During the first phase of the funnel of complexity theoretical and case knowledge should be used to develop the research design (Rihoux & Lobe, 2009). In this before the analytic moment phase of the research design, the conditions, outcome and cases should therefore be informed by theoretical and case knowledge.
Configurational comparative methods should not be used for anything other than their original aims of understanding complexity (Ragin, 2009; Rihoux & Lobe, 2009; Schneider & Wagemann, 2010, 2012). To ensure these aims are met there needs to be transparent justification surrounding selection of cases, conditions and
outcomes, the use of appropriate terminology and a detailed discussion
surrounding the assignment of set membership scores (Berg-Schlosser & De Meur, 2009; Schneider & Wagemann, 2010).
It has already been highlighted that the aims and research question of this research have been developed from theory and existing research, as outlined in the
90 have further ensured the use of theory in the development of the research design will now be provided.
The first of these research sub-questions is: what level of reporting and disclosure of different severity of a hypothetical medication error do nurses in rural clinical
settings think is occurring? This sub-question was developed through consideration of the literature. Little research is available surrounding reporting and disclosure of error in rural clinical settings (Aranaz-Andrés et al., 2011; Jones et al., 2008; Wholey et al., 2004)(Section 2.12). In addition, the involvement of nurses in error disclosure is not well researched (Harrison et al., 2014).
Similar issues were identified in relation to safety culture and more particularly safety climate. This resulted in the development of a second research sub-question which is: what is the nature of workplace safety climate amongst nurses in these settings?
In both instances differences were noted amongst different workplace settings and work roles as well as other elements such as experience and issues such as burnout. Current research has focused upon the identification and measurement of such differences but not upon understanding them.
The conflicting information provided in the t literature makes it difficult to
determine if there is a relationship between safety climate and the reporting and disclosure of error. Although some studies suggest there is a relationship (Colla et al., 2005; Hutchinson et al., 2009) others suggest there may not be (Freeth et al., 2012; Groves, 2014). In addition, in relation to rural clinical settings there are no known studies of this relationship.
In order to address the first two research sub-questions frequency data are required. As noted in the literature review existing studies have adopted the approach of asking participants their reporting and disclosure habits (Haw et al., 2014; Kagan & Barnoy, 2008) or they have observed them (Bayazidi et al., 2012;
91 Westbrook et al., 2015). For this reason the use of a statistical approach with CCM, was required, particularly the use of inferential statistics.
In addition, safety climate is generally measured using questionnaires which have the capacity to reduce questions or items into groups or factors (Etchegaray, St. John, & Thomas, 2011; Freeth et al., 2012). For this reason principal components analysis (PCA) was also used for this research.
Therefore, whilst a variety of options to obtain data are available, use of a
questionnaire would allow for collection of data in relation to both factors of safety climate and views of reporting and disclosure. Use of a hypothetical medication error could also be accommodated through this means.
Further detail regarding the development of a questionnaire is presented later in this chapter. Before this can be considered, more detail regarding the identification of outcomes, conditions and cases is required.
The third research sub-question is: what is the relationship between workplace safety climate amongst nurses in rural clinical settings and their views of reporting/disclosure of a hypothetical medication error? This sub-question compares the relationship between safety climate and views of reporting and disclosure of error. Recalling the debate outlined in the previous study regarding case and variable research (Ragin & Amoroso, 2011)it is possible that both approaches may be used for this research in order to address the third sub- question. That is, the relationship between safety climate and views of reporting and disclosure could be analysed using both a variable-based and case-based analysis.
In addition it is recommended that when undertaking fsQCA another form of analysis is used (Schneider & Wagemann, 2010). Therefore, use of inferential statistics to compare variables was possible alongside a case-based analysis of safety climate conditions analysed in relation to the outcome of views of reporting
92 and disclosure of a hypothetical medication error also conducted. The use of both inferential statistics and PCA within a CCM research design assisted in obtaining and analysing data to address a further sub-question and therefore contribute to the findings relating to the research question and aims of this research.
With the conditions and outcomes informed by the research sub-questions a further step was required. The selection of cases needed to be informed by case and
theoretical knowledge.
The aim of this research specifically refers to nurses in rural clinical settings as does the main research question and three sub-questions discussed thus far. The
research aim, question and sub-questions were developed through reviewing both the context of patient safety and related research. Therefore theoretical and case knowledge has therefore informed the selection of cases.
The rationale for the focus upon nurses was also determined through the literature review. Nurses are actively involved in medication management (Choo et al., 2010). In addition, there is limited research involving nurses, including rural nurses, in the process of error disclosure (Harrison et al., 2014; O'Connor et al., 2010).
It is therefore clear that theoretical knowledge determined the outcome (views of reporting and disclosure), conditions (factors of teamwork and safety climate) and
cases (nurses in rural clinical settings) for this research. Having done this it was then necessary to obtain data from the cases in relation to the outcome and conditions in a manner that allowed for transparent set calibration.
Questionnaire development
As outlined in the previous section, it was necessary to transparently obtain data for this research in relation to conditions, outcome and cases. A questionnaire was developed for this purpose and this will now be detailed.
93 The questionnaire contained three sections. The first obtained data relating to factors of safety climate, the second made use of a hypothetical scenario to obtain data regarding views of reporting and disclosure and the third section collected demographic information.
To enhance transparency and reduce researcher bias, for the first two sections existing tools were used. The Safety Attitudes Questionnaire (SAQ) (Sexton et al., 2006) was used for collecting data regarding the conditions and a hypothetical medication error used in a previous study allowed for data relating to the outcome to be obtained (Weissman et al., 2005). The section relating to demographic was developed based upon the type of data collected by the SAQ, differences identified through the literature review (Chapter 2) and the context in which the research was undertaken (Department of Health and Human Services, 2007; Sexton et al., 2006). As noted in the literature review (Section 2.15) there are numerous tools available for collection of data relating to safety climate (Colla et al., 2005; Flin et al., 2006; Valentine et al., 2015). Three elements require attention when choosing a safety climate data collection instrument (Flin et al., 2006). These are content validity, criterion related validity and factor analysis.
Content validity refers to the nature of questions contained within theSAQ and whether or not they measure what is intended to be measured (Flin et al., 2006). Use of theory, expert judgment and available literature are means of determining
content validity (Bryman, 2012).
One particular study undertaken in the UK adapted the language of the SAQ to suit the non-hospital primary care environment (Hutchinson et al., 2006). The settings of this previous research were similar to those of the present research in that they were outside the acute hospital environment. Therefore the same wording was used for this present research. For in the context of this research, theory therefore informed content validity.
94 The correlation of safety attitudes factor scores with outcome data is referred to as
criterion related validity (Flin et al., 2006). The SAQ has been used in several studies in this respect (Colla et al., 2005; Hutchinson et al., 2009). Although the preference is to source information from something other than the data collection instrument it has been noted that self-reporting is often the only means available (Flin et al., 2006). Data regarding outcomes were collected from the same instrument for this research.
Factor analysis shows if the themes being measured can be distinguished (Flin et al., 2006). This approach has been used in prior studies with the SAQ (Hutchinson et al., 2006; Sexton et al., 2006). The term factor analysis is used to describe various ways in which a number of variables may be reduced to a smaller number of coherently grouped factors (Tabachnick & Fidell, 2007).
The term is often used interchangeably with that of principal components analysis
(PCA). However, there is a subtle difference in that PCA is used to reduce the observed variables into components (Matsunaga, 2010). For this research the items in the SAQ were reduced to a set of factors for safety climate and teamwork. Therefore the term principal components analysis is used. Although this process identifies components rather than factors, it has been noted that the term factors
may also be used to avoid confusion. Therefore, the term factor was used for this research. More detail regarding how this was analysis was conducted is presented in Section 4.3.3.
As much information as possible should be collected when using PCA (Matsunaga, 2010). For this reason the questionnaire used in the previously mentioned UK study (Hutchinson et al., 2006) was administered with use of all items including those not retained in the final factor structure identified by the previous study.
Organisations are considered hierarchical, therefore safety climate may be
measured at the individual, work group, department or organisational level (Flin et al., 2006). Of the two most widely used surveys for safety climate, the SAQ and
95 HSOPSC, the former has been found to be more suitable for benchmarking and examining relationships with outcomes whereas the latter allows for unit- and institutional-level results (Etchegaray & Thomas, 2012).
As noted in Chapter 2 (Section 2.15) the SAQ is considered more reliable to assess safety climate across a whole health system (Etchegaray & Thomas, 2012). This supports the use of the SAQ for this research which was conducted to examine the relationship of safety climate with views of reporting and disclosure of error. The second section of the questionnaire obtained data relating to these outcomes. A hypothetical medication error was used to collect data regarding the outcome of nurses’ views of reporting and disclosure (Weissman et al., 2005). The use of a hypothetical scenario was considered a means to reduce the risk to participants in that they would be answering in relation to a hypothetical situation rather than being asked to respond to events that had occurred in their workplace that may or may not have been reported or disclosed.
The error constituted the prescribing of an antibiotic to which a patient had a known documented allergy and three outcomes of an allergic reaction with irreversible severe harm, a reaction that was treated and the patient recovered (moderate error) and a near miss error outcome where the patient had no reaction and after two days of receiving the medication the prescription was changed to another medication. The near miss scenario could also be considered a “no harm incident” in reference to definitions used in the Australian context (Australian Commission for Safety and Quality in Health Care, 2013a).
This scenario was adapted, with permission, from that of a previous study amongst hospital leaders in the US (Weissman et al., 2005). Adaptions of the scenario for this present research included use of the generic (non-brand) name for the medication prescribed and clarification that there were no allergy symptoms for the near miss outcome.
96 The content validity of this scenario was verified through the literature relating to medication error. Prescribing errors where there are known documented allergies occur in hospital, community and aged care settings (Australian Commission for Safety and Quality in Health Care, 2013b; Easton et al., 2009). As the prescribing of the medication was for a urinary tract infection, a condition that is common within hospitals and community settings in Australia, the scenario was considered suitable for use in this research (Jarvis, Chan, & Gottlieb, 2014).
It should be noted here that the error scenario relates to views of what nurses think is being reported or disclosed rather than what nurses themselves would report. In addition, it has already been highlighted in previous chapters that nurses often state they would report an error more than which they actually do report in practice (Section 2.10). Therefore, this present study may not be directly comparable to studies of actual reporting rates. Both these elements are considered in more detail in Chapter 3 (Section 6.1)
The final section of the questionnaire obtained information relating to the nurse respondents who made up the cases for this research. Demographic information obtained included, workplace setting, work role, experience in current role and experience working in nursing, and worksite postcode. Details of how questions were developed for each of these can be found in Appendix 5. As noted above, the SAQ is best used to collect data across a whole health system so its use in this research for the collection of the demographic data was appropriate as it allowed for a comparisons of safety climate and views of reporting and disclosure to be compared amongst different groups from within a whole health system.
It has already been identified in the literature review that differences in reporting, disclosure and safety climate have been found relating to workplace setting (Latif et al., 2013), work role (Hobgood, Weiner et al., 2006; Kagan & Barnoy, 2008; Kim et al., 2007; Morello et al., 2013) and experience (Chiang et al., 2010; Cole et al., 2013). Therefore work setting, work role and experience were included in demographic data collection.
97 Registration level, employment sector and facility size were also included as
demographic items. Two levels of registration operate in Australia encompassing different roles in the administration of medications (Kerr, Lu, Mill, & McKinlay, 2012). It was considered there may therefore be different views associated with this.
The ACSQHC has also noted concerns regarding low response rates from non- government small rural hospitals to a survey they conducted relating to the new standards for accreditation (Australian Commission for Safety and Quality in Health Care, 2012b). This suggested it was worthwhile examining data based upon
employment sector as well as facility size.
Postcode data were obtained in order to ensure responses were received from worksites in the locations of interest as well as allowing for data to be analysed in relation to the three regional health areas operating in Tasmania at the time of data collection (Department of Health and Human Services, 2007, 2015). The ASGC-RA locality was also determined by postcode (Australian Government, 2010). A table combining this information and appears in Appendix 5.
The demographic information collected was kept to a minimum and details regarding specific worksites were omitted. This was done to ensure nurses could not be identified despite the anonymous nature of the questionnaire. In addition, as the content of the research related to safety there were concerns that if data were analysed on a worksite-by-worksite basis there was potential for a particular facility to be labelled as “unsafe” if safety climate scores were found to be low.
Set calibration
With the data collection questionnaire finalised it was necessary to determine how the cases would be assigned to the condition and outcome sets through a
transparent process of calibration. It is recommended that this process be discussed in detail (Schneider & Wagemann, 2010).
98 Whilst the name “Qualitative Comparative Analysis” suggests the method is
qualitative this does not mean that only qualitative data may be analysed. Both numerical and non-numerical forms of data may be used with QCA (Ragin, 2009). The term “qualitative” is more applicable in the nature of the analysis itself than the data that is used (Schneider & Wagemann, 2012). Through applying a qualitative descriptor to a data set the method allows for data to be “described” (Schneider & Wagemann, 2012). Hence it was possible to use a questionnaire using quantitative data for this research.
The use of another form of analysis alongside fsQCA is also recommended (Schneider & Wagemann, 2010). The calibration of the data for fsQCA also determined which statistical tests were applied for this research. These are discussed in detail at a later stage in this chapter (Section 4.3).
When undertaking set calibration it is important to determine the crossover point
(also referred to as the cross-over or qualitative anchor) (Ragin, 2008; Schneider & Wagemann, 2012). This is given the value of 0.5 and distinguishes the point at which a case may be neither in nor out of a set. Values also need to be set for fully in and fully out.
The SAQ was used for the purpose of obtaining data relating to the conditions. The importance of factor analysis has already been noted earlier and how it has been applied in this research will be discussed in more detail at a later stage. Through the use of this process it is possible to develop factor scores from the SAQ.
Once factors were identified a factor score was calculated. Items were allocated a score from 1-5 (Agree Strongly = 5, Agree Slightly = 4, Neither Agree nor Disagree = 3, Disagree Slightly = 2, Disagree Strongly =1) (University of Texas Health, 2015).
Following reversal of negatively worded items, the scores are then converted to a 100 point scale score using the formula:
99 Guidelines relating to SAQ scores were used to assist calibration of the conditions set further (University of Texas Health, 2015). A score of 75 or higher is considered positive (University of Texas Health, 2015). The crossover point for set calibration was therefore set at a value of 74.999. This value ensured a case with a score of 75 or higher was allocated to be fully in the set of positive factor score (the qualitative descriptor) and a scores of 74.99 were considered outside the set.
The scores below 75 are “0”, “25” and “50” representing initial values of 1 (strongly disagree), 2 (disagree) and 3 (neutral) respectively. It could be argued that a neutral
score, whilst not positive, is closer to being so that disagree or strongly disagree,
with the former partially out of the set of positive factor scores, the latter fully out, and disagree responses almost fully out. Thus, scores of 25 or less were determined as fully out (allocated a value of 0), scores of 50 but less than 75 were considered
partially out (0.49), and scores over 25 but less than 50 considered almost fully out (0.25)., This resulted raw data being converted to calibration ranges of between
0and 1. A summary of the calibration of conditions appears in Table 4-1.
Calibration of the outcome sets for views of reporting was undertaken using a