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      Clarifying values: an updated review

      review-article
      1 , , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15
      BMC Medical Informatics and Decision Making
      BioMed Central
      The International Patient Decision Aid Standards (IPDAS) Collaboration's Quality Dimensions: Theoretical Rationales, Current Evidence, and Emerging Issues
      1392012

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          Abstract

          Background

          Consensus guidelines have recommended that decision aids include a process for helping patients clarify their values. We sought to examine the theoretical and empirical evidence related to the use of values clarification methods in patient decision aids.

          Methods

          Building on the International Patient Decision Aid Standards (IPDAS) Collaboration’s 2005 review of values clarification methods in decision aids, we convened a multi-disciplinary expert group to examine key definitions, decision-making process theories, and empirical evidence about the effects of values clarification methods in decision aids. To summarize the current state of theory and evidence about the role of values clarification methods in decision aids, we undertook a process of evidence review and summary.

          Results

          Values clarification methods (VCMs) are best defined as methods to help patients think about the desirability of options or attributes of options within a specific decision context, in order to identify which option he/she prefers. Several decision making process theories were identified that can inform the design of values clarification methods, but no single “best” practice for how such methods should be constructed was determined. Our evidence review found that existing VCMs were used for a variety of different decisions, rarely referenced underlying theory for their design, but generally were well described in regard to their development process. Listing the pros and cons of a decision was the most common method used. The 13 trials that compared decision support with or without VCMs reached mixed results: some found that VCMs improved some decision-making processes, while others found no effect.

          Conclusions

          Values clarification methods may improve decision-making processes and potentially more distal outcomes. However, the small number of evaluations of VCMs and, where evaluations exist, the heterogeneity in outcome measures makes it difficult to determine their overall effectiveness or the specific characteristics that increase effectiveness.

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          Most cited references9

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          Thinking too much: introspection can reduce the quality of preferences and decisions.

          In Study 1, college students' preferences for different brands of strawberry jams were compared with experts' ratings of the jams. Students who analyzed why they felt the way they did agreed less with the experts than students who did not. In Study 2, college students' preferences for college courses were compared with expert opinion. Some students were asked to analyze reasons; others were asked to evaluate all attributes of all courses. Both kinds of introspection caused people to make choices that, compared with control subjects', corresponded less with expert opinion. Analyzing reasons can focus people's attention on nonoptimal criteria, causing them to base their subsequent choices on these criteria. Evaluating multiple attributes can moderate people's judgments, causing them to discriminate less between the different alternatives.
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            Intuition in Judgment and Decision Making: Extensive Thinking Without Effort

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              Where is the theory? Evaluating the theoretical frameworks described in decision support technologies.

              To identify and describe the extent to which theory or theoretical frameworks informed the development and evaluation of decision support technologies (DSTs). The analysis was based on the decision technologies used in studies included in the Cochrane systematic review of patient decision aids for people facing health screening or treatment decisions. The assumption was made that DSTs evaluated by randomized controlled trials, and therefore included in the updated Cochrane review have been the most rigorously developed. Of the 50 DSTs evaluated only 17 (34%) were based on a theoretical framework. Amongst these, 11 decision-making theories were described but the extent to which theory informed the development, field-testing and evaluation of these interventions was highly variable between DSTs. The majority of the 17 DSTs that relied on a theory was not explicit about how theory had guided their design and evaluation. Many had superficial descriptions of the theory or theories involved. Furthermore, based on the analysis of those 17 DSTs, none had reported field-testing prior to evaluation. The use of decision-making theory in DST development is rare and poorly described. The lack of theoretical underpinning to the design and development of DSTs most likely reflects the early development stage of the DST field. The findings clearly indicate the need to give more attention to how the most important decision-making theories could be better used to guide the design of key decision support components and their modes of action.
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                Author and article information

                Contributors
                Conference
                BMC Med Inform Decis Mak
                BMC Med Inform Decis Mak
                BMC Medical Informatics and Decision Making
                BioMed Central
                1472-6947
                2013
                29 November 2013
                : 13
                : Suppl 2
                : S8
                Affiliations
                [1 ]Department of Internal Medicine and Center for Bioethics and Social Sciences in Medicine, University of Michigan and VA Ann Arbor Center for Clinical Management Research, 2800 Plymouth Road, Building 16, Rm. 455S, Ann Arbor, MI 48109-2800, USA
                [2 ]Department of Medicine and Cecil Sheps Center for Health Services Research, CB# 7110, University of North Carolina, Chapel Hill, NC 27599, USA
                [3 ]Nursing, Midwifery and Allied Health Professions Research Unit, University of Stirling, Iris Murdoch Building, Stirling, FK9 4LA, UK
                [4 ]Departments of Family Medicine and Geriatric Medicine, College of Osteopathic Medicine and Center for Excellence in the Neurosciences, University of New England, Biddeford, ME 04005, USA
                [5 ]Division of Cancer Care and Epidemiology, Cancer Research Institute, Department of Oncology, Queen’s University, Kingston, K7L 3N6, Canada
                [6 ]Department of Developmental Psychology and Socialization, University of Padova (Italy), Via Venezia, 835131 Padova, Italy
                [7 ]Leeds Institute of Health Sciences, Charles Thackrah Building, University of Leeds, 101 Clarendon Road, Leeds, LS2 9LJ
                [8 ]University of Saskatchewan College of Nursing, St. Andrew’s College, 312, 1121 College Drive, Saskatoon, SK, S7N 0W3, Canada
                [9 ]Informed Medical Decisions Foundation, 40 Court Street, Suite 300, Boston, MA 02108, USA
                [10 ]Department of Medical Decision Making, Leiden University Medical Center, The Netherlands
                [11 ]Center for Behavioral Economics and Decision Research and Cornell Magnetic Resonance Imaging Facility, Cornell University MVR B44, Ithaca, New York, 14853, USA
                [12 ]Department of Medical Decision Making, Leiden University Medical Center, The Netherlands
                [13 ]Department of Psychological Sciences, University of Missouri-Columbia, Columbia, MO, USA
                [14 ]The Ohio State University College of Nursing, 384 Newton Hall, 1585 Neil Avenue, Columbus, OH 43210, USA
                [15 ]Office of Education & Continuing Professional Development and Department of Family and Emergency Medicine, Université Laval, Pavillon Ferdinand-Vandry, bureau 2881-F, 1050 avenue de la Médecine, Quebec City, QC, G1V 0A6, Canada
                Article
                1472-6947-13-S2-S8
                10.1186/1472-6947-13-S2-S8
                4044232
                24625261
                e2be215d-b5ef-420c-b75c-4962111aba82
                Copyright © 2013 Fagerlin et al; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                The International Patient Decision Aid Standards (IPDAS) Collaboration's Quality Dimensions: Theoretical Rationales, Current Evidence, and Emerging Issues
                Rockville, MD, USA
                1392012
                History
                Categories
                Review

                Bioinformatics & Computational biology
                Bioinformatics & Computational biology

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