Nursing Research Methods Discussion (WALDEN)

Discussion 1: Qualitative Research in Nursing Practice

Which method of research provides the best evidence for nursing practice? Is there a place for both quantitative and qualitative research in evidence-based practice? How do these research methods improve patient outcomes?
Return to and reflect on your thoughts and postings from Week 1’s Discussion on the respective characteristics, strengths, and limitations of quantitative and qualitative research. Consider the uses of each type of research in the health care field. Also reflect on the amount of quantitative versus qualitative research that exists in the health care field in particular. There is an impression among some researchers that qualitative research is inferior to, easier than, or not as rigorous as quantitative research.
In this Discussion, you consider the idea of rigor, or thoroughness and scope of study, with regard to qualitative research. You examine the methods of qualitative research outlined and recommended in this week’s Learning Resources and how they aim to create standards of rigor by which to assess qualitative studies. You also have the opportunity to assess an article of your choice in terms of rigor and recommended methods of qualitative data collection.

To prepare:

Consider your readings about and understanding of quantitative and qualitative research. If you had to choose, which type of research (quantitative or qualitative) do you think is more rigorous and why? Do you think it is useful to make such generalizations and comparisons?
Locate an article describing a qualitative research study related to a health care topic. (I HAVE ATTACHED THE CHOSEN ARTICLE BELOW LABELED “CHOSEN ARTICLE”)
Formulate a research question to address the problem and that would lead you to employ correlational statistics.
With information from the Learning Resources in mind, critically analyze your selected study. Ask yourself: How rigorous was the study in terms of the researchers’ efforts, the data collected, and the conclusions drawn? What might the researchers have done to improve the rigor?

By Day 3

Post a cohesive response that addresses the following:
Do you think there is one type of research (quantitative or qualitative) that is inherently more rigorous than the other? If so, identify which one and why. If not, discuss your reasoning.
Post a brief summary of your research article analysis and the correct APA citation for the article.
Outline how the study’s qualitative data collection and analysis did, or did not, promote rigor, provide scientific or systematic scaffolding, and/or generate a more thorough analysis of the research topic.

Smith, J., & Firth, J. (2011). Qualitative data analysis: the framework approach. Nurse Researcher, 18(2), 52–62. Retrieved from https://search-ebscohost-com.ezp.waldenulibrary.or…

A Sample Answer For the Assignment: Nursing Research Methods Discussion (WALDEN)

Title:  Nursing Research Methods Discussion (WALDEN)

Health Research and Educational Trust DOI: 10.1111/j.1475-6773.2006.00684.x Qualitative Data Analysis for Health Services Research: Developing Taxonomy, Themes, and Theory Elizabeth H. Bradley, Leslie A. Curry, and Kelly J. Devers [Correction added after online publication February 2, 2007: on the first page, an author’s name was misspelled as Kelly J. Devens. The correct spelling is Kelly J. Devers.] Objective. To provide practical strategies for conducting and evaluating analyses of qualitative data applicable for health services researchers. Data Sources and Design. We draw on extant qualitative methodological literature to describe practical approaches to qualitative data analysis. Approaches to data analysis vary by discipline and analytic tradition; however, we focus on qualitative data analysis that has as a goal the generation of taxonomy, themes, and theory germane to health services research. Principle Findings. We describe an approach to qualitative data analysis that applies the principles of inductive reasoning while also employing predetermined code types to guide data analysis and interpretation. These code types (conceptual, relationship, perspective, participant characteristics, and setting codes) define a structure that is appropriate for generation of taxonomy, themes, and theory. Conceptual codes and subcodes facilitate the development of taxonomies. Relationship and perspective codes facilitate the development of themes and theory. Intersectional analyses with data coded for participant characteristics and setting codes can facilitate comparative analyses. Conclusions. Qualitative inquiry can improve the description and explanation of complex, real-world phenomena pertinent to health services research. Greater understanding of the processes of qualitative data analysis can be helpful for health services researchers as they use these methods themselves or collaborate with qualitative researchers from a wide range of disciplines. Key Words. Qualitative methods, taxonomy, theme development, theory generation Qualitative research is increasingly common in health services research (Shortell 1999; Sofaer 1999). Qualitative studies have been used, for example, to study culture change (Marshall et al. 2003; Craigie and Hobbs 2004), physician–patient relationships and primary care (Flocke, Miller, and Crabtree 2002; Gallagher et al. 2003; Sobo, Seid, and Reyes Gelhard 2006), diffusion of innovations and 1758 Qualitative Data Analysis for Health Services Research 1759 quality improvement strategies (Bradley et al. 2005; Crosson et al. 2005), novel interventions to improve care (Koops and Lindley 2002; Stapleton, Kirkham, and Thomas 2002; Dy et al. 2005), and managed care market trends (Scanlon et al. 2001; Devers et al. 2003). Despite substantial methodological papers and seminal texts (Glaser and Strauss 1967; Miles and Huberman 1994; Mays and Pope 1995; Strauss and Corbin 1998; Crabtree and Miller 1999; Devers 1999; Patton 1999; Devers and Frankel 2000; Giacomini and Cook 2000; Morse and Richards 2002) about designing qualitative projects and collecting qualitative data, less attention has been paid to the data analysis aspects of qualitative research. The purpose of this paper is to offer practical strategies for the analysis of qualitative data that may be generated from in-depth interviewing, focus groups, field observations, primary or secondary qualitative data (e.g., diaries, meeting minutes, annual reports), or a combination of these data collection approaches. WHY QUALITATIVE RESEARCH? Qualitative research is well suited for understanding phenomena within their context, uncovering links among concepts and behaviors, and generating and refining theory (Glaser and Strauss 1967; Miles and Huberman 1994; Crabtree and Miller 1999; Morse 1999; Ragin 1999; Sofaer 1999; Patton 2002; Campbell and Gregor 2004; Quinn 2005). Distinct from qualitative work, quantitative research seeks to count occurrences, establish statistical links among variables, and generalize findings to the population from which the sample was drawn. Although qualitative and quantitative methods have historically been viewed as mutually exclusive, rigid distinctions are increasingly recognized as inappropriate and counterproductive (Ragin 1999; Sofaer 1999; Creswell 2003; Skocpol 2003). Mixed methods approaches (Creswell 2003) may include both methods employed simultaneously or sequentially, as appropriate. TYPES OF QUALITATIVE ANALYSIS There is immense diversity in the disciplinary and theoretical orientation, methods, and types of findings generated by qualitative research (Yardley Address correspondence to Elizabeth H. Bradley, Ph.D., Professor, Department of Epidemiology and Public Health, Yale University School of Medicine, 60 College Street, New Haven, CT 065208034. Leslie A. Curry, Ph.D., Associate Professor of Medicine, is with the University of Connecticut School of Medicine, Farmington, CT. Kelly J. Devers, Ph.D., Associate Professor, is with the Departments of Health Administration and Family Medicine, Virginia Commonwealth University, Richmond, VA. 1760 HSR: Health Services Research 42:4 (August 2007) 2000). The many traditions of qualitative research include, but are not limited to, cultural ethnography (Agar 1996; Quinn 2005), institutional ethnography (Campbell and Gregor 2004), comparative historical analyses (Skocpol 2003), case studies (Yin 1994), focus groups (Krueger and Casey 2000), in-depth interviews (Glaser and Strauss 1967; McCracken 1988; Patton 2002; Quinn 2005), participant and nonparticipant observations (Spradley 1980), and hybrid approaches that include parts or wholes of multiple study types. Consistent with the pluralism in theoretical traditions, methods, and study designs, many experts (Feldman 1995; Greenhalgh and Taylor 1997; Sofaer 1999; Yardley 2000; Morse and Richards 2002) have argued that there cannot and should not be a uniform approach to qualitative methods. Nevertheless, some approaches to qualitative data analysis are useful in health services research. In this paper, we focus on strategies for analysis of qualitative data that are especially applicable in the generation of taxonomy, themes, and theory (Table 1). Taxonomy is a formal system for classifying multifaceted, complex phenomena (Patton 2002) according to a set of common conceptual domains and dimensions. Taxonomies promote increased clarity in defining and hence comparing diverse, complex interventions (Sofaer 1999), which are common in health policy and management. Themes are recurrent unifying concepts or statements (Boyatzis 1998) about the subject of inquiry. Themes are fundamental concepts (Ryan and Bernard 2003) that characterize specific experiences of individual participants by the more general insights that are apparent from the whole of the data. Theory is a set of general, modifiable propositions that help explain, predict, and interpret events or phenomena of interest (Dubin 1969; Patton 2002). Theory is important for understanding potential causal links and confounding variables, for understanding the context within which a phenomenon occurs, and for providing a potential framework for guiding subsequent empirical research. CONDUCTING THE ANALYSIS Overview There is no singularly appropriate way to conduct qualitative data analysis, although there is general agreement that analysis is an ongoing, iterative process that begins in the early stages of data collection and continues throughout the study. Qualitative data analysis, wherein one is making sense of the data collected, may seem particularly mysterious (Campbell and Gregor 2004). The following steps represent a systematic approach that allows for Qualitative Data Analysis for Health Services Research 1761 Table 1: Selected Types of Results from Qualitative Data Analysis Results Taxonomy Themes Theory Definition Formal system for classifying multifaceted, complex phenomena according to a set of common conceptual domains and dimensions Recurrent unifying concepts or statements about the subject of inquiry A set of general propositions that help explain, predict, and interpret events or phenomena of interest Application/Purpose Increase clarity in defining and comparing complex phenomena Characterize experiences of individual participants by general insights from the whole of the data Identify possible levers for affecting specific outcomes; guide further examination of explicit hypotheses derived from theory open discovery of emergent concepts with a focus on generating taxonomy, themes, or theory. Reading for Overall Understanding Immersion in the data to comprehend its meaning in its entirety (Crabtree and Miller 1999; Pope, Ziebland, and Mays 2000) is an important first step in the analysis. Reviewing data without coding helps identify emergent themes without losing the connections between concepts and their context. Coding Qualitative Data Once the data have been reviewed and there is a general understanding of the scope and contexts of the key experiences under study, coding provides the analyst with a formal system to organize the data, uncovering and documenting additional links within and between concepts and experiences described in the data. Codes are tags (Miles and Huberman 1994) or labels, which are assigned to whole documents or segments of documents (i.e., paragraphs, sentences, or words) to help catalogue key concepts while preserving the context in which these concepts occur. The coding process includes development, finalization, and application of the code structure. Some experts (Morse 1994; Morse and Richards 2002; Janesick 2003) argue that a single researcher conducting all the coding is both sufficient and preferred. This is particularly true in studies where being embedded in ongoing relationships with research participants is critical for the quality of the data collected. In such cases, the researcher is the instrument; 1762 HSR: Health Services Research 42:4 (August 2007) data collection and analysis are so intertwined that they should be integrated in a single person who is the ‘‘choreographer’’ ( Janesick 2003) of his/her own ‘‘dance.’’ Such an analysis may not be possible to be repeated by others who have differing traditions and paradigms; therefore, disclosure (Gubrium and Holstein 1997) of the researcher’s biases and philosophical approaches is important. In contrast, other experts recommend that the coding process involve a team of researchers with differing backgrounds (Denzin 1978; Mays and Pope 1995; Patton 1999; Pope, Ziebland, and Mays 2000) to improve the breadth and depth of the analysis and subsequent findings. Cross-training is important in the use of such teams. Developing the Code Structure The development of the code structure is an iterative and lengthy process, which begins in the data collection phase. There is substantial diversity in how to develop the code structure. This debate (Glaser 1992; Heath and Cowley 2004) centers on whether coding should be more inductive or more deductive. Regardless of approach, a well-crafted, clear, and comprehensive code structure promotes the quality of subsequent analysis (Miles and Huberman 1994). Grounded Theory Approach to Developing Code Structure For grounded theorists, the recommended approach to developing a set of codes is purely inductive. This approach limits researchers from erroneously ‘‘forcing’’ a preconceived result (Glaser 1992). Data are reviewed line by line in detail and as a concept becomes apparent, a code is assigned. Upon further review of data, the analyst continues to assign codes that reflect the concepts that emerge, highlighting and coding lines, paragraphs, or segments that illustrate the chosen concept. As more data are reviewed, the specifications of codes are developed and refined to fit the data. To ascertain whether a code is appropriately assigned, the analyst compares text segments to segments that have been previously assigned the same code and decides whether they reflect the same concept. Using this ‘‘constant comparison’’ method (Glaser and Strauss 1967), the researchers refine dimensions of existing codes and identify new codes. Through this process, the code structure evolves inductively, reflecting ‘‘the ground,’’ i.e., the experiences of participants. More Deductive Approaches to Developing Code Structure Some qualitative research experts (Miles and Huberman 1994) describe a more deductive approach, which starts with an organizing framework for the Qualitative Data Analysis for Health Services Research 1763 Table 2: Code Types and Applications Code Types Characterization Conceptual codes/subcodes Key conceptual domains and essential conceptual dimensions of the domains Relationship codes Links among conceptual codes/subcodes Participant perspective Directional views (positive, negative, or indifferent) of participants Participant characteristics Characteristics that identify participants, such as age, gender, insurance type, socioeconomic status, etc. Setting codes Characteristics that identify settings, such as intervention versus nonintervention group, fee-for-service versus prepaid insurance, etc. Application/Purpose Developing taxonomies; useful in themes and theory Generating themes and theory Generating themes and theory Comparing key concepts across types of participants Comparing key concepts across types of settings codes. In this approach, the initial step defines a structure of initial codes before line-by-line review of the data. Preliminary codes can help researchers integrate concepts already well known in the extant literature. For example, a deductive approach of health service use might begin with predetermined codes for predisposing, enabling, and need factors based on the behavioral model (Andersen 1995). Great care must be taken to avoid forcing data into these categories because a code exists for them; however such a ‘‘start list’’ (Miles and Huberman 1994) does allow new inquiries to benefit from and build on previous insights in the field. An Integrated Approach to Developing Code Structure An integrated approach employs both inductive (ground-up) development of codes as well as a deductive organizing framework for code types (start list). Previous researchers have identified various code types (Lofland 1971; Lincoln and Guba 1985; Strauss and Corbin 1990; Miles and Huberman 1994); however, five code types (Table 2) are helpful in generating taxonomy, themes, and theory, all of which have practical relevance for health services research. These code types are (1) conceptual codes and subcodes identifying key concept domains and essential dimensions of these concept domains, (2) relationship codes identifying links between other concepts coded with conceptual 1764 HSR: Health Services Research 42:4 (August 2007) codes, (3) participant perspective codes, which identify if the participant is positive, negative, or indifferent about a particular experience or part of an experience, (4) participant characteristic codes, and (5) setting codes. Finalizing and Applying the Code Structure The codes and code structure can be considered finalized at the point of theoretical saturation (Glaser and Strauss 1967; Glaser 1992; Patton 2002). This is the point at which no new concepts emerge from reviewing of successive data from a theoretically sensitive sample of participants, i.e., a sample that is diverse in pertinent characteristics and experiences. Theoretical saturation will take longer to accomplish for more multifaceted areas of inquiry with greater diversity among participants. If, during analysis, a conceptual gap is identified, the researcher should expand the sample to continue data collection to clarify and refine emerging concepts and codes. For instance, if an observation or interview elicits information about a concept that has not been heard or that contradicts previous understandings, the researchers should expand the sample to include participants and experiences to understand this new concept more fully. This use of the codes to guide data collection is known as theoretical sampling and is central to conducting qualitative research. Applying the Finalized Code Structure The application of the finalized code structure to the data is an important step of analysis. One approach to applying the finalized code structure to the data is to have two to three members of the research team re-review all the data, applying independently the codes from the finalized code structure. Then, the team meets in a group to review discrepancies, resolving differences by indepth discussion and negotiated consensus. The result is a single, agreed upon application of the final codes to all parts of the data. This approach is reasonable and frequently used in the published literature. Another approach to applying the finalized code structure is to establish the reliability of multiple coders from the research team with a selected group of data. Once coders have been established to be reliable with one another, one of the coders completes the remainder of the coding independently. This approach can be more time efficient than the approach that requires the multiple coders to recode all data with the final code structure and then resolve disagreement by joint consensus. Intercoder reliability (Miles and Huberman 1994) can be evaluated by selecting new data (for instance, two to three transcripts that were not analyzed as part of the code development phase before theoretical saturation) and Qualitative Data Analysis for Health Services Research 1765 having two researchers code these data, using the finalized code structure. The two researchers code the transcripts independently and compare the agreement on coding used. One calculates the percentage of all segments coded, which are coded with the same codes, and some experts (Miles and Huberman 1994) have proposed 80 percent agreement as a rule of thumb for reasonable reliability. The approach in each of the steps of qualitative data analysis reflects a balance of differing views among researchers. Formality, including quantifying intercoder reliability, may improve the ability of those less trained in qualitative methods to understand and value evidence generated from qualitative studies. However, overly mechanistic approaches or reliance on inexperienced qualitative analysts may dampen the insights from qualitative research (Morgan 1997). Formal rules and processes should not replace analytic thought itself. In any project, if the codes are not conceptually rich and are oversimplified in their separation from the context of their occurrence, the insights from the inquiry will be limited. GENERATING RESULTS Overview We focus on three types of output from qualitative studies——taxonomy, themes, and theory. These outputs can be helpful in a number of ways including, but not limited to, the fostering of improved measurement of multifaceted interventions; the generation of hypotheses about causal links among service quality, cost, or access; and the revealing of insights into how the context of an events might influence various health-related outcomes. Taxonomy Taxonomy is a system for classifying multifaceted, complex phenomena according to common conceptual domains and dimensions. In health services research, we are often evaluating multifaceted interventions, implemented in the real world rather than controlled conditions. Qualitative methods provide a sophisticated approach to specifying the complexity rather than simple dichotomous characterizations of interventions (i.e., treatment versus control) common in quantitative research (Sofaer 1999). Furthermore, a common language or taxonomy that distills complex interventions into their essential components is paramount to comparing alternative interventions and promoting clear communication. Examples of taxonomy include classification 1766 HSR: Health Services Research 42:4 (August 2007) systems for health maintenance organizations (Welch, Hillman, and Pauly 1990), integrated health systems (Gillies et al. 1993; Bazzoli et al. 1999), goalsetting for older adults with dementia (Bogardus, Bradley, and Tinetti 1998), and quality improvement efforts in the hospital setting (Bradley et al. 2001). How does one move from the phase of applying the finalized code structure to generating and reporting taxonomy? If one has applied the code types as described above, then the structure of the taxonomy will mirror closely the conceptual codes and subcodes. Conceptual codes define key domains that characterize the phenomenon; conceptual subcodes define common dimensions within those key domains. Within each dimension, there may be further subdimensions depending on the complexity of the inquiry. Importantly, taxonomies identify domains and dimensions that are broad in nature. For example, in a taxonomy classifying quality improvement (Bradley et al. 2001), we defined six domains that comprise quality improvement efforts in the hospital setting: organizational goals, administrative support, clinician leadership, performance improvement initiatives, use of data, and contextual factors. Within the domain of organizational goals, there were four dimensions (i.e., content, specificity, challenge, sharedness of the goals). For each domain and dimension, the code represents the abstract concept, not the specific statement about that concept. For instance, a domain might be ‘‘nursing leadership,’’ as opposed to the statement, ‘‘there is strong nursing leadership here.’’ The difference is important to recognize as taxonomies describe a discrete set of axes or domains that characterize multifaceted phenomena. Themes Themes are general propositions that emerge from diverse and detail-rich experiences of participants and provide recurrent and unifying ideas regarding the subject of inquiry. Themes typically evolve not only from the conceptual codes and subcodes as in the case of taxonomy but also from the relationship codes, which tag data that link concepts to each other. For example, as in a study of health services integration (Gillies et al. 1993), three concepts were identified that might form a taxonomy of integration approaches: functional integration, physician integration, and clinical integration. However, the study also suggests that clinical integration requires success in function and, ideally, physician integration before full clinical integration can be achieved. This latter statement might be called a theme, a statement or proposition about how health system integration proceeds. The statement does more than just identify conceptual domains; it also suggests a relationship Qualitative Data Analysis for Health Services Research 1767 among the concepts. Similarly, a study of managing a safety-net emergency department (Dohan 2002) identified themes of patients using the emergency department for relief from social, not health, problems and the extreme financial stress that is part of every day in the department. The study also revealed how these tensions were managed, i.e., by defining patients as ‘‘interesting cases’’ and fostering an organizational obligation to provide uncompensated care. Another approach to developing themes is to conduct a comparative analysis of concepts coded in different participant groups or setting codes. The researcher retrieves data coded with both a conceptual or relationship code and with a participant characteristic code (e.g., fee-for-service Medicare versus traditional Medicare). The comparison can assess whether certain concepts, relationships among concepts, or positive/negative perspectives are more apparent or are experienced differently in one group than in another. These kinds of comparisons are sometimes performed informally by researchers reading and comparing statements and observations; however, formal mechanisms including the use of truth tables (Ragin 1987, 1999) and explanatory effects matrices (Miles and Huberman 1994) to catalogue the presence of selected concepts among comparisons groups have also been implemented. Theory Theory emphasizes the nature of correlative or causal relationships, often delving into the systematic reasons for the events, experiences, and phenomena of inquiry. Theory predicts and explains phenomena (Kaplan 1964; Merton 1967; Weick 1995). Data tagged by relationship codes are essential to generating and reporting theory. A comprehensive theory will integrate data tagged with conceptual codes and subcodes as well as with relationship and perspective codes. Comparative analysis about group-specific differences is also sometimes used to develop theory. Theory development can be less bewildering with consistent cataloguing of relationships among concepts, using the constant comparison method to generate inductively conceptual codes and subcodes as well as relationship codes. The process for developing theory is, nonetheless, diverse depending on the subject, the context, and the experience of the researcher. Illustrating theory development, a study of barriers to pediatric health care (Sobo, Seid, and Reyes Gelhard 2006), parents identified a set of six barriers that can limit access and use of critical pediatric services. The study then linked these barriers into a theory about the interaction of necessary skills and 1768 HSR: Health Services Research 42:4 (August 2007) prerequisites, realization of access, the site of care, and parent/patient outcomes. Through its theoretical development, the study also suggests a new paradigm for understanding the biomedical health care system, likening it to a cultural system in which parents and patients needed to learn (or be acculturated) to function competently. CONCLUSION Qualitative research methodologies can generate rich information about health care including, but not limited to, patient preferences, medical decision making, culturally determined values and health beliefs, consumer satisfaction, health-seeking behaviors, and health disparities. Furthermore, qualitative methods can reveal critical insights to inform development, translation, and dissemination of interventions to address health system shortcomings. A clear understanding of such methodologies can help the field adopt and integrate qualitative approaches when they are appropriate. Taxonomies, themes, and theory produced with rigorous qualitative methods can be particularly useful in health services research. Taxonomies improve our description and hence, measurement and evaluation, of real-world phenomena by allowing for multiple domains and dimensions of multifaceted interventions. Themes and theory guide our research to explain and predict various outcomes within diverse contexts of the health care system. In this paper, we highlight an integrated approach to qualitative data analysis, which applies the principles of inductive reasoning and the constant comparison method (Glaser and Strauss 1967) while employing predetermined code types (conceptual, relationship, perspective, participant characteristics, and setting codes) to analyze data. A vast body of methodological work conducted over decades has produced impressive innovation and advancement in qualitative research techniques. This paper has sought to translate qualitative data analysis strategies and approaches from this methodological literature to enhance their accessibility and use for improving health services research. ACKNOWLEDGMENTS Dr. Bradley is supported by the Patrick and Catherine Weldon Donaghue Medical Research Foundation and the Claude D. 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Yardley, L. 2000. ‘‘Dilemmas in Qualitative Health Research.’’ Psychology and Health 15: 215–28. Yin, R. K. 1994. Case Study Research: Design and Methods, 2d Edition. Thousand Oaks, CA: Sage Publications. Eide et al. BMC International Health and Human Rights (2018) 18:26 https://doi.org/10.1186/s12914-018-0166-2 TECHNICAL ADVANCE Open Access Combining survey data, GIS and qualitative interviews in the analysis of health service access for persons with disabilities Arne H. Eide1* , Karin Dyrstad2, Alister Munthali3, Gert Van Rooy4, Stine H. Braathen1, Thomas Halvorsen5, Frans Persendt6, Peter Mvula3 and Jan Ketil Rød7 Abstract Background: Equitable access to health services is a key ingredient in reaching health for persons with disabilities and other vulnerable groups. So far, research on access to health services in low- and middle-income countries has largely relied on self-reported survey data. Realizing that there may be substantial discrepancies between perceived and actual access, other methods are needed for more precise knowledge to guide health policy and planning. The objective of this article is to describe and discuss an innovative methodological triangulation where statistical and spatial analysis of perceived distance and objective measures of access is combined with qualitative evidence. Methods: The data for the study was drawn from a large household and individual questionnaire based survey carried out in Namibia and Malawi. The survey data was combined with spatial data of respondents and health facilities, key informant interviews and focus group discussions. To analyse access and barriers to access, a model is developed that takes into account both measured and perceived access. The geo-referenced survey data is used to establish four outcome categories of perceived and measured access as either good or poor. Combined with analyses of the terrain and the actual distance from where the respondents live to the health facility they go to, the data allows for categorising areas and respondents according to the four outcome categories. The four groups are subsequently analysed with respect to variation in individual characteristics and vulnerability factors. The qualitative component includes participatory map drawing and is used to gain further insight into the mechanisms behind the different combinations of perceived and actual access. Results: Preliminary results show that there are substantial discrepancies between perceived and actual access to health services and the qualitative study provides insight into mechanisms behind such divergences. Conclusion: The novel combination of survey data, geographical data and qualitative data will generate a model on access to health services in poor contexts that will feed into efforts to improve access for the most vulnerable people in underserved areas. Keywords: Health services, Vulnerable groups, Access, Low-income countries, Combining methods, Survey, Qualitative study, Geographical data * Correspondence: [email protected] 1 SINTEF, Department of Health, P.B.124, N-0314 Oslo, Norway Full list of author information is available at the end of the article © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Eide et al. BMC International Health and Human Rights (2018) 18:26 Background The human right to health [1] for all is enshrined in a number of international policy documents, including the Alma Ata Declaration [2], the 2030 Agenda for Sustainable Development [3], and the Convention on the Rights of Persons with Disabilities [4]. Equitable access to health services is a key ingredient in reaching health for persons with disabilities and other vulnerable groups. Nevertheless, existing research on access to health services for vulnerable groups in low- and middle-income countries is rather scattered, largely self-reported survey data, and to some extent inconclusive. The purpose of this article is to describe and discuss an innovative methodological triangulation where statistical and spatial analysis of perceived distance and objective measures of access is combined with qualitative evidence from carefully selected areas. We aim to show that this approach provides a potentially stronger fundament for assessing access to health services in low-income contexts and yields new insight into lack of access, including the basis for individual perception of access. In our study, we focus explicitly on vulnerable groups, particularly people with disabilities. However, the approach can easily be adapted to other user groups. There is an increasing recognition that access to health services and vulnerability vary geographically and individually. Where you live, who you are and with whom you live determine your access to health services. Although the interplay between physical environment and individual characteristics influences people’s access to health care, it is rarely taken explicitly into account in the theoretical framework or empirical design. Whether a physical factor, such as poor roads or long distance to nearest health facility, actually represents a barrier to access, depends on income, family situation and health. Thus, what merely constitutes an inconvenience for one person could represent an insurmountable barrier for someone else [5]. Arguably, the moderating factor here is vulnerability; local barriers are accentuated by individual characteristics that either alleviate or exacerbate access problems [6–9]. Therefore, we argue that to understand barriers to access, one should identify circumstances that lead to particularly poor access, i.e., situations where the combination of individual and contextual factors impedes access, explicitly taking the interplay between individual characteristics and people’s local environment into account. A handful of case studies by the World Health Organization (WHO) illustrate the usefulness of a spatial approach to access [10, 11]. In comparison, our approach also incorporates the subjective element of perceived access. The studies by Trani et al. in Sierra Leone and Afghanistan [12, 13] have shown that there is a substantial gap between the perceived need for and received health Page 2 of 8 services, and that persons with disabilities are disadvantaged regarding access when compared to non-disabled individuals. However, much of the literature has concentrated on identifying barriers, such as Van Rooy et al. [9] in Namibia, Vergunst et al. in rural South Africa [14], Munthali et al. [15] in Malawi, Eide et al. [16] and Visagie et al. [17] in four sub-Saharan countries. These studies and others confirm that there exists a range of environmental barriers to accessing health care and that a substantial number of persons with disabilities in low-income countries who need health services do not access it. This is a serious challenge in an equity perspective. To understand barriers to access, one should identify circumstances that lead to particularly poor access, i.e. situations where the combination of individual and contextual factors impedes access, explicitly taking the interplay between individual characteristics and people’s local environment into account. While existing research is relatively congruent, few studies in low-income contexts have applied more objective measures such as distance, time of travel, topography and other environmental characteristics [18–22]. Accessibility to health services Accessibility to health services is a fuzzy term with a range of different definitions [23]. Access to health services is affected by contextual-, cultural-, community-, health service-, and individual level- characteristics as well as an interaction of these [24]. Access can further be split into components like availability, affordability, acceptability, adequacy, and accessibilty [25]. Access can, and mostly has been, measured as perceived access, through self-reported and subjective experience of access [26]. Access can also be measured as actual access, through directly observable dimensions like time and distance. Within health geography, spatial or geographical accessibility can be understood as the spatial separation of the population and the supply of health care facilities [11]. The other, non-spatial components to health access (affordability, appropriateness and accommodation) are commonly excluded from GIS (Geographical Information Systems) -based analysis. Comparing geographical accessibility with perceived access and barriers to access can shed light on the strengths and limitations of different measurement as well as providing new insights into variations in the individual experiences of access to health care [11]. Ultimately, this knowledge can be used for improved planning and policy development. Access to healthcare for vulnerable people in resource poor settings in Africa While the World Disability Report (WDR) [27] states that a large number of individuals with disabilities do Eide et al. BMC International Health and Human Rights (2018) 18:26 not have access to health and other services (page xxi) and are denied equal access to health care (page 9), evidence is limited and mostly based on subjective measures. Data on access to health care in the 2002–2003 World Health Survey, which is a main source for WDR, is entirely based on individual perception of access. This is the case also for other studies, such as SINTEF’s studies on living conditions among persons with disabilities in low-income contexts (see e.g. [28]), and the Equitable study in four sub-Saharan countries [16, 17]. According to Eide et al. [16], lack of transport, poor availability of services, inadequate drugs or equipment, and cost of the visit are the four most pronounced barriers in both Namibia and Malawi. The research project EquitAble (Enabling universal and equitable access to healthcare for vulnerable people in resource poor settings in Africa) was carried out over the period 2009–2012 by an international research consortium studying access to health services for vulnerable groups, including persons with disabilities. A comprehensive household survey was carried out in four African countries. In two of them, Namibia and Malawi, the data collection also included the GPS (Global Positioning System) coordinates of the sampled households. The sample comprises 1624 individuals in Namibia and 1526 individuals in Malawi, of which around 50 % were screened and identified as persons with disabilities using the Washington Group screening questions [29]. The selection of study sites in each country aimed at including populations with different characteristics and at the same time highlighting country specific characteristics, in this case a highly dispersed population (Namibia) and a population living in chronic poverty and with a high disease burden (Malawi). In Namibia, selected sites were Khomas (Central region), Hardap (south), Omusati (north), Kunene (north-west), and Caprivi (far north east). In Malawi, the four sites were Blantyre and Phalombe district (Southern region), Ntchisi (Central region) and Rumphi (Northern region). Clusters within each site were defined by the country teams based on the predefined characteristics as well as practical considerations. EquitAble developed a framework for analysing human rights and inclusion in health policies [30], which led to the identification of a range of vulnerability factors that may influence on individual capabilities and ability to access services, including disability, ethnic minorities, female-headed households, limited resources (poverty), increased relative risk for morbidity, mother child mortality, children with special needs, aged, youth, displaced populations, living away from services, and suffering from chronic illness. By means of GPS coordinates and available geographical data, this provides an opportunity to analyse the relationship between perceived access and measured access and how this is influenced by vulnerability factors. Page 3 of 8 Building on EquitAble,1 an independent follow-up project, GeoHealthAccess2 – The geography of vulnerability and health service access in southern Africa – has set out to expand the empirical approach for access to healthcare by combining the geo-referenced survey data on perceived access, with geographical features like distance and travel time to health facilities. Notably, the project will combine quantitative analysis with qualitative data collection and analysis, where the selection of study sites for the qualitative data collection is guided by the quantitative analysis. The qualitative case studies serve to explore potential divergence between subjective and more objective measures of access. Health geography Traditionally, there has been a divide between health geography and medical geography where the former uses qualitative approaches (as well as elaborating on social theories) while the latter uses quantitative methods (including GIS) and, largely, being atheoretical [31]. An emerging trend in health geography is to apply GIS in health promotion, planning and evaluation of health systems [32, 33]. A growing literature is devoted to physical access and how it should be measured, and complex models of spatial accessibility have been developed [32, 34, 35]. The suitability of GIS in improving health care in Africa and other parts of the world was pointed out almost a decade ago [36, 37]. Yet, despite the obvious inherent advantages in using GIS to map and plan access at the micro level, the application of these methods in Africa has been limited [37], in particular relative to Europe [38]. A recent literature review from South Africa found that health geography is still a limited research field [39], and there is little reason to believe that the progress has been more substantial in other African contexts. Traditionally, only GIS specialists have been able to model physical access to health care services, but this is being democratized with the general availability of GIS tools and georeferenced health care data. WHO has supported and financed this development, and made modelling of physical accessibility more available by the tool AccessMod [40]. Over the past decades, geographical research has taken on new ways, incorporating a triangulation of methods and knowledge, using both cartographic and non-cartographic information, such as surveys, epidemiological and qualitative data [39]. This approach is in line with the way the public health field engages with geography and space as described by Gatrell and Elliott [41]: Modern public health sees the environment as social and psychological, not merely as physical. In this sense, then, “environment” and “place” converge to provide a spatial context for health that transcends Eide et al. BMC International Health and Human Rights (2018) 18:26 Page 4 of 8 the individual’s own behaviour and health outcomes (p. 15). facilities. GPS coordinates of households were recorded during data collection in Namibia and Malawi. Additionally, we will carry out qualitative studies to explore divergence between the survey and geographical data. During the first stage of GeoHealthAccess, we supplemented the household coordinates with the coordinates of the health clinics that each household uses (obtained from Ministry of Health). Together, these data form the basis for developing GIS-based measures of access. In the second stage, we use a statistical model of perceived access that combines the survey data and geographical data to identify areas where there are significant clustering of households with divergence in perceived access and GIS measured access. In the third stage, we revisit a strategic sample of these clusters to collect additional data that can explain such divergences. Here we conduct qualitative interviews and apply participatory GIS techniques. Geography and perception Within healthcare geography, several studies recognize the importance of including non-spatial factors, like demographics and socioeconomic status which influence access [33]. So far, this has largely implied aggregation of individual or household level characteristics to units or clusters. Despite several studies illustrating the usefulness of a GIS approach to health care [42–45], GIS applications to the study of health care and health outcomes in Africa remain limited. In addition, a weakness in most of them is that they do not take people’s perception of access into account. One exception is Manguno [46], who combined physical distance to the nearest immunization centre, with mothers’ perceptions of distance as determinants of child immunization in Nigeria, where perception of distance turned out to be a more robust determinant of access than actual distance. This finding highlights the need to combine more objective measures of accessibility with people’s perceptions of access to health care. Furthermore, a weakness in the existing literature on individuals’ perception of access to health services in low-income contexts is the lack of data and in particular representative data that can be used to analyse accessibility more broadly. The complexity of measuring access may have influenced researchers to utilize barriers as a proxy to access. However, as pointed out by Fortney et al. [26], perceived access may be equally valid as a measure of access than more objective measures based on characteristics of health care systems. Individuals’ own interpretation of access, whether based on own or others’ experiences, knowledge, prevailing attitudes or other social or individual factors may influence on individuals’ health seeking behaviour and for instance whether to access health care services or not. Methods A mixed methods design: combing survey data on perceived access, GIS-based geographical measures and qualitative interviews Building on these insights, GeoHealthAccess will develop and test a model of access to health care services for persons with disabilities in resource poor settings. The model combines physical accessibility with perceptions of access, individual level characteristics and vulnerability factors. The main hypothesis is that there is a relationship between local context and access to health care, which is strengthened or mediated by vulnerability factors at the individual level. The project combines EquitAble survey data on perceived access to health care with geographical data on distance and travel time to health Empirical approach The EquitAble household survey included data on demographics, perceived barriers to access health services and GPS coordinates of households. The novelty of GeoHealthAccess is the combination of EquitAble data at both household and individual level with additional geographical data and new qualitative data. To analyse access and barriers to access, GeoHealthAccess developed a model that takes into account both measured and perceived accessibility (using data from EquitAble). Table 1 presents possible outcomes of convergence and divergence between measured accessibility and perceived access. Outcome A and D in Table 1 represent full convergence of measured and perceived access, whereas C and D represent full divergence. The last two are theoretically interesting and represent an opportunity to explore the relationship between perceived and measured access. For instance, analysing differences in perceptions of access in outcomes C and B may reveal variation in individual characteristics and vulnerability factors that can drive the divergence in different directions. To classify outcomes, we combined the geo-referenced EquitAble survey data with spatial features. The survey data comprised a series of questions about access and barriers to health services, socio-demographic variables as well as vulnerability factors that will be utilised to calculate perceived distance. The relationship between the need for and actual access to health services was measured in two different ways: i) a direct question on whether access was achieved the last time the respondents needed health services, and ii) a series of questions on awareness, need and access which can be used to calculate gaps in health services. These variables established whether perceived access was good or poor. Combined with an analysis of the terrain and the actual distance from where the respondents live to the health facility Eide et al. BMC International Health and Human Rights (2018) 18:26 Page 5 of 8 Table 1 Possible outcomes of perceived and measured access Perceived access Measured access Good Poor A B C D Good Poor they go to, the data allows us to categorise areas and respondents according to the classification in Table 1. The four groups will then be analysed with respect to variation in individual characteristics and vulnerability factors. This will indicate underlying causes of discrepancies between perceived and measured access. While the table presents the four categories as dichotomous, in reality they are continuous phenomena that will add to the complexity of the analyses. By using various statistical techniques such as spatial regression analysis, structural equation modelling and multilevel analysis, we will be able to test preliminary models of access to health care. Qualitative research methods will be utilized to gain more in-depth data on the causes of divergence (outcomes B and C in Table 1), and to better understand how a combination of individual and contextual factors can ameliorate or aggravate access to health care. Focus Group Discussions (FGDs) and Key Informant Interviews (KIIs) will be used to collect data among health workers, other centrally placed professionals and decision makers and health service users with one or more of the vulnerability characteristics described above. The qualitative component of the study will draw on experiences from participatory GIS [47], where the starting point for the discussion is one or more maps created for the purpose. In our case, one such starting point will be a map visualising different outcomes of physical access and perceived access (Fig. 2). However, we expect that map-literacy will be low among many of our informants, so we will use a combination of map-drawing (Fig. 1) and interpretation of printed maps. When using qualitative methods in health geography, the focus is less on measurement, and more on “the interpretation and understanding of ill-health, disease and disability in the context of place” ( [48], p. 100). This data is essential in order to explore and support the quantitative and cartographic data [41]. preliminary analyses of a combination of Equitable survey data, GPS coordinates of households and health facilities. The map shows households selected in the Rumphi district in northern Malawi. It identifies clusters of households that are classified within the categories B and C from Table 1 above, i.e., households where the self-reported access is worse than average, but measured access is better than average (measured access is better than perceived access; B), and vice versa (measured access is worse than perceived access; C). The clusters were identified by regressing age, health status and household distance to health facility on perceived access. The predicted residuals from this regression were then used to categorise the households into categories A-D. Going ahead, we will also want to explain the variation in other relevant dependent variables including (but are not limited to) local prevalence of disability and barriers to access. Statistical techniques include traditional regression analysis, but also more explicitly spatial modelling tools such as spatial regression analysis and spatial clustering techniques based on Getis-Ord Gi* local statistics. The latter technique will enable us to determine An illustration of the approach Preliminary analyses from Malawi illustrated the potential of this empirical approach. Figure 2 is based on Fig. 1 Participatory GIS/map drawing, Malawi 2017. Photo: Stine H Braathen, SINTEF Eide et al. BMC International Health and Human Rights (2018) 18:26 where there are significant B and C category clusters of households (Fig. 2). According to the map, there are households, particularly in the southern part of the region, that are located very close to a health centre, and yet report of poor access. Similarly, we find that whether transport represents a barrier or not vary widely among individuals living in the same area and within the same distance from the road and the facilities. Within small geographical areas where households are close to each other and distance to the nearest health facility is the same for everyone, that lack of transport as a barrier ranges from “insurmountable problem” to “no problem” among those who live in this village. This indicates a variation in access Page 6 of 8 that we assume stems from variation in personal and socio-demographic characteristics in this population. Discussion A mix of individual and contextual characteristics could help explain the observed variations in access. For example, income and personal network may influence available modes of transportation. For a person with relatively good health, having to walk to get to the nearest road might not be an obstacle, while for a person with disabilities or chronic illness, it could effectively deter access. Hence, the interaction between individual and contextual characteristics should be taken into account, as individual factors of vulnerability may moderate or mediate the Fig. 2 Classification of households based on perceived and measured access, Rumphi, Malawi. Note: B and C refer to classification in Table 1 (divergence between perceived and measured access) Eide et al. BMC International Health and Human Rights (2018) 18:26 impact of physical barriers on access, and vice versa. The novel combination of survey data, geographical data and qualitative exploratory data in GeoHealthAccess will generate a model on access to health services in poor contexts that will feed into efforts to improve access for the most vulnerable people in the most underserved areas of the world. Both the specification of “barrier hotspots” where both perceived and measured access is poor, identification of areas with large divergence between perceived and actual access, and identifying the impact of individual and household vulnerability factors such as disability on access has the potential to guide the targeting of resource input such as out-reach health services. In order to ensuring utilization of the findings of this study, the consortium will present the results to officials in the MoH in Malawi and Namibia. Conclusion Given the large number of people who die or become disabled from largely preventable diseases in Africa, as seen in low life expectancy and high level of child mortality and child disability, there is an urgent need to increase access to basic health services, in particular among the most vulnerable groups. GeoHealthAccess will provide knowledge that can be applied to developing efficient health policies that target barriers to access both at the community level and at the individual level, improving access where it is most needed. Consortium GeoHealthAccess is a collaborative project between SINTEF Technology and Society, University of Malawi, University of Namibia, and Norwegian University of Science and Technology. Stellenbosch University, South Africa, University of Cambridge and University of East Anglia, UK, Trinity College Dublin and University College Dublin, Ireland, and Peace Research Institute Oslo, Norway, are all involved as advisors to the project. The project is funded by the Research Council of Norway, while EquitAble was funded by EU/FP (European Union/Framework Program) 7. Endnotes 1 www.Equitableproject.org. 2 https://www.sintef.no/en/projects/ geohealthaccess-the-geography-of-vulnerability-and/ Abbreviations EU: European Union; FGD: Focus group discussion; FP: Framework program; GIS: Geographical information systems; GPS: Global positioning system; KII: Key informant interview; WDR: World Disability Report; WHO: World Health Organization Funding Funding to GeoHealthAccess was obtained from the Norwegian Research Council. Equitable was funded by the European Union FP7 Health. None of Page 7 of 8 these funding bodies have had any role whatsoever in designing of the study and collection, analysis, and interpretation of data and in writing the manuscript. Availability of data and materials The datasets used for the current publication are available from the corresponding author on reasonable request. Upon completion of the project, data will be available through the Norwegian Centre for Research Data (http://www.nsd.uib.no/nsd/english/index.html). A statement on data availability has been included in the manuscript. Authors’ contributions KD, SHB, AHE, AM, GVR, JKR and TH were all involved in development of the project. AHE initiated and led the writing of the manuscript. All authors were actively involved in developing the design for the study. AM, PM, GVR, FP and SHB were all involved in planning and implementing the qualitative data collection. AM, PM, GVR, JKR, TH and PM were responsible for obtaining georeferenced data of health facilities and combine the survey and geographical data for analyses. JKR, TH and KD have done the initial analyses. All authors read several versions of the manuscript and approved the submitted version. Ethics approval and consent to participate The current study (GeoHealthAccess) draws on existing survey data from the project EquitAble. Ethical clearance for Equitable was obtained from Health Research Ethics Committee, Stellenbosch University (South Africa), Office of the Permanent Secretary, Ministry of Health and Social Services (Namibia), the National Health Sciences Research Committee (Malawi), and the Norwegian Social Science Data Services. The study obtained oral consent from all respondents. GeoHealthAccess was approved by: The National Commission for Science and Technology in Malawi (Ref No NCST/RTT/2/6, date: 20th October 2017. Ministry of Health and Social Services in Namibia (Ref 17/3/3 GV), date: 29th June 2017. Norwegian Centre for Research Data (Ref. 53903), Norway, date: 22nd May 2017. Consent for publication The purpose of the study and use of data for scientific publication was included in the standard information provided when oral consent was given. This concerns both survey data (Equitable) and qualitative data collected in GeoHealthAccess. Both survey data and qualitative data are anonymized, including also the image used in the current text. Competing interests The authors declare that they have no competing interests. Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Author details 1 SINTEF, Department of Health, P.B.124, N-0314 Oslo, Norway. 2Department of Sociology and Political Science, Norwegian University of Science and Technology, NO-7491 Trondheim, Norway. 3Centre for Social Research, University of Malawi, P.O. Box 280, Zomba, Malawi. 4Multidisciplinary Research Centre, University of Namibia, P. B. 13301, Windhoek, Namibia. 5 SINTEF, Department of Health, P.B. 4760, Torgarden, N-7465 Trondheim, Norway. 6Department of Geography, History and Environmental Studies, University of Namibia, P.B. 13301, Windhoek, Namibia. 7Department of Geography, Norwegian University of Science and Technology, NO-7491 Trondheim, Norway. 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