In 2007, the periodical, Social Science & Medicine (one of the leading journals in community health science) published a special edition on the complexities of studying community health--65, Nov 2007, starting page 1281.
The theme of the special edition was PLACING HEALTH IN CONTEXT. As the editors of this edition, James Dunn and Steve Cummins state, "While there is a long history of interest in place and health in the geography of health, in the past decade or more a number of disciplines have witnessed an increasing interest in the ‘effect’ that attributes of collective social organization and the local built environment at neighbourhood scale have on a variety of social outcomes, including health, health behaviours, early child development, youth delinquency, crime and deviance, political behaviour, employment outcomes and other economic opportunities" (p. 1821).
While Dunn and Cummins agree that significant advances resulted from the research surrounding the community-as-context model (see earlier post), there is much still to be done. Put simply by me (and I do mean simply), the community-as-context model needs to be replaced by the community-as-complex-system model. That is not quite what they say, but it works for a general sense of the articles. The community-as-context needs to get sophisticated; as it stands currently, it lacks the theoretical and methodological rigor to get the job done.
As data for my statement, here, for example, is a quote from Dunn and Cummins toward the end of their editorial overview: "The collection of papers presented here that sow the seeds of debate, for example, on the role of neighbourhood preference in understanding associations between context and health, is a potential lightning rod. Similarly, the use of complexity theory, given its novelty and its dissimilarity to the conventional ‘black box’ approach of investigating the effects of interventions should also spark responses in the literature. All of the papers in this Special Issue point us in compelling new directions for research that places health in context. We hope that this special issue sparks debate and new lines of inquiry and look forward to its future repercussions" (p. 1821).
The list of authors that Dunn and Cummins draw upon is impressive. The arguements made by these authors is even more incredible. Agree with them or not, you need to read this special edition and consider the arguments its authors make!
22/05/2009
Interview with David Byrne
The following is a brief interview I conducted with British Sociologist and Complexity Scientist, David Byrne.
Dr. Byrne is Professor in the School of Applied Social Sciences at Durham University, England, where he is also Director of Postgraduate Studies. Dr. Byrne is the author of several books and a long list of articles, including his 1998 book, Complexity Theory and the Social Sciences--the first book to critically review and explore the application of complexity science to sociological inquiry. His most recent book, edited with noted sociologist and methodologist, Charles Ragin is The SAGE Handbook of Case-Based Methods
Dr. Byrne is an expert in methods, urban planning, community health, social policy, social exclusion and complexity science.
-----------------------------------------------
INTERVIEW WITH DR. BYRNE
CASTELLANI: Dr. Byrne, thanks so much for taking the time to do this interview. Your research agenda is rather vast in its scope—ranging from the philosophy of complexity science to method to urban planning to health care inequality. If you do not mind, I am going to narrow in on method first, given its wider implications for those reading this blog—most of whom are students and researchers new to the field of complexity science and its practice within sociology.
A. Case-Based Research
CASTELLANI: For the last several years, you have been a major advocate of a case-based approach to research. You specifically endorse what you and Charles Ragin refer to as Qualitative Comparative Analysis (QCA). First, how do you define QCA?
BYRNE: It is a method which is ‘set theoretic’ i.e. it understands causal relations in the social world in terms of relationships in combination – sets, rather than the unique contribution of single variables. It is based on systematic comparison – essentially an extension of John Stuart Mills’ method of differences. It requires careful qualitative engagement with specific cases as the foundation of that comparison.
CASTELLANI: Of the three major types of QCA (crisp-set, multi-value and fuzzy set), which do you find most useful and why? Or, do you approach the distinctions within QCA a different way?
BYRNE: I generally work with crisp set techniques and actually almost never go beyond the truth table. So I use QCA as a kind of mix of exploratory / explanatory – often focusing on ‘contradictory configurations’ in which the assemblage of elements in the line of the truth table – the configuration – generates different outcomes. That makes me look at those cases for what else is different about them. I see multi-value QCA as an extension of crisp set but it is much more complex to use. I frequently use Cluster Analysis as a data reduction technique and binarize membership of a cluster. Fuzzy set is very interesting and I have thought about how we might use distance from a cluster centre as a fuzzying principle but I have never managed to bring it off.
CASTELLANI: For researchers and graduate students new to case-based research, what is your best argument (apologetic) for including QCA in their toolbox of techniques?
BYRNE: For me the crucial things about QCA are the following:
•It allows for complex causation – lots of things acting together to generate an outcome. Conventional statistical modelling can do this in a limited sort of way through interaction.
•It allows for multiple causation – different combinations – in QCA terms configurations – can generate the same outcome. More than one way to skin a cat.
•It really makes us think about ‘what is a case’ – what Charles Ragin calls the processes of casing – just as important to specify the character and boundaries of cases as to be careful about operationalizing in measurement of what I prefer to call attributes or variate traces rather than variables.
•It really does have qualitative phases – conventionally at the beginning because the researcher really does have to engage closely with cases using qualitative techniques in order to establish attribute values. If you start, as I have often, with a data set of pre-given measures, you often have to move on to qualitative investigation to explore further differences.
•That word – differences – QCA is founded on distinctions.
B. Epistemology
CASTELLANI: Your research agenda is grounded in what you refer to as a complex/critical realist approach. What is complex/critical realism?
BYRNE: The term comes from David L. Harvey and his collaborator Reed. It involves a synthesis of the critical realist perspective of Roy Bhaskar (but the early Bhaskar) and complexity theory. So it says most of the world is made up of complex systems – although see Paul Cilliers’ important work on how such systems are both real and the products of scientific construction – the complexity part. Then it endorses critical realism’s deep ontology of the real as generative mechanisms, the actual as the contingently and contextually expressed outcome of those mechanisms (I wish we had another word than mechanisms), and the empirical as what we as scientists make from those mechanisms in action in the actual. Note ‘make’. This is a constructionist position but one which says that the real also has a say.
CASTELLANI: Why should researchers consider your epistemological approach important enough to adopt?
BYRNE: I would say it is David L. Harvey’s and I adopted his approach because it enabled me to make sense of social causality and allows agency, including conscious and informed agency, into play with the potential for knowledge to actually be applied in a meaningful and useful fashion. Does that for me and I recommend the treatment to others for the same reason.
C. The Complexity of Place, Space and Health.
CASTELLANI: Our Q&A is situated within the larger theme that I have been blogging on for the past couple weeks: how to improve the community health science literature by adopting a complexity science perspective.
You may disagree, but a major theme that I see in your work over the last decade is your rigorous and nuanced attempt to develop a methodological-epistemological framework researchers can use to develop better models of the complexities surrounding place, space and health. This includes the complexities of social exclusion, urban planning, spatial inequality, and the challenges surrounding the relationship between individuals and the communities in which they live. For example in your chapter, Complex and Contingent Causation—the Implications of Complex Realism for Quantitative Modeling (found in Carter and New’s Making Realism Work, 2004) you address one of the biggest challenges facing the community health science literature today: the inability of researchers to create a satisfactory way to address the relationship between micro-level health outcomes and aggregate level phenomena such as the neighborhood effect.
You state: “Multi-level modeling has been proposed as a way of resolving the difficulties of cross-level relationships among individually expressed health and social conditions. This interesting approach does represent a genuine effort to confront problems which are central to the relationship between the collective and the individual. However, this chapter will argue that the approach remains unsatisfactory, precisely because it ‘disembodies’ both aspects of the complex individual and aspects of the complex social systems through which individuals lead their lives” (p. 51).
CASTELLANI: What do you mean that researchers tend to “disembody” complexity?
BYRNE: Disembody is a specific kind of abstraction. Abstraction is necessary – I think Katherine Hayles is great on this in her How we became post-human but we also have to be very careful. I was using Chris Allen’s arguments – which I found interesting, well put and provocative – to frame my own argument. Chris was saying: don’t lets regard agentic human beings as physiological dopes ‘determined’ by the external and their own attributes in interaction. He pointed out that there is real variation in outcome – the reality of any probabilistic form of explanation of cause e.g. in a randomized controlled trial (RCT). I agree up to a point but think that we can move towards a better account if we think really hard about complex and contingent causation. I have written elsewhere about how I don’t have TB despite being exposed to cases in adolescence and having a very strong Heaf test reaction at that point. Too well fed, too well housed, and with parents who didn’t get the disease or die of it whilst they both had siblings who did and did so bred for resistance. But if I get AIDS or am starved in conditions like a WWII Japanese prisoner of war camp, then I will get TB. That is complexity expressed in my individual body and I want a modelling process which moves towards allowing for that.
CASTELLANI: As a solution, how do you think the methodological-epistemological framework you have developed helps researchers to preserve the complexity of their models?
BYRNE: First by making us think about it. Second, by looking for and using methods, quantitative and qualitative, which respect the complexity of the real as opposed to artificial (I owe this distinction to Elias Khalil) world. So always be skeptical about simplicity. It might be there but mostly it isn’t.
CASTELLANI: Related, you and others (such as Paul Cilliers and Charles Ragin) have criticized complexity scientists for making the same reductionistic mistake as multi-level researchers: complexity scientists still seem to reduce to an unnecessary level the complexity of systems. Why do you think complexity scientists fall prey to this reductionistic tendency? How do they get out of this trap?
BYRNE: See Morin’s excellent essay on this very point at: http://cogprints.org/5217/1/Morin.pdf
My take is that the kind of complexity which says we can always generate complexity from simple interactions following for example rules – note always, I have no quarrel with sometimes here – ends up with specifications which ‘look like’ the laws of Newtonian science although of course they are nothing of the kind. However, they are reductionist – you can do this if not in a white coat then in a techy sort of way which makes you look like a proper scientistic scientist. There is a real battle to be fought here although interestingly there are physicists – Peter Allen’s excellent work for example – and lots of eco centred biologists – as well as medics – who are beginning to recognize that they cannot deal with problems of explanation and action without dealing in what Morin calls general complexity.
D. The Future of Sociology
CASTELLANI: Without creating a straw-person, I think it is fair to say that sociologists, particularly those in the main-street of the profession have been slow to embrace or involve themselves in a critical dialogue with complexity science. What is your best argument for why sociologists should involve themselves in the new science(s) of complexity?
BYRNE: Because it allows us to deal with systems without falling into the Parsonian trap (although note that Parsons did have a sense of the complex from time to time). It also is a way towards agentic intervention. My first degree was in Sociology and Social Administration – we would usually but not necessarily correctly talk about Social Policy instead of administration today – and my Master’s was in that field rather than mainstream Sociology. I am an applied social scientist and complexity pushes towards action. It also is a way of getting past what frankly I see as the dead hand of much of contemporary sociological theory. Post modernism is a dead end but I am thinking here as much of Giddens and even of Bourdieu (and I have a deal of respect for Bourdieu). We need to engage empirically and get beyond the absolutely necessary preliminary task of empirical description into a serious and non-positivist engagement with social causality. That is what complexity lets me do.
---------------------
CASTELLANI: Dr. Byrne, thank you so much for your time. For more information on Dr. Byrne's work, visit his website by clicking here.
Dr. Byrne is Professor in the School of Applied Social Sciences at Durham University, England, where he is also Director of Postgraduate Studies. Dr. Byrne is the author of several books and a long list of articles, including his 1998 book, Complexity Theory and the Social Sciences--the first book to critically review and explore the application of complexity science to sociological inquiry. His most recent book, edited with noted sociologist and methodologist, Charles Ragin is The SAGE Handbook of Case-Based Methods
Dr. Byrne is an expert in methods, urban planning, community health, social policy, social exclusion and complexity science.
-----------------------------------------------
INTERVIEW WITH DR. BYRNE
CASTELLANI: Dr. Byrne, thanks so much for taking the time to do this interview. Your research agenda is rather vast in its scope—ranging from the philosophy of complexity science to method to urban planning to health care inequality. If you do not mind, I am going to narrow in on method first, given its wider implications for those reading this blog—most of whom are students and researchers new to the field of complexity science and its practice within sociology.
A. Case-Based Research
CASTELLANI: For the last several years, you have been a major advocate of a case-based approach to research. You specifically endorse what you and Charles Ragin refer to as Qualitative Comparative Analysis (QCA). First, how do you define QCA?
BYRNE: It is a method which is ‘set theoretic’ i.e. it understands causal relations in the social world in terms of relationships in combination – sets, rather than the unique contribution of single variables. It is based on systematic comparison – essentially an extension of John Stuart Mills’ method of differences. It requires careful qualitative engagement with specific cases as the foundation of that comparison.
CASTELLANI: Of the three major types of QCA (crisp-set, multi-value and fuzzy set), which do you find most useful and why? Or, do you approach the distinctions within QCA a different way?
BYRNE: I generally work with crisp set techniques and actually almost never go beyond the truth table. So I use QCA as a kind of mix of exploratory / explanatory – often focusing on ‘contradictory configurations’ in which the assemblage of elements in the line of the truth table – the configuration – generates different outcomes. That makes me look at those cases for what else is different about them. I see multi-value QCA as an extension of crisp set but it is much more complex to use. I frequently use Cluster Analysis as a data reduction technique and binarize membership of a cluster. Fuzzy set is very interesting and I have thought about how we might use distance from a cluster centre as a fuzzying principle but I have never managed to bring it off.
CASTELLANI: For researchers and graduate students new to case-based research, what is your best argument (apologetic) for including QCA in their toolbox of techniques?
BYRNE: For me the crucial things about QCA are the following:
•It allows for complex causation – lots of things acting together to generate an outcome. Conventional statistical modelling can do this in a limited sort of way through interaction.
•It allows for multiple causation – different combinations – in QCA terms configurations – can generate the same outcome. More than one way to skin a cat.
•It really makes us think about ‘what is a case’ – what Charles Ragin calls the processes of casing – just as important to specify the character and boundaries of cases as to be careful about operationalizing in measurement of what I prefer to call attributes or variate traces rather than variables.
•It really does have qualitative phases – conventionally at the beginning because the researcher really does have to engage closely with cases using qualitative techniques in order to establish attribute values. If you start, as I have often, with a data set of pre-given measures, you often have to move on to qualitative investigation to explore further differences.
•That word – differences – QCA is founded on distinctions.
B. Epistemology
CASTELLANI: Your research agenda is grounded in what you refer to as a complex/critical realist approach. What is complex/critical realism?
BYRNE: The term comes from David L. Harvey and his collaborator Reed. It involves a synthesis of the critical realist perspective of Roy Bhaskar (but the early Bhaskar) and complexity theory. So it says most of the world is made up of complex systems – although see Paul Cilliers’ important work on how such systems are both real and the products of scientific construction – the complexity part. Then it endorses critical realism’s deep ontology of the real as generative mechanisms, the actual as the contingently and contextually expressed outcome of those mechanisms (I wish we had another word than mechanisms), and the empirical as what we as scientists make from those mechanisms in action in the actual. Note ‘make’. This is a constructionist position but one which says that the real also has a say.
CASTELLANI: Why should researchers consider your epistemological approach important enough to adopt?
BYRNE: I would say it is David L. Harvey’s and I adopted his approach because it enabled me to make sense of social causality and allows agency, including conscious and informed agency, into play with the potential for knowledge to actually be applied in a meaningful and useful fashion. Does that for me and I recommend the treatment to others for the same reason.
C. The Complexity of Place, Space and Health.
CASTELLANI: Our Q&A is situated within the larger theme that I have been blogging on for the past couple weeks: how to improve the community health science literature by adopting a complexity science perspective.
You may disagree, but a major theme that I see in your work over the last decade is your rigorous and nuanced attempt to develop a methodological-epistemological framework researchers can use to develop better models of the complexities surrounding place, space and health. This includes the complexities of social exclusion, urban planning, spatial inequality, and the challenges surrounding the relationship between individuals and the communities in which they live. For example in your chapter, Complex and Contingent Causation—the Implications of Complex Realism for Quantitative Modeling (found in Carter and New’s Making Realism Work, 2004) you address one of the biggest challenges facing the community health science literature today: the inability of researchers to create a satisfactory way to address the relationship between micro-level health outcomes and aggregate level phenomena such as the neighborhood effect.
You state: “Multi-level modeling has been proposed as a way of resolving the difficulties of cross-level relationships among individually expressed health and social conditions. This interesting approach does represent a genuine effort to confront problems which are central to the relationship between the collective and the individual. However, this chapter will argue that the approach remains unsatisfactory, precisely because it ‘disembodies’ both aspects of the complex individual and aspects of the complex social systems through which individuals lead their lives” (p. 51).
CASTELLANI: What do you mean that researchers tend to “disembody” complexity?
BYRNE: Disembody is a specific kind of abstraction. Abstraction is necessary – I think Katherine Hayles is great on this in her How we became post-human but we also have to be very careful. I was using Chris Allen’s arguments – which I found interesting, well put and provocative – to frame my own argument. Chris was saying: don’t lets regard agentic human beings as physiological dopes ‘determined’ by the external and their own attributes in interaction. He pointed out that there is real variation in outcome – the reality of any probabilistic form of explanation of cause e.g. in a randomized controlled trial (RCT). I agree up to a point but think that we can move towards a better account if we think really hard about complex and contingent causation. I have written elsewhere about how I don’t have TB despite being exposed to cases in adolescence and having a very strong Heaf test reaction at that point. Too well fed, too well housed, and with parents who didn’t get the disease or die of it whilst they both had siblings who did and did so bred for resistance. But if I get AIDS or am starved in conditions like a WWII Japanese prisoner of war camp, then I will get TB. That is complexity expressed in my individual body and I want a modelling process which moves towards allowing for that.
CASTELLANI: As a solution, how do you think the methodological-epistemological framework you have developed helps researchers to preserve the complexity of their models?
BYRNE: First by making us think about it. Second, by looking for and using methods, quantitative and qualitative, which respect the complexity of the real as opposed to artificial (I owe this distinction to Elias Khalil) world. So always be skeptical about simplicity. It might be there but mostly it isn’t.
CASTELLANI: Related, you and others (such as Paul Cilliers and Charles Ragin) have criticized complexity scientists for making the same reductionistic mistake as multi-level researchers: complexity scientists still seem to reduce to an unnecessary level the complexity of systems. Why do you think complexity scientists fall prey to this reductionistic tendency? How do they get out of this trap?
BYRNE: See Morin’s excellent essay on this very point at: http://cogprints.org/5217/1/Morin.pdf
My take is that the kind of complexity which says we can always generate complexity from simple interactions following for example rules – note always, I have no quarrel with sometimes here – ends up with specifications which ‘look like’ the laws of Newtonian science although of course they are nothing of the kind. However, they are reductionist – you can do this if not in a white coat then in a techy sort of way which makes you look like a proper scientistic scientist. There is a real battle to be fought here although interestingly there are physicists – Peter Allen’s excellent work for example – and lots of eco centred biologists – as well as medics – who are beginning to recognize that they cannot deal with problems of explanation and action without dealing in what Morin calls general complexity.
D. The Future of Sociology
CASTELLANI: Without creating a straw-person, I think it is fair to say that sociologists, particularly those in the main-street of the profession have been slow to embrace or involve themselves in a critical dialogue with complexity science. What is your best argument for why sociologists should involve themselves in the new science(s) of complexity?
BYRNE: Because it allows us to deal with systems without falling into the Parsonian trap (although note that Parsons did have a sense of the complex from time to time). It also is a way towards agentic intervention. My first degree was in Sociology and Social Administration – we would usually but not necessarily correctly talk about Social Policy instead of administration today – and my Master’s was in that field rather than mainstream Sociology. I am an applied social scientist and complexity pushes towards action. It also is a way of getting past what frankly I see as the dead hand of much of contemporary sociological theory. Post modernism is a dead end but I am thinking here as much of Giddens and even of Bourdieu (and I have a deal of respect for Bourdieu). We need to engage empirically and get beyond the absolutely necessary preliminary task of empirical description into a serious and non-positivist engagement with social causality. That is what complexity lets me do.
---------------------
CASTELLANI: Dr. Byrne, thank you so much for your time. For more information on Dr. Byrne's work, visit his website by clicking here.
20/05/2009
Health & Place: An International Journal
While the community-as-complex-system model is relatively new, it already has a major journal outlet, called Health & Place: An International Journal.
Edited by Graham Moon, University of Southampton, School of Geography, Highfield, Southampton, the journal is dedicated to the study of all aspects of health and health care in which place or location matters.
As stated on its website, "Recent years have seen closer links evolving between medical geography, medical sociology, health policy, public health and epidemiology. The journal reflects these convergences, which emphasise differences in health and health care between places, the experience of health and care in specific places, the development of health care for places, and the methodologies and theories underpinning the study of these issues.
The journal brings together international contributors from geography, sociology, social policy and public health. It offers readers comparative perspectives on the difference that place makes to the incidence of ill-health, the structuring of health-related behaviour, the provision and use of health services, and the development of health policy.
At a time when health matters are the subject of ever-increasing attention, Health & Place provides accessible and readable papers summarizing developments and reporting the latest research findings."
It is important to note that the journal is a combination of both the community-as-context model and the community-as-complex-system model. So, it is important to identify the model being used in a particular paper. Overall, it is an excellent resource for the lastest developments in the field.
Edited by Graham Moon, University of Southampton, School of Geography, Highfield, Southampton, the journal is dedicated to the study of all aspects of health and health care in which place or location matters.
As stated on its website, "Recent years have seen closer links evolving between medical geography, medical sociology, health policy, public health and epidemiology. The journal reflects these convergences, which emphasise differences in health and health care between places, the experience of health and care in specific places, the development of health care for places, and the methodologies and theories underpinning the study of these issues.
The journal brings together international contributors from geography, sociology, social policy and public health. It offers readers comparative perspectives on the difference that place makes to the incidence of ill-health, the structuring of health-related behaviour, the provision and use of health services, and the development of health policy.
At a time when health matters are the subject of ever-increasing attention, Health & Place provides accessible and readable papers summarizing developments and reporting the latest research findings."
It is important to note that the journal is a combination of both the community-as-context model and the community-as-complex-system model. So, it is important to identify the model being used in a particular paper. Overall, it is an excellent resource for the lastest developments in the field.
19/05/2009
Placing Health by Tim Blackman
In yesterday's post, I discussed the three models currently used to do community health science. Of the three models, I am obviously a champion of the third--the community-as-complex-system model.
While I provided a basic overview of this model, I did not provide much in the way of references. I did, however, mention a book at the end.
The book is Placing Health by Tim Blackman. A link to the Google Books peek into the book is here.
Blackman's basic goal is to explain and demonstrate (through empirical inquiry) how complexity science improves our understanding of the role communities play in the health of people. Specifically, it explores how communities function as complex systems and the role these complex systems play in the lives of people, particularly in terms of spatial inequality.
Rather than review the book here, I will list several reviews for you to read. I will, however, make one point. Toward the end of the book, Blackman points to one of the explicit ways that a complex systems viewpoint changes how one approaches improving community health. While the community-as-context model is a major step forward, it is, nonetheless, a top-down model. This means that is treats the citizens of a community as objects of treatment. This leads to top-heavy, public health--the kind that does NOT involve people in their own health improvement. The community-as-complex-system model, however, is entirely different. Because it takes a bottom-up approach, it begins, by definition, with an interactive (relational) view of people and their communities, looking at how both effect the other. As such, it follows an action research protocol--people need to be involved in the improvement of their health care and their communities, which in turn, impacts of health of these people.
Okay, I will stop there. I think the book is fantastic and needs to be read by anyone serious about community or public health.
Here are some reviews to read
Review 1. International Journal of Integrated Care
2. Journal of Epidemiology and Community Health
While I provided a basic overview of this model, I did not provide much in the way of references. I did, however, mention a book at the end.
The book is Placing Health by Tim Blackman. A link to the Google Books peek into the book is here.
Blackman's basic goal is to explain and demonstrate (through empirical inquiry) how complexity science improves our understanding of the role communities play in the health of people. Specifically, it explores how communities function as complex systems and the role these complex systems play in the lives of people, particularly in terms of spatial inequality.
Rather than review the book here, I will list several reviews for you to read. I will, however, make one point. Toward the end of the book, Blackman points to one of the explicit ways that a complex systems viewpoint changes how one approaches improving community health. While the community-as-context model is a major step forward, it is, nonetheless, a top-down model. This means that is treats the citizens of a community as objects of treatment. This leads to top-heavy, public health--the kind that does NOT involve people in their own health improvement. The community-as-complex-system model, however, is entirely different. Because it takes a bottom-up approach, it begins, by definition, with an interactive (relational) view of people and their communities, looking at how both effect the other. As such, it follows an action research protocol--people need to be involved in the improvement of their health care and their communities, which in turn, impacts of health of these people.
Okay, I will stop there. I think the book is fantastic and needs to be read by anyone serious about community or public health.
Here are some reviews to read
Review 1. International Journal of Integrated Care
2. Journal of Epidemiology and Community Health
18/05/2009
Three Different Approaches to Community Health
At present, one can organize the community health science literature into three dominant approaches.
1. Social Pathways Model: The oldest and most widely practiced approach is the social pathways model. This model takes a nomothetic position, seeking to determine how a small set of social factors impacts the health of a community. In this model, community is also treated as a dependent (or grouping) variable.
2. Community as Context Model: This more recent approach emerged during the 1990s and has remained very hot! In this model, community context is treated as an independent variable, separate from the contribution of various other social factors--income, educational level, family health behaviors, etc. This approach to studying communities is a top-down model.
3. Community as a Complex System: The last model is the newest and least practiced. It views communities as complex systems; and takes a bottom-up approach to modeling.
The strength of the third approach is its ability to overcome the limitations of the other two models.
The other two models suffer from a reductionistic approach to community health--community is either an independent or dependent variable, with little research done to explore the "system-level" effects of a community; or, for that matter, the link within a community between micro-level (agent-based) and macro-level (emergent) behaviors. There is also no sense of environmental forces or the dynamics of a community over time--as a system--in the other two models.
Obviously, the limitations of the first two models are challenges that a complexity science approach to communities can handle. It can handle these challenges because this third approach has a complex view of communities as systems--that is, it sees the link between the micro and macro; has the tools to study system-level, emergent behavior; and has the ability to frame how environmental forces and the larger systems within which communities are situated impacts their respective health. Its bottom-up approach also allows it to see communities as both independent and dependent variables (via the concept of feedback loop). And, its bottom-up approach allows it to see communities as both context and composite--in other words, it does not construct a false dichotomy between community and other social (individual-level) factors such as income, education, etc.
For a basic introduction to the community-as-complex-system model, see Tim Blackman's new book, Placing Health.
1. Social Pathways Model: The oldest and most widely practiced approach is the social pathways model. This model takes a nomothetic position, seeking to determine how a small set of social factors impacts the health of a community. In this model, community is also treated as a dependent (or grouping) variable.
2. Community as Context Model: This more recent approach emerged during the 1990s and has remained very hot! In this model, community context is treated as an independent variable, separate from the contribution of various other social factors--income, educational level, family health behaviors, etc. This approach to studying communities is a top-down model.
3. Community as a Complex System: The last model is the newest and least practiced. It views communities as complex systems; and takes a bottom-up approach to modeling.
The strength of the third approach is its ability to overcome the limitations of the other two models.
The other two models suffer from a reductionistic approach to community health--community is either an independent or dependent variable, with little research done to explore the "system-level" effects of a community; or, for that matter, the link within a community between micro-level (agent-based) and macro-level (emergent) behaviors. There is also no sense of environmental forces or the dynamics of a community over time--as a system--in the other two models.
Obviously, the limitations of the first two models are challenges that a complexity science approach to communities can handle. It can handle these challenges because this third approach has a complex view of communities as systems--that is, it sees the link between the micro and macro; has the tools to study system-level, emergent behavior; and has the ability to frame how environmental forces and the larger systems within which communities are situated impacts their respective health. Its bottom-up approach also allows it to see communities as both independent and dependent variables (via the concept of feedback loop). And, its bottom-up approach allows it to see communities as both context and composite--in other words, it does not construct a false dichotomy between community and other social (individual-level) factors such as income, education, etc.
For a basic introduction to the community-as-complex-system model, see Tim Blackman's new book, Placing Health.
14/05/2009
Complexity Science & Community Health--Univ of Michigan Style
As I have discussed in previous posts, my two main substative foci are medical professionalism and community health--both from a complexity science perspective.
Over the next week I will be posting on the topic of community health, from a complexity science perspective, highlighting key ideas, scholars, periodicals, books, videos, and institutes.
I will begin with one of the leading institutes involved in the study of community health from a complexity science perspective, the Center for Social Epidemiology and Population Health (CSEPH), at the University of Michigan.
Working in conjunction with the world-renowned Center for the Study of Complex Systems at the Univ of Michigan, the CSEPH sits at the forefront of a complexity science approach to community health.
In 2007, the CSEPH held a symposium on complexity and community health. Here is an excellent video introducing the CSEPH symposium, housed at the National Institutes of Health, titled Symposium on a Complex Systems Approach to Population Health.
Over the next week I will be posting on the topic of community health, from a complexity science perspective, highlighting key ideas, scholars, periodicals, books, videos, and institutes.
I will begin with one of the leading institutes involved in the study of community health from a complexity science perspective, the Center for Social Epidemiology and Population Health (CSEPH), at the University of Michigan.
Working in conjunction with the world-renowned Center for the Study of Complex Systems at the Univ of Michigan, the CSEPH sits at the forefront of a complexity science approach to community health.
In 2007, the CSEPH held a symposium on complexity and community health. Here is an excellent video introducing the CSEPH symposium, housed at the National Institutes of Health, titled Symposium on a Complex Systems Approach to Population Health.
08/05/2009
SACS Toolkit: E-Social Science from a Systems Perspective
I am presenting the following paper at the upcoming sociocybernetics conference this June in Urbino Italy.
This year's conference is all about e-science and web science. The title is 'MODERNITY 2.0': EMERGING SOCIAL MEDIA TECHNOLOGIES AND THEIR IMPACTS.
For those following this blog, you know that I include e-science and web science on my map of complexity, situating them as the two newest areas of complexity science research.
My paper explores how the new toolkit my colleague, Fred Hafferty, and I have developed for modeling complex social systems (called the SACS Toolkit) can be used to manage and analyze web-based data. In fact, one of the reasons we created our toolkit was to find ways to address the growing complexity of digital data.
Here is the abstract of our paper. I will post the paper later in June, 2009.
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The SACS Toolkit provides researchers a new informatics-based ontology and methodology for managing and analyzing the massive, multi-dimensional databases regularly encountered on the web today. The SACS Toolkit does this by functioning as an intermediary between the web and researcher. Its intermediary function provides researchers several advantages. In terms of ontology, the SACS Toolkit: 1) provides a user-based filing system (social complexity theory) that help researchers organize and link multidimensional databases in a theoretically meaningful manner; 2) the filing system is also designed to form a complex system—to match the complexity of most web-based data. In terms of method, the SACS Toolkit: 1) provides a novel algorithm (assemblage) researchers can use to model complex systems with web data; 2) this algorithm works with any type of data; and 3) can be used with most methodological techniques (e.g., field research, statistics, etc), including the latest advances in agent-based modeling, network analysis, e-science and web science. In the current paper, we demonstrate the utility of the SACS Toolkit by applying it to a web-based community health science database we are currently studying. We begin with a review of the SACS Toolkit. Next, we explore the ontological and methodological challenges our database presented us—focusing on how the SACS Toolkit solved them. Fourth, we examine the model of community health we built, showing how the SACS Toolkit allowed us to make important advances in the current health sciences literature. We end inductively, suggesting how others may likewise use the SACS Toolkit.
This year's conference is all about e-science and web science. The title is 'MODERNITY 2.0': EMERGING SOCIAL MEDIA TECHNOLOGIES AND THEIR IMPACTS.
For those following this blog, you know that I include e-science and web science on my map of complexity, situating them as the two newest areas of complexity science research.
My paper explores how the new toolkit my colleague, Fred Hafferty, and I have developed for modeling complex social systems (called the SACS Toolkit) can be used to manage and analyze web-based data. In fact, one of the reasons we created our toolkit was to find ways to address the growing complexity of digital data.
Here is the abstract of our paper. I will post the paper later in June, 2009.
----------------------------------------
The SACS Toolkit provides researchers a new informatics-based ontology and methodology for managing and analyzing the massive, multi-dimensional databases regularly encountered on the web today. The SACS Toolkit does this by functioning as an intermediary between the web and researcher. Its intermediary function provides researchers several advantages. In terms of ontology, the SACS Toolkit: 1) provides a user-based filing system (social complexity theory) that help researchers organize and link multidimensional databases in a theoretically meaningful manner; 2) the filing system is also designed to form a complex system—to match the complexity of most web-based data. In terms of method, the SACS Toolkit: 1) provides a novel algorithm (assemblage) researchers can use to model complex systems with web data; 2) this algorithm works with any type of data; and 3) can be used with most methodological techniques (e.g., field research, statistics, etc), including the latest advances in agent-based modeling, network analysis, e-science and web science. In the current paper, we demonstrate the utility of the SACS Toolkit by applying it to a web-based community health science database we are currently studying. We begin with a review of the SACS Toolkit. Next, we explore the ontological and methodological challenges our database presented us—focusing on how the SACS Toolkit solved them. Fourth, we examine the model of community health we built, showing how the SACS Toolkit allowed us to make important advances in the current health sciences literature. We end inductively, suggesting how others may likewise use the SACS Toolkit.
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