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Kastner et al. BMC Medical Research Methodology 201 3, 1 3:1 1 2 
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^BMC 

Medical Research Methodology 



RESEARCH ARTICLE Open Access 



Making sense of complex data: a mapping 
process for analyzing findings of a realist review 
on guideline implementability 

Monika Kastner 1 , Julie Makarski 2 , Leigh Hayden 1 , Lisa Durocher 2 , Ananda Chatterjee 1 , Melissa Brouwers 2 
and Onil Bhattacharyya 1 



Abstract 

Background: Realist reviews offer a rigorous method to analyze heterogeneous data emerging from multiple 
disciplines as a means to develop new concepts, understand the relationships between them, and identify the 
evidentiary base underpinning them. However, emerging synthesis methods such as the Realist Review are not well 
operationalized and may be difficult for the novice researcher to grasp. The objective of this paper is to describe 
the development of an analytic process to organize and synthesize data from a realist review. 

Methods: Clinical practice guidelines have had an inconsistent and modest impact on clinical practice, which may 
in part be due to limitations in their design. This study illustrates the development of a transparent method for 
organizing and analyzing a complex data set informed by a Realist Review on guideline implementability to better 
understand the characteristics of guidelines that affect their uptake in practice (e.g., clarity, format). The data 
organization method consisted of 4 levels of refinement: 1) extraction and 2) organization of data; 3) creation of a 
conceptual map of guideline implementability; and 4) the development of a codebook of definitions. 

Results: This new method is comprised of four steps: data extraction, data organization, development of a 
conceptual map, and operationalization vis-a-vis a codebook. Applying this method, we extracted 1736 guideline 
attributes from 278 articles into a consensus-based set of categories, and collapsed them into 5 core conceptual 
domains for our guideline implementability map: Language, Format, Rigor of development, Feasibility, 
Decision-making. 

Conclusions: This study advances analysis methods by offering a systematic approach to analyzing complex data 
sets where the goals are to condense, organize and identify relationships. 



Background 

Complex interventions, such as those used to improve 
quality of health care, are informed by principles from 
health services research, management, psychology and 
engineering, in addition to medicine. Despite this, they 
often lack a clear theoretical basis, making it hard to 
summarize this disparate literature in a way that can 
inform intervention design or interpretation of results 
[1]. A realist review is a knowledge synthesis methodology 
pioneered by Ray Pawson [2], which seeks to better under- 
stand what works for whom, in what circumstances and 



* Correspondence: monika. kastner@utoronto.ca 

'St. Michael's Hospital Li Ka Shing Knowledge Institute, 209 Victoria Street, 
Toronto M5B 1W8, ON, Canada 

Full list of author information is available at the end of the article 

Bio Med Central 



why [2], Realist reviews are an emerging method with few 
published examples [3-5], and are particularly relevant for 
complex and under-conceptualized topics with a heteroge- 
neous evidence base where traditional systematic reviews 
would often conclude that there is no evidence to inform 
next steps [6]. The recently published publication standards 
for Realist Reviews (i.e., RAMESES criteria [7] will likely fa- 
cilitate improved reporting of this method, as existing tech- 
niques to organize and synthesize such information are not 
well operationalized [8], and require further development 
to be optimized and to help novice researchers manage 
large datasets. 

To advance the science of analyzing complex and 
disparate data, this paper describes the development of 
a process for organizing and analyzing complex evidence 



© 201 3 Kastner et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative 
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reproduction in any medium, provided the original work is properly cited. 



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in the context of a Realist Review in the area of guideline 
implementability. We selected guideline implementability 
to illustrate our data analysis process because guidelines 
are considered an important knowledge translation tool yet 
its potential to facilitate the implementation of evidence 
into clinical practice has largely been unrealized [9-11]. 
Poor guideline uptake may be due to external factors such 
as the complex and competing demands on providers' time, 
organizational constraints, and lack of knowledge; as well 
as characteristics of the guidelines themselves (i.e., intrinsic 
factors). Approaches to improving uptake of guidelines 
have largely focused on complex knowledge translation 
interventions consisting of extrinsic strategies that target 
providers or practice environments. However, these strat- 
egies have yielded modest improvement with variable costs 
[12,13]. Intrinsic strategies (e.g., addressing the clarity, 
specificity and clinical applicability of recommendations) 
are promising because they are inexpensive, easy to 
implement and may be broadly applicable. Additionally, 
strategies that are being developed do not include disciplines 
outside of medicine (e.g., management and psychology), so 
they are not being optimized to advance knowledge in this 
area. We therefore conducted a realist review to better 
understand the concept of guideline implementability from 
a broad perspective of the literature, and to identify how 
guidelines could be optimized to increase their impact. 
More specifically, our goal was to identify guideline attri- 
butes that affect guideline uptake in clinical practice. The 
complete protocol for this review is described elsewhere 
[14], and the final results of this review will be published in 
a separate paper. Briefly, the realist review considered 
evidence from four disciplines (medicine, psychology, 
management, and human factors engineering) to determine 
what works for whom, in what circumstances and why in 
relation to guideline implementation [14]. The search strat- 
egy included expert-identified, purposive and bibliographic 
searching. The analytic approach drew on multiple ana- 
lysis methods (i.e., Realist synthesis and other qualitative 
synthesis methods). Although the realist review synthesis 
methods were helpful for interrogating our underlying 
theory (i.e., why guidelines are not being implemented) 
[1], Realist Review methods are relatively new, and it's 
guidance on the process for organizing and relating findings 
(i.e., the R AMESES criteria [7]) may be a challenge to 
reproduce by people who are new to the field. 

To address this issue, we describe the development of 
a process for organizing and analyzing complex evidence 
derived from findings of our realist review on guideline 
implementability as a means to advance the science of 
knowledge synthesis. 

Methods and results 

Figure 1 shows the flow of the process that was used to 
make sense of the realist review data consisting of 4 levels 



of refinement: 1) extraction and 2) organization of data; 3) 
creation of a conceptual map of guideline implementability; 
and 4) the operationalization of the map and its compo- 
nents vis a vis the development of a codebook of definitions 
that will inform the design of a framework. In this section 
we provide a description of the method used at each step 
and the results that emerged when the step was applied to 
our data set. 

Level 1 - Extraction of data 

Two groups of investigators extracted 1736 intrinsic 
guideline attributes (i.e., characteristics) from 278 included 
articles on study discipline (i.e., medicine, psychology, 
management, human factors engineering), attribute name 
and definition (as documented by authors), attribute 
operationalization (i.e., an explanation of how the attribute 
functions within the context of the discipline or study), at- 
tribute relationship with uptake, and any potential tradeoffs. 
To ensure reliability, consistency and accuracy of the data 
extraction, we used an auditing process whereby secondary 
reviewers checked data extractions of primary reviewers. 
Disagreements were resolved through consensus-based 
group discussions involving all investigators. 

Level 2 - Organization of data 

The 1736 identified attributes were sorted with the same 
name or root (e.g., valid/ validity) in an Excel database. 
Two groups of investigators (6 in total, 3 per group) then 
took the same list of sorted attributes and independendy 
clustered them into logical categories. This involved a 
process of building up groups of similar or like attributes 
(including their synonyms and antonyms) that concep- 
tually "fit" within a larger theme, and creating a label 
and description for each category. Table 1 describes the 
operationalization of this process. Categorizations between 
the two groups were compared for agreement aimed at 
identifying a common set of categories and their included 
attributes. This involved documenting "agreed" and "diver- 
gent" classifications, and making consensus -based decisions 
through group discussion. This highly systematic approach 
allowed for efficient filtering and consolidation of a large 
and complex dataset. 

Level 3 - Building a conceptual map of guideline 
implementability 

Using a consensus approach among the two groups of 
investigators via discussions of the attribute definitions 
and their similarities and relationships, the final set of 27 
categories (Table 2) were further grouped into 5 broad di- 
mensions associated with the uptake or use of guidelines: 
Language, Format, Rigor of Development, Feasibility, 
Decision-making. Based on the evidence around these 
domains, we developed broad and common sense defi- 
nitions for each as well as their included categories, which 



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Realist Review Methods 



Search strategy 



Data Organization 



Search strategy 

An iterative, multiple search strategy that 
consisted of 5 non-linear stages of searching 






Article selection 

Two sets of reviewers (6 in total) independently 
screened articles using inclusion criteria 






LEVEL 1 : Extraction of data 

1736 guideline attributes were extracted in 
duplicate from 278 included articles 








LEVEL 2: Organization of data 

2 sets of investigators (Group 1; Group 2) 
independently clustered 1736 attributes into 
logical categories 



Group 1 = 33 
categories 



Group 1 = 28 
categories 



Derived a common set of 
categories through group 
discussion (N = 27) 



LEVEL 3: Building a conceptual map of 
guideline implementability 

Discussions of the content and patterns of the 
attributes within categories led to their further 
classification into 5 broad domains: Language, 
Format, Evidence, Feasibility, and Decision- 
making 



LEVEL 4: Development of a Codebook of 
definitions 

Determine evidence-based definitions, 
operationalization, context, and relationship 
with uptake 



Stage 1: Core articles 

Stage 2: Expert identified 

Stage 3: PubMed Related articles 

Stage 4: Bibliography 

Stage 5: Other 

Abstract level: N = 2044 

Full text level: N =350 

Auditing process 

• Primary reviewers' data extractions 
audited by second reviewer 

• Disagreements resolved through 



Category agreement 

• Compared "agreed" and "divergent" 
classifications 

• Derived a common set of 
categories through group 
discussion 



Validation with 9 experts 

• Identify flaws in categorization 

• Sense and lit of categories, attributes 
and their labels and definitions 



Figure 1 Flow of data analysis process. 



informed a conceptual map of guideline implementability. 
The development of this map was guided by a web-based 
visualization tool, MindMeister (http://www.mindmeister. 
com), which was used iteratively by all investigators to de- 
termine the structure of the framework (i.e., moving back- 
and-forth from the map to definitions and source material), 
and to facilitate the decision-making process for group- 
ing and identifying patterns in the data. Such visualization 
techniques have been shown to facilitate comprehension, 
identify the inferences about the qualities of parts and the 
relations among them, and be useful for revealing the 
hierarchy of groupings and important relationships [15]. 

To validate and to identify potential flaws in categorization 
and to obtain agreement on the sensibility and fit of 
attributes within and across the categories, a group of 9 
stakeholders with knowledge translation and guideline 
development expertise were surveyed. These experts 



were asked to review the content of the 5 domains and 
its sub-domains, and to rename, rearrange and condense 
attributes as they saw fit. The survey comprised Likert-type 
and open-ended questions about the operational definition 
of the domains, and the fit of categories and their attributes 
within them (see Additional file 1). Through consensus- 
based discussions amongst our team, findings of this survey 
were used to make modifications to the organization and 
structure of our data (e.g., collapsing and renaming some 
attributes, categories and domains). 

Level 4 - Development of a codebook 

The two groups collectively developed a codebook of 
definitions to better understand each of the 5 domains 
of implementability, the relationships between guideline 
attributes and their uptake, and potential tradeoffs. The 
process involved documenting definitions for modifiable 



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Table 1 Operationalization of the categorization process using the "LANGUAGE" domain as an example 



Goal Steps Example 



uryamze, yruup, anci appropriately (duel 
similar or "like" attributes 


1. Group attributes that arc antonyms 

2. Group attributes that are synonyms 


• Complex/Simple 

• Unclear/Gonfusing 




3. Group attributes with the same root 


• Specific/Specificity 

• Validity/Valid 




4. Sort database by attribute 




Categorize attributes into logical clusters 


5. Are there commonalities among attributes? 


The following attributes can be grouped 
into a category called "Clarity" 

• Unambiguous 

* Pr6?cis^ 




6. Is there a central theme or focus among 
groups of attributes? 


• Specific 


Go through each cluster to determine 
sense and fit of attributes 


7. Do the attributes belong within the same cluster? 

8. Gan they be collapsed? 


The following categories can be collapsed: 
• "Complexity" with "Information overload" 




9. Use attribute definitions to make these decisions 


• "Actionability" (e.g., using active voice) 
with 'Wording" 


Develop a definition for clusters 


10. Based on their included attributes and 
definitions, define and label the cluster 


The LANGUAGE domain can be defined as: 
The clarity, precision, and specificity of the 
context and message of the guideline 



attributes (i.e., those that have the potential to be changed 
by guideline developers) and their operationalization 
(i.e., how the attribute can be used and examples of how it 
functions), the context and setting in which these occur, for 
whom, any relationship with uptake, and attribute tradeoffs 
if they existed (see the Additional file 2 for an example 
Codebook). The codebook was developed one domain at 
a time using a modified duplicate reviewing process 
that involved a set of primary reviewers extracting and 
documenting the information, and a second group of 
reviewers "auditing" (i.e., checking) primary reviews in 
small-group discussions; a third group of reviewers resolved 
disagreements. The main objectives of the auditing process 
were to verify the completion of documentation, to ensure 
the appropriate understanding of concepts, and to deter- 
mine the best fit of attributes and information within and 
between categories and domains. 

Discussion 

Complex interventions are often atheoretical and loosely 
draw on a broad literature that includes different disciplines 
and is difficult to summarize systematically. Qualitative 
synthesis methods are poorly operationalized and do not 
describe how to organize and analyze large heterogeneous 
datasets. We used a systematic process of analysis to build 
a conceptual map of guideline implementability through 
the classification of 1736 attributes into a consensus-based 
set of categories, which were then collapsed into 5 core con- 
ceptual domains of guideline implementability: Language, 
Format, Rigor of development, Feasibility, Decision-mating. 



These findings will be used to answer our Realist review 
question: What is it about guidelines that facilitate or 
impede their uptake, for whom and in what circumstances 
this happens, and how and why this happens. 

We reviewed a range of review methods to answer our 
research. The details explaining the rationale for selecting 
a Realist Review is published in our protocol [14]. Briefly, 
we assessed a range of review methods (i.e., Realist Review, 
Meta-narrative synthesis, and Meta-ethnography) to 
determine which of these was the most appropriate, 
but we found that none were a "perfect fit" to sufficiently 
cover all our questions. We selected the Realist Review 
method because the approach provides the most sys- 
tematic guidance on how to conduct a complete review 
(i.e., a process for a search strategy, article selection, and 
data analysis), it allows the inclusion of diverse evidence 
(i.e., quantitative and qualitative), and provides an explana- 
tory investigation of underlying theories and mechanisms 
of the study under investigation. In our case, causation' was 
determined by considering the interaction between con- 
texts (i.e., the circumstances and settings of guideline use), 
mechanisms (i.e., the processes operating within guidelines 
that explain why they are used in some circumstances but 
not in others) and outcomes (whether guidelines are used 
or not). We theorized that unpacking these C-M-O rela- 
tionships would facilitate our understanding of guideline 
implementability. However, one difficulty with the Realist 
Review method is that it lacks a comprehensive process to 
compare disciplinary perspectives on a given issue. We then 
considered Meta-narrative synthesis, which can be helpful 



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Table 2 Final list of attribute categories across 5 domains of guideline implementability 



Category (N = 27) 



Major attributes 



Domain (N = 5) 



Clarity 

Cognitive fluency 

Complexity 

Wording 

Framing 

Graphical 

Inclusion of specific elements in recommendation 

Mode of delivery 

Presentation/Layout/Design 

Structure/Organization 

Benefits-harms 

Credibility 

Reliability/Reproducibility 

Rigor of development 

Strength and quality of recommendations 

Validity 

Acceptability 

Actionability 

Adaptability 

Feasibility 

Implementation considerations 
Usability 

Clinical significance 
Considered judgment 
Flexibility 

Patient preferences 
Values 



Rigor of development 



Ambiguity, Specificity, Vagueness Language 
Congruity, Fluency, Schema 
Complexity, Options, Difficult to understand 
Concision, Embedded propositions 

Relative advantage, Gain-loss frame Format 
Algorithm, Graphs, Tables 

Elements (e.g., include harms-benefits, patient information, 
Boolean operators) 

Accessibility, Computability 

Visual imagery, Presentation 

Arrangement, 

Balance of benefits/harms, Dual viewpoint 
Credible, Authoritative 
Reliable, Reproducible, Explicitness 
Evidence-based, Evidence-linked 

Quality of evidence, Strength of evidence, Evidence grading 
Validity, Up-to-date 

Acceptability, Fit with decision-making, Perceived usefulness, Visibility Feasibility 

Actionable, Executable, Operationalizable 

Adaptability, Context, Tailoring 

Feasibility, Compatibility, Costs, Resources 

Implementability factors affecting feasibility, Trialability 

Ease of use, Usefulness 

Clinical relevance, Applicability Decision-making 

Appropriateness, Value judgments 

Flexibility, Clinical freedom 

Patient involvement/communication/values 

Beliefs, Compatibility, Values/Norms 



for analysing data across different fields or disciplines [16]. 
Meta-ethnography was another method that we considered, 
which involves translating key concepts from one study 
to another to reveal new insights [17], but its application 
to large data sets and its focus on qualitative studies pre- 
sents challenges when the data set is large and comprised 
of mixed study designs. This lack of a "perfect fit" highlights 
the need to consider all factors associated with the research 
question when deciding which method is the most appropri- 
ate to answer them. These included determining the breadth 
of evidence needed (quantitative or qualitative or both) and 
balancing this need with the feasibility or resources available 
to perform the review, anticipating the end-users of findings, 
and to what extent the method provides strategies for rigor 
and transparency. In fact, these are similar considerations 
we may use for selecting the most appropriate methods for 
primary studies. There has been a resurgence of interest in 



developing new knowledge synthesis methods to address 
the limitations of some of the traditional synthesis strat- 
egies such as the systematic review. Like realist review, 
the advantage of these methods is that they can help 
organize information from underconceptualized fields 
like knowledge translation and quality improvement to 
create a more cumulative knowledge base. However, 
methodological strategies that are more accessible are 
required if they are to be widely used and optimized. To this 
end, a scoping review by Tricco et al. is currently underway 
to determine which knowledge synthesis methods are 
available, and to develop a systematic process to help re- 
searchers select the most appropriate method(s) to address 
their research questions about complex evidence [18]. 

A limitation of our work is that the approach we used 
was largely interpretive. However, the quality of synthe- 
sis is dependent on reviewers' explicitness and reflexivity 



Table 3 Suggested approach to organize, synthesize, validate and make sense of complex findings 



Step 



Points to consider 



Example 



Advantages 



Challenges 



How to overcome challenges 



1. Selection of analysis 
method 



2. Organization and 
analysis of data 



3. Validity measures 



4. Representation of data 



5. Dissemination of data 



• Which method is the most 
appropriate to answer 
research questions? 



• How will the data be 
organized? 



• Will also depend on 
selected analysis method 



■ How are you going to 
verify findings and 
minimize bias? 



• How will the results 
and data be used? 



• Who are the target 
knowledge end users? 



■ To what extent should 
the data be disseminated? 

• Will the work inform 
practice, system, policy? 



• We searched the literature 
for various synthesis methods 
of complex evidence 



• We sorted and organized our 
data (1736 guideline attributes) 
in an Excel database 



• Analysis process was done 
in duplicate 



• Sought expert consensus 
on findings using survey 
methodology 



• We developed a conceptual map 
of guideline implementability for 
guideline developers and 
end-users 



• The map will inform a guideline 
implementability framework for 
guideline developers, users and 
policy makers 



• Potentially more valid if the 
method matches the question 



• Sorting of concepts and 
themes on multiple levels 
(e.g., across attributes, categories, 
disciplines) 

• Duplicate analysis 
minimizes bias 



• Survey methodology is quick 
and efficient 



• There was no single 
synthesis method that best 
fit our questions 



• Difficult to keep track 
of changes from multiple 
reviewers 

• Duplicate review is 
time consuming and 
resource intensive 



• Survey methodology 
has inherent biases 



• The conceptual map 
contributes to the 
understanding of guideline 
implementability 

• The process advances the 
knowledge about analysis 
methods for complex evidence 

• The framework will inform 
end-users about attributes that 
facilitate guideline uptake; and 
may also inform policy around 
guideline development 



• There may be other 
factors not captured in the 
map that may influence 
guideline implementability 



• There may be other factors 
influencing guideline 
implementability 



• Need to adopt a flexible approach to 
match appropriate methods to answer 
research questions 

• Consider selecting a primary analysis 
method supplemented by other or 
modified methods to address all questions 

• We used a modified duplicate review 
process that involved a group of second 
reviewers "auditing" the analysis of 
primary reviewers 

• Ensure that document tracking is 
transparent and efficient (e.g., track 

and document changes and include detailed 
notes from all reviewers) 

• Depending on resources, other consensus 
methods may increase validity such as the 
Delphi method 

• Transparency (i.e., document what was 
planned, what was done and why) 

• The conceptual framework needs to be 
refined according to the codebook of 
definitions 

• The conceptual framework needs to be 
rigorously evaluated to determine the 
feasibility of its use by guideline 
developers, and its potential to influence 
guideline uptake by family physicians 

• Prior to dissemination, the framework will 
need to undergo rigorous evaluation 
(including quantitative and qualitative studies) 
to test its potential to influence guideline 
uptake by family physicians who are the 
primary end-users of clinical practice guidelines 



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of the methods. In our process to make sense of the 
complex data that emerged from our Realist Review, we 
ensured transparency of the methods and included several 
validity measures to minimize sources of error. This was 
important given the interpretive nature of our process and 
the anticipated learning curve involved in data abstraction. 
The measures included an auditing process whereby pri- 
mary data extractions was checked by secondary reviewers, 
and a process to verify this data against a codebook of defi- 
nitions during Level 4 analysis. Lasdy we tested the validity 
of our data organization and analysis through an expert 
survey to verify the sense and fit of attributes and categories 
within the framework. 

In our realist review, we considered each attribute and 
integrated like-attributes into common themes and 
domains. Further, we considered evidence of impact or 
effectiveness on our relevant outcome. For example, 
evidence indicates that a guideline recommendation is 
more actionable if it clearly specifies when, who should do 
precisely what action; if a recommendation does not specify 
these steps or uses passive verbs, its actionability will be di- 
minished. Such conceptualization of the evidence can then 
be useful to support or refute various theories or their ele- 
ments in the literature about guideline implementability. 
These strategies enabled us to embrace the whole of the 
data, with few preconceived expectations, to identify and 
carefully define elements that are relevant to guideline up- 
take. The approach described in this paper is an example of 
how new analytic methods can emerge and respond to the 
challenges related to finding the best fit between methods 
and research questions. Based on our experience, Table 3 
highlights suggested steps to help determine the purpose 
and scope of poorly understood concepts under investi- 
gation such as guideline implementability. This may be 
particularly useful to help organize, synthesize, validate, 
and represent complex data resulting from qualitative 
reviews in a relevant and meaningful way. 

Our work has the potential for wide influence. The 
proposed method will appeal to more investigators 
because the process has now been operationalized, is fairly 
straightforward to apply, it can be applied to a wide range 
of topics and the return on effort is significant. Expanding 
this knowledge base will become particularly important as 
these rapidly expanding fields most often require more so- 
phisticated techniques to analyze data, which is informed 
by complex interventions that cut across multiple disci- 
plines and from the input of multiple stakeholders. 

Conclusions 

This study represents a novel contribution to advancing 
complex data analysis methods by offering a systematic 
approach to analyzing any large and disparate data sets 
where the goals are to condense, organize and identify 
relationships. 



Additional files 



Additional file 1: Expert feedback review form on the Guideline 
Implementability Framework. 

Additional file 2: Example of a Codebook of definitions. 



Competing interests 

None of the authors have any financial or non-financial competing interests 
to declare. 

Authors' contributions 

All authors contributed in the design of the study. MK, LH, AC, JM, LD 
executed the study and MK, LH, JM, AC, LD, OB, MB conducted the analysis 
and interpreted the results. MK drafted the manuscript, and all authors read 
and approved the final manuscript. 

Author details 

'St, Michael's Hospital Li Ka Shing Knowledge Institute, 209 Victoria Street, 
Toronto M5B 1W8, ON, Canada, department of Oncology, Juravinski 
Hospital and Cancer Centre, McMaster University, 71 1 Concession Street, 
Hamilton L8V 1C3, ON, Canada. 

Received: 5 December 2012 Accepted: 6 September 2013 
Published: 12 September 2013 

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doi:1 0.1 1 86/1 471 -2288-1 3-112 

Cite this article as: Kastner et al:. Making sense of complex data: a 
mapping process for analyzing findings of a realist review on guideline 
implementability. BMC Medical Research Methodology 201 3 1 3:1 1 2. 



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