IDEAS home Printed from https://ideas.repec.org/a/sae/medema/v36y2016i4p453-471.html
   My bibliography  Save this article

Design Features of Explicit Values Clarification Methods

Author

Listed:
  • Holly O. Witteman
  • Laura D. Scherer
  • Teresa Gavaruzzi
  • Arwen H. Pieterse
  • Andrea Fuhrel-Forbis
  • Selma Chipenda Dansokho
  • Nicole Exe
  • Valerie C. Kahn
  • Deb Feldman-Stewart
  • Nananda F. Col
  • Alexis F. Turgeon
  • Angela Fagerlin

Abstract

Background. Values clarification is a recommended element of patient decision aids. Many different values clarification methods exist, but there is little evidence synthesis available to guide design decisions. Purpose. To describe practices in the field of explicit values clarification methods according to a taxonomy of design features. Data Sources. MEDLINE, all EBM Reviews, CINAHL, EMBASE, Google Scholar, manual search of reference lists, and expert contacts. Study Selection. Articles were included if they described 1 or more explicit values clarification methods. Data Extraction. We extracted data about decisions addressed; use of theories, frameworks, and guidelines; and 12 design features. Data Synthesis. We identified 110 articles describing 98 explicit values clarification methods. Most of these addressed decisions in cancer or reproductive health, and half addressed a decision between just 2 options. Most used neither theory nor guidelines to structure their design. “Pros and cons†was the most common type of values clarification method. Most methods did not allow users to add their own concerns. Few methods explicitly presented tradeoffs inherent in the decision, supported an iterative process of values exploration, or showed how different options aligned with users’ values. Limitations . Study selection criteria and choice of elements for the taxonomy may have excluded values clarification methods or design features. Conclusions . Explicit values clarification methods have diverse designs but can be systematically cataloged within the structure of a taxonomy. Developers of values clarification methods should carefully consider each of the design features in this taxonomy and publish adequate descriptions of their designs. More research is needed to study the effects of different design features.

Suggested Citation

  • Holly O. Witteman & Laura D. Scherer & Teresa Gavaruzzi & Arwen H. Pieterse & Andrea Fuhrel-Forbis & Selma Chipenda Dansokho & Nicole Exe & Valerie C. Kahn & Deb Feldman-Stewart & Nananda F. Col & Ale, 2016. "Design Features of Explicit Values Clarification Methods," Medical Decision Making, , vol. 36(4), pages 453-471, May.
  • Handle: RePEc:sae:medema:v:36:y:2016:i:4:p:453-471
    DOI: 10.1177/0272989X15626397
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0272989X15626397
    Download Restriction: no

    File URL: https://libkey.io/10.1177/0272989X15626397?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Linzer, Drew A. & Lewis, Jeffrey B., 2011. "poLCA: An R Package for Polytomous Variable Latent Class Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 42(i10).
    2. Pieterse, Arwen H. & de Vries, Marieke & Kunneman, Marleen & Stiggelbout, Anne M. & Feldman-Stewart, Deb, 2013. "Theory-informed design of values clarification methods: A cognitive psychological perspective on patient health-related decision making," Social Science & Medicine, Elsevier, vol. 77(C), pages 156-163.
    3. Gaston, Christine M. & Mitchell, Geoffrey, 2005. "Information giving and decision-making in patients with advanced cancer: A systematic review," Social Science & Medicine, Elsevier, vol. 61(10), pages 2252-2264, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Nananda F. Col & Andrew J. Solomon & Vicky Springmann & Calvin P. Garbin & Carolina Ionete & Lori Pbert & Enrique Alvarez & Brenda Tierman & Ashli Hopson & Christen Kutz & Idanis Berrios Morales & Car, 2018. "Whose Preferences Matter? A Patient-Centered Approach for Eliciting Treatment Goals," Medical Decision Making, , vol. 38(1), pages 44-55, January.
    2. Laura D. Scherer & Jeffrey T. Kullgren & Tanner Caverly & Aaron M. Scherer & Victoria A. Shaffer & Angela Fagerlin & Brian J. Zikmund-Fisher, 2018. "Medical Maximizing-Minimizing Preferences Predict Responses to Information about Prostate-Specific Antigen Screening," Medical Decision Making, , vol. 38(6), pages 708-718, August.
    3. Bo Min Jeon & Su Hyun Kim & Soo Jung Lee, 2018. "Decisional conflict in end‐of‐life cancer treatment among family surrogates: A cross‐sectional survey," Nursing & Health Sciences, John Wiley & Sons, vol. 20(4), pages 472-478, December.
    4. Marieke G.M. Weernink & Janine A. van Til & Holly O. Witteman & Liana Fraenkel & Maarten J. IJzerman, 2018. "Individual Value Clarification Methods Based on Conjoint Analysis: A Systematic Review of Common Practice in Task Design, Statistical Analysis, and Presentation of Results," Medical Decision Making, , vol. 38(6), pages 746-755, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Adrian O’Hagan & Arthur White, 2019. "Improved model-based clustering performance using Bayesian initialization averaging," Computational Statistics, Springer, vol. 34(1), pages 201-231, March.
    2. Aline Riboli Marasca & Maurício Scopel Hoffmann & Anelise Reis Gaya & Denise Ruschel Bandeira, 2021. "Subjective Well-Being and Psychopathology Symptoms: Mental Health Profiles and their Relations with Academic Achievement in Brazilian Children," Child Indicators Research, Springer;The International Society of Child Indicators (ISCI), vol. 14(3), pages 1121-1137, June.
    3. Lisa Blaydes, 2023. "Assessing the Labor Conditions of Migrant Domestic Workers in the Arab Gulf States," ILR Review, Cornell University, ILR School, vol. 76(4), pages 724-747, August.
    4. Jindřich Špička & Zdeňka Náglová, 2022. "Consumer segmentation in the meat market - The case study of Czech Republic," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 68(2), pages 68-77.
    5. Nicholas T. Davis & Kirby Goidel & Yikai Zhao, 2021. "The Meanings of Democracy among Mass Publics," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 153(3), pages 849-921, February.
    6. Carter, Virginia & Derudder, Ben & Henríquez, Cristián, 2021. "Assessing local governments’ perception of the potential implementation of biophilic urbanism in Chile: A latent class approach," Land Use Policy, Elsevier, vol. 101(C).
    7. Arjan S. Gosal & Janine A. McMahon & Katharine M. Bowgen & Catherine H. Hoppe & Guy Ziv, 2021. "Identifying and Mapping Groups of Protected Area Visitors by Environmental Awareness," Land, MDPI, vol. 10(6), pages 1-14, May.
    8. Assem Abu Hatab & Padmaja Ravula & Swamikannu Nedumaran & Carl-Johan Lagerkvist, 2022. "Perceptions of the impacts of urban sprawl among urban and peri-urban dwellers of Hyderabad, India: a Latent class clustering analysis," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(11), pages 12787-12812, November.
    9. Martin Eling & David Pankoke, 2016. "Costs and Benefits of Financial Regulation: An Empirical Assessment for Insurance Companies," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 41(4), pages 529-554, October.
    10. Sunil Kumar & Zakir Husain & Diganta Mukherjee, 2015. "Assessing Consistency of Consumer Confidence Data using Dynamic Latent Class Analysis," Papers 1509.01215, arXiv.org.
    11. Lorena Charrier & Paola Berchialla & Paola Dalmasso & Alberto Borraccino & Patrizia Lemma & Franco Cavallo, 2019. "Cigarette Smoking and Multiple Health Risk Behaviors: A Latent Class Regression Model to Identify a Profile of Young Adolescents," Risk Analysis, John Wiley & Sons, vol. 39(8), pages 1771-1782, August.
    12. Raphaela Grafiadeli & Heide Glaesmer & Birgit Wagner, 2022. "Loss-Related Characteristics and Symptoms of Depression, Prolonged Grief, and Posttraumatic Stress Following Suicide Bereavement," IJERPH, MDPI, vol. 19(16), pages 1-10, August.
    13. Daniel L. Oberski, 2016. "A Review of Latent Variable Modeling With R," Journal of Educational and Behavioral Statistics, , vol. 41(2), pages 226-233, April.
    14. Guangchao Feng, 2014. "Estimating intercoder reliability: a structural equation modeling approach," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(4), pages 2355-2369, July.
    15. Giorgio Eduardo Montanari & Marco Doretti & Maria Francesca Marino, 2022. "Model-based two-way clustering of second-level units in ordinal multilevel latent Markov models," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(2), pages 457-485, June.
    16. Bradley D. Custer & Hope O. Akaeze, 2021. "A Typology of State Financial Aid Grant Programs Using Latent Class Analysis," Research in Higher Education, Springer;Association for Institutional Research, vol. 62(2), pages 175-205, March.
    17. Paolo Brunori & Alain Trannoy & Caterina Francesca Guidi, 2021. "Ranking populations in terms of inequality of health opportunity: A flexible latent type approach," Health Economics, John Wiley & Sons, Ltd., vol. 30(2), pages 358-383, February.
    18. Rezwana Rafiq & Michael G. McNally, 2023. "An exploratory analysis of alternative travel behaviors of ride-hailing users," Transportation, Springer, vol. 50(2), pages 571-605, April.
    19. Weissinger, Guy & Shelby Rivers, Alannah & Atte, Tita & Diamond, Guy, 2023. "Suicide risk screening in the school environment: Family factors and profiles," Children and Youth Services Review, Elsevier, vol. 145(C).
    20. Bart Neuts & João Romão & Peter Nijkamp & Asami Shikida, 2016. "Market segmentation and their potential economic impacts in an ecotourism destination," Tourism Economics, , vol. 22(4), pages 793-808, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:medema:v:36:y:2016:i:4:p:453-471. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.