained here.
Crisp set QCA (csQCA) is the approach used when the set membership of the conditions and outcome are dichotomised (Marx, Cambre, & Rihoux, 2013; Rihoux & De Meur, 2009) That is, it is determined that the cases are contained in the set of the condition or outcome (fully in) or they are not (fully out). The terminology used to indicate each is True or 1 for fully in and False or 0 for fully out. Fuzzy set QCA (fsQCA) allocates cases to set membership by degrees rather than by
dichotomisation (Greckhamer et al., 2013; Ragin, 2009; Schneider & Wagemann, 2012). Hence, rather than be classified “fully in” or “fully out” the classification can be by degrees on a range from 0 (fully out) to 1 (fully in). Sets are thus calibrated to reflect the degrees. A crossover point is set as the point at which a condition or outcome is considered neither in nor out of the set. Thus cases can then be
assigned set membership by degrees. This point, which is within the range of 0—1, is set as 0.5 (Ragin, 2009; Schneider & Wagemann, 2012).
88 This may seem somewhat confusing when explained in isolation but applying it to an example assists in understanding. If it was necessary to determine set
membership of the set Red Squares, then a red square may be considered fully in (1) and a blue circle fully out (0). If there were four cases present, these being a red square, a blue square, a red circle and a blue circle, then a red square could easily be assigned as fully in (1) and any other cases present, regardless of shape or colour would be assigned fully out (0).
The approach taken with csQCA only allows for the choice of considering each case as either fully in (1) or fully out (0) (Ragin, 2009; Schneider & Wagemann, 2012). By adopting an fsQCA approach it is possible to consider that cases may be partially in
a given set.
Thus, in the example of the set of Red Squares, if a researcher was more interested in cases based upon shape rather than colour then for the condition of red or
square was present then they may assign a value of 0.75 to the blue square and a value of 0 to the red circle to reflect that the condition of interest square was more present in the blue square. In this instance the respective shape with the calibrated value of 0.75 would be considered partially in the set of red squares, indicated by a
calibration of above 0.5 but less than 1. With this approach to set calibration the diversity of cases can be accommodated.
Decisions around set calibration and membership need to be transparent and based upon theory and research (Ragin, 2008; Schneider & Wagemann, 2010, 2012). That is, when calibrating set membership of cases for a given set the researcher needs to justify any decisions regarding how cases are assigned.
As noted in the previous chapter, when the neat world of science meets the reality of society and social science that data can become noisy (Schneider & Wagemann, 2012). This noise reflects the nature of the complex/chaotic world and hence a method that allows the nature of chaos to be reflected in the way data can be analysed is useful in aiding the understanding of complexity. The ability to calibrate
89 by degrees in fsQCA, when compared to the crisp set approach of a set being either ‘fully in’ or ‘fully out’, has resulted in growing use of this approach(Rihoux & Marx, 2013; Thiem & Dusa, 2013).
Having determined fsQCA as the analysis approach for this research, further detail will now be presented regarding the research design, including selection of cases as well as detail of conditions and outcome of interest. The specifics of how sets were calibrated will be provided later in this chapter. Included is reference to several of the sub-questions which also reflect theoretical knowledge informing the research design with regards to the selection of outcomes, conditions and cases that
underpinned the analysis with fsQCA.