Capturing Causal Complexity: Heuristics for Configurational Theorizing

Furnari, Santi; Crilly, Donal; Misangyi, Vilmos F.; Greckhamer, Thomas; Fiss, Peer C.; Aguilera, Ruth V.

doi:10.5465/amr.2019.0298

Cited by 409 publications

(409 citation statements)

References 134 publications

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“…In everyday life, adaptive response is the outcome of rapid decisions based on numerous different interrelated variables [ 72 , 73 ]. In any given situation, we identify the variables which we believe are “causing” or influencing the current situation [ 72 , 73 , 74 ]. In other words, decision-making is not linear, but a dynamic process, where there are many alternative options for achieving an outcome [ 72 ].…”

Section: Problem Solving In Medicinementioning

confidence: 99%

“…In any given situation, we identify the variables which we believe are “causing” or influencing the current situation [ 72 , 73 , 74 ]. In other words, decision-making is not linear, but a dynamic process, where there are many alternative options for achieving an outcome [ 72 ]. Interestingly, there is strong consensus that the clinician’s beliefs about clinical problems are probabilistic and are weighted in the causal model by how likely any individual variable is believed to be causing or influencing the outcome [ 1 ], consistent with Bayesian inference [ 75 ].…”

Section: Problem Solving In Medicinementioning

confidence: 99%

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Making Decision-Making Visible—Teaching the Process of Evaluating Interventions

Benfield

Krueger

2021

IJERPH

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Significant efforts in the past decades to teach evidence-based practice (EBP) implementation has emphasized increasing knowledge of EBP and developing interventions to support adoption to practice. These efforts have resulted in only limited sustained improvements in the daily use of evidence-based interventions in clinical practice in most health professions. Many new interventions with limited evidence of effectiveness are readily adopted each year—indicating openness to change is not the problem. The selection of an intervention is the outcome of an elaborate and complex cognitive process, which is shaped by how they represent the problem in their mind and is mostly invisible processes to others. Therefore, the complex thinking process that support appropriate adoption of interventions should be taught more explicitly. Making the process visible to clinicians increases the acquisition of the skills required to judiciously select one intervention over others. The purpose of this paper is to provide a review of the selection process and the critical analysis that is required to appropriately decide to trial or not trial new intervention strategies with patients.

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Section: Problem Solving In Medicinementioning

confidence: 99%

Section: Problem Solving In Medicinementioning

confidence: 99%

Making Decision-Making Visible—Teaching the Process of Evaluating Interventions

Benfield

Krueger

2021

IJERPH

View full text Add to dashboard Cite

show abstract

“…In everyday life, adaptive response is the outcome of rapid decisions based on numerous different interrelated variables [61; 62]. In any given situation, we identify the variables which we believe are 'causing' or influencing the current situation [61][62][63]. In other words, decision-making is not linear, but a dynamic process, where there are many alternative options for achieving an outcome [61].…”

Section: Heuristics and Causal Modelsmentioning

confidence: 99%

“…In any given situation, we identify the variables which we believe are 'causing' or influencing the current situation [61][62][63]. In other words, decision-making is not linear, but a dynamic process, where there are many alternative options for achieving an outcome [61]. Interestingly, there is strong consensus that the clinician's beliefs about clinical problems are probabilistic and are weighted in the causal model by how likely any individual variable is believed to be causing or influencing the outcome [1], consistent with Bayesian inference [64].…”

Section: Heuristics and Causal Modelsmentioning

confidence: 99%

Making Decision-Making Visible- Teaching the Process of Evaluating Interventions

Benfield¹,

Krueger²

2021

Preprint

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Significant efforts in the past decades to teach evidence-based practice (EBP) implementation has emphasized increasing knowledge of EBP and developing interventions to support adoption to practice. These efforts have resulted in only limited sustained improvements in the daily use of evidence-based interventions in clinical practice in most health professions. Many new interven-tions with limited evidence of effectiveness are readily adopted each year - indicating openness to change is not the problem. The selection of an intervention is the outcome of an elaborate and complex cognitive process which is shaped by how they represent the problem in their mind and is mostly invisible processes to others. Therefore, the complex thinking process which support appropriate adoption of interventions should be taught more explicitly. Making the process visible to clinicians increases the acquisition of the skills required to judiciously select one in-tervention over others. The purpose of this paper is to provide a review of the selection process and the critical analysis that is required to appropriately decide to trial or not trial new intervention strategies with patients.

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“…[11] The analytic objective of con gurational analysis is to identify necessary and su cient conditions for an outcome to occur, a fundamentally different search target than those used in correlation-based methods. [11][12][13] Con gurational methods expressly allow for causal complexity (when several conditions must jointly appear for an outcome to occur) as well as equi nality (when multiple pathways lead to the same outcome), making it well-suited for discerning different solution pathways related to local context. Con gurational analysis is one method within a broader family of con gurational comparative methods, which include Qualitative Comparative Analysis (QCA) and more recently Coincidence Analysis (CNA).…”

Section: Con Gurational Analysismentioning

confidence: 99%

Facility-Level Conditions Leading to Higher Reach: A Configurational Analysis of National VA Weight Management Programming

Miech

Freitag

Evans

et al. 2021

Preprint

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Background: While the Veterans Health Administration (VHA) MOVE! weight management program is effective in helping patients lose weight and is available at every VHA medical center across the United States, reaching patients to engage them in treatment remains a challenge. Facility-based MOVE! programs vary in structures, processes of programming and levels of reach, with no single factor explaining variation in reach. Configurational analysis, based on Boolean algebra and set theory, represents a mathematical approach to data analysis well-suited for discerning how conditions interact and identifying multiple pathways leading to the same outcome. We applied configurational analysis to identify facility-level obesity treatment program arrangements that directly link to higher reach.Methods: A national survey was fielded in March 2017 to elicit information about more than 75 different components of obesity treatment programs in all VHA medical centers. This survey data was linked to facility-level reach scores available through administrative data. Reach scores were calculated by dividing the total number of Veterans who are candidates for obesity treatment by the number of “new” MOVE! visits in 2017 for each program and then multiplied by 1000. Programs with the top 40% highest reach scores (n=50) were compared to those in the lowest 40% (n=50). Configurational analysis was applied to identify specific combinations of conditions linked to reach rates. Results: 125 MOVE! program representatives responded to the survey and had complete reach data. The final solution consisted of 5 distinct pathways comprising combinations of program components related to pharmacotherapy, bariatric surgery, and comprehensive lifestyle intervention; 3 of the 5 pathways depended on the size/complexity of medical center. The 5 pathways explained 78% (39/50) of the facilities in the higher-reach group with 87% consistency (39/45). Conclusions: Specific combinations of facility-level conditions identified through configurational analysis uniquely distinguished facilities with higher reach from those with lower reach. Solutions demonstrated the importance of local context plus specific program components link together to account for a key implementation outcome. These findings will guide system recommendations about optimal program structures to maximize reach to patients who would benefit from obesity treatment such as the MOVE! program.

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Capturing Causal Complexity: Heuristics for Configurational Theorizing

Cited by 409 publications

References 134 publications

Making Decision-Making Visible—Teaching the Process of Evaluating Interventions

Making Decision-Making Visible—Teaching the Process of Evaluating Interventions

Making Decision-Making Visible- Teaching the Process of Evaluating Interventions

Facility-Level Conditions Leading to Higher Reach: A Configurational Analysis of National VA Weight Management Programming

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