A Multi-Methodology Approach to Creating a Causal Loop Diagram

Dhirasasna, NiNa; Sahin, Oz

doi:10.3390/systems7030042

Cited by 47 publications

(27 citation statements)

References 123 publications

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“…Detailed de nitions were created for each overarching concept. Lines of argument, re ecting pathways through which these concepts interacted to facilitate community transformation were created and visually depicted using concept maps and causal loop diagrams [17]. A detailed explanation of these diagrams is presented elsewhere [10].…”

Section: Discussionmentioning

confidence: 99%

Blending Participatory Action Synthesis and Meta-ethnography: An Innovative Approach to Evaluating Complex Community Health Transformation

Reed

Carr

Rabbani

et al. 2020

Preprint

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Background: Community health improvement processes are diverse and complex, and evaluation methods to gain generalizable knowledge across community are limited by available data, and the need for deep contextual knowledge. Methods: This article describes an innovative participatory approach to evaluation of a community transformation initiative involving up to eighteen communication nationwide. The approach blends two qualitative research synthesis methods: participatory action synthesis and meta-ethnography and applies them to the pragmatic evaluation of a program in real-life settings. Results: In this article, we present the justification for and details about the evaluation process. Four cycles of synthesis and engagement resulted in development of concepts to describe community actions for transformation.Conclusion: The approach presented here will be useful to both researchers and practitioners interested in evaluating community-based health and well-being initiatives and other complex interventions conducted in complex settings.

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Section: Discussionmentioning

confidence: 99%

Blending Participatory Action Synthesis and Meta-ethnography: An Innovative Approach to Evaluating Complex Community Health Transformation

Reed

Carr

Rabbani

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…In a CLD, empirical research and theoretical knowledge (i.e. well-reasoned hypotheses) can be combined to compose a network of variables and causal links [ 30 ]. A CLD is thus a visual representation of “initial hypotheses, or sets of linked hypotheses, about the processes involved” [ 31 ].…”

Section: Methodsmentioning

confidence: 99%

Understanding the impact of exposure to adverse socioeconomic conditions on chronic stress from a complexity science perspective

Crielaard

Nicolaou

Sawyer

et al. 2021

BMC Med

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Background Chronic stress increases chronic disease risk and may underlie the association between exposure to adverse socioeconomic conditions and adverse health outcomes. The relationship between exposure to such conditions and chronic stress is complex due to feedback loops between stressor exposure and psychological processes, encompassing different temporal (acute stress response to repeated exposure over the life course) and spatial (biological/psychological/social) scales. We examined the mechanisms underlying the relationship between exposure to adverse socioeconomic conditions and chronic stress from a complexity science perspective, focusing on amplifying feedback loops across different scales. Methods We developed a causal loop diagram (CLD) to interpret available evidence from this perspective. The CLD was drafted by an interdisciplinary group of researchers. Evidence from literature was used to confirm/contest the variables and causal links included in the conceptual framework and refine their conceptualisation. Our findings were evaluated by eight independent researchers. Results Adverse socioeconomic conditions imply an accumulation of stressors and increase the likelihood of exposure to uncontrollable childhood and life course stressors. Repetition of such stressors may activate mechanisms that can affect coping resources and coping strategies and stimulate appraisal of subsequent stressors as uncontrollable. We identified five feedback loops describing these mechanisms: (1) progressive deterioration of access to coping resources because of repeated insolvability of stressors; (2) perception of stressors as uncontrollable due to learned helplessness; (3) tax on cognitive bandwidth caused by stress; (4) stimulation of problem avoidance to provide relief from the stress response and free up cognitive bandwidth; and (5) susceptibility to appraising stimuli as stressors against a background of stress. Conclusions Taking a complexity science perspective reveals that exposure to adverse socioeconomic conditions implies recurrent stressor exposure which impacts chronic stress via amplifying feedback loops that together could be conceptualised as one vicious cycle. This means that in order for individual-level psychological interventions to be effective, the context of exposure to adverse socioeconomic conditions also needs to be addressed.

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“…One way of displaying such a conceptual model is in the form of a causal loop diagram (CLD), a user-friendly medium to visualize mental models—making it suitable to facilitate the inclusion of knowledge from experts with specializations ranging from biology to sociology. In a CLD, relevant knowledge and empirical research findings concerning the system of interest can be combined (Dhirasasna & Sahin, 2019). This combination is achieved by displaying causal links between variables by directed edges, which usually have polarities for positive (+) or negative (–) links (Haraldsson, 2004; Figure 2, adapted from Burger et al, 2020).…”

Section: Developing An Annotated Causal Loop Diagrammentioning

confidence: 99%

Refining the causal loop diagram: A tutorial for maximizing the contribution of domain expertise in computational system dynamics modeling.

Crielaard¹,

Uleman²,

Châtel³

et al. 2024

Psychological Methods

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Complexity science and systems thinking are increasingly recognized as relevant paradigms for studying systems where biology, psychology, and socioenvironmental factors interact. The application of systems thinking, however, often stops at developing a conceptual model that visualizes the mapping of causal links within a system, e.g., a causal loop diagram (CLD). While this is an important contribution in itself, it is imperative to subsequently formulate a computable version of a CLD in order to interpret the dynamics of the modeled system and simulate “what if” scenarios. We propose to realize this by deriving knowledge from experts’ mental models in biopsychosocial domains. This article first describes the steps required for capturing expert knowledge in a CLD such that it may result in a computational system dynamics model (SDM). For this purpose, we introduce several annotations to the CLD that facilitate this intended conversion. This annotated CLD (aCLD) includes sources of evidence, intermediary variables, functional forms of causal links, and the distinction between uncertain and known-to-be-absent causal links. We propose an algorithm for developing an aCLD that includes these annotations. We then describe how to formulate an SDM based on the aCLD. The described steps for this conversion help identify, quantify, and potentially reduce sources of uncertainty and obtain confidence in the results of the SDM’s simulations. We utilize a running example that illustrates each step of this conversion process. The systematic approach described in this article facilitates and advances the application of computational science methods to biopsychosocial systems.

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A Multi-Methodology Approach to Creating a Causal Loop Diagram

Cited by 47 publications

References 123 publications

Blending Participatory Action Synthesis and Meta-ethnography: An Innovative Approach to Evaluating Complex Community Health Transformation

Blending Participatory Action Synthesis and Meta-ethnography: An Innovative Approach to Evaluating Complex Community Health Transformation

Understanding the impact of exposure to adverse socioeconomic conditions on chronic stress from a complexity science perspective

Refining the causal loop diagram: A tutorial for maximizing the contribution of domain expertise in computational system dynamics modeling.

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