Design Features of Explicit Values Clarification Methods

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.

Keywords

values clarification values clarification exercise values clarification method values clarify decision making decision aids shared decision making design preferences

Individual values are a critical ingredient in high-quality decision making and, indeed, in high-quality care.^1–4 What is important to one person may not be the same as what is important to others. Thus, a common definition of an informed decision begins with 2 foundational elements: such a choice must be based on relevant knowledge, and it must be congruent with the individual’s values.^5,6

Considerable work has been done on the first element to determine how best to ensure that people have relevant knowledge before making health decisions.^7–10 Although such evidence is not always consistently implemented within decision support tools, researchers and practitioners can look to best practices for guidance on how to present health information.^11,12

There is considerably less consensus on the second element. Although there is widespread agreement that supporting the process of values clarification is a key step in effective decision making,^4,12–14 to the point that inclusion of such a component has been used as a metric of quality of decision support tools,¹⁵ there are no established best practices for values clarification.^16,17 This may be attributable at least partly to the fact that activities described as values clarification are extremely varied. They include tasks such as identifying pros and cons of an option,¹³ rating¹⁸ or ranking¹⁹ the importance of these specific risks or benefits, indicating whether each piece of information pushes one toward oraway from a given choice,²⁰ viewing a “soap opera” whose characters are faced with a medical decision and choosing the character with whom one most identifies,²¹ or having an open discussion about attributes of interest.²²

All of these activities are designed to achieve the goal of helping people clarify their values relevant to a given decision. However, they have vastly different features, which makes it difficult to compare and contrast different designs, draw conclusions about their comparative effectiveness, and thus make decisions about how we can best help people clarify their values relevant to a health decision.

To begin to build an evidence base concerning values clarification, we undertook a systematic review of explicit values clarification methods. The aim of this article is to catalog the diverse methods that have been described in the literature. The cataloguing scheme—or taxonomy—will also serve to provide structure for the development and reporting of values clarification methods, as well as for studying the effects of different design features.

Values, Values Clarification, and Preferences

The terms values, values clarification, and preferences are used in a number of ways in the literature. Values may refer to broad principles such as valuing family or to more specific concepts such as the extent to which decision attributes matter to an individual. In this review, the term values refers to the latter, narrower meaning, and we therefore refer to values clarification as it is commonly used in the medical decision-making literature, meaning the process of sorting out what matters to an individual relevant to a given health decision. Similarly, the term preferences refers to an individual’s inclination toward or away from a given decision option. According to these definitions, values clarification methods should help people sort out what matters to them, which should, in turn, help determine preferences. We note that the related terms values elicitation and preference elicitation refer to processes by which values and preferences, respectively, are drawn out.

Methods

Inclusion and Exclusion Criteria

Articles were included in this review if they sufficiently described the design of an explicit values clarification method intended to assist someone in making an individual-level health decision. We defined an explicit method as one in which the user of the method explicitly interacted with an interface, for example, by shading in boxes in a booklet or moving a slider in a web-based application. Articles were considered to have described a method sufficiently if screeners deemed that it would be possible to extract data for a minimum of 10 of the 12 design features in our taxonomy, either because the information was contained in the text of the article, in an appendix, or if the article included a URL freely linking to a copy of the values clarification method. An included values clarification method could be part of a decision aid but could also be an independent intervention or another type of intervention. In addition, an article could conceivably describe more than 1 values clarification method. We excluded articles that described a decision aid and mentioned that a values clarification method was included but did not describe the method in sufficient detail or used an implicit method.

Search Strategy

With assistance from 2 medical librarians to develop and deploy our search strategy, we conducted a systematic search to identify published accounts of values clarification methods. We searched MEDLINE, all EBM Reviews, CINAHL, and EMBASE for either value or values and clarif* within 5 words of each other in abstracts and titles. For CINAHL, we also used the major subject heading “Values Clarification.” In addition, we searched Google Scholar for values clarification exercise, the term used until recently to describe such interactive tools.¹⁷ We also included all articles that either cited the previous version of the International Patient Decision Aid Standards (IPDAS) guidelines¹² or were included in the most recent published Cochrane review of decision aids at the time of the search.²³ Two searches were conducted: the first on 17 December 2010 and an update using the same search strings and methods on 29 January 2014. We did not use date or language restrictions. This strategy yielded a total of 2629 articles after duplicates were removed (2145 in the initial search and 484 in the update). We also searched references of included articles in which the articles referred to previous designs not included in our original set, consulted with experts to identify any articles that might have been missed, and reviewed all articles added in the update to the Cochrane systematic review of decision aids, which was in process at the time of this review.⁹ These steps yielded an additional 3, 4, and 23 articles, respectively. Thus, we screened a total of 2659 articles.

Screening Process

Two authors (H.O.W. plus one of L.D.S., T.G., S.C.D.) independently screened all articles. Discrepancies were resolved by discussion until consensus was reached.

Quality Appraisal

Because we sought descriptive articles, no quality appraisal beyond our inclusion criteria was necessary.

Development of the Taxonomy

The overall structure of the taxonomy was developed collaboratively by all authors. We posed broad questions and iteratively reviewed the data to refine each data element and its categories. The broad questions posed were: 1) For what decision was this values clarification method created? 2) What theory, framework, guidelines, and previous work guided its design? 3) What were the design features of the method?

In identifying design features, we aimed to describe a taxonomy of design choices that developers of values clarification methods must make—deliberately or not—that determine how users may interact with a given values clarification method. To develop the taxonomy, we used an iterative method of constant comparison, in which we identified design features that distinguished different values clarification methods from each other, examined those features across methods, discussed the features among data extractors (H.O.W., L.D.S., T.G., A.H.P., A.F.-F., S.C.D.), consulted with other authors, revised definitions and categories, and ultimately arrived at the structure described below.

Data Extraction

One author (H.O.W.) extracted all data into evidence tables, which were subsequently reviewed in detail by 5 authors (L.D.S., T.G., A.H.P., A.F.-F., S.C.D.), each of whom examined specific columns, identified any data of concern, and resolved any issues together with H.O.W. Items for which further information was deemed necessary were referred for consultation with authors of the original articles.

Data Synthesis

Summary statistics were calculated in Microsoft Excel.²⁴

Analysis

We explored whether it would be possible to simplify the taxonomy of design features by examining pairwise comparisons between design features and by applying latent class analysis. Latent class analysis is similar to factor analysis but is better suited to categorical data. Analyses were performed in R, version 3.0.2,²⁵ using the poLCA package for latent class analysis.²⁶

Results

Overview of Included Studies

This review includes 110 articles describing 98 explicit values clarification methods. See Figure 1 for details of the identification, screening, and eligibility assessment of articles; Table 1 for a list of included articles; and Table 2 for full descriptive statistics of the values clarification methods.

Figure 1

Screening process.

Table 1

Included Articles

Article(s)	Number of Values Clarification Methods Described in Article(s)	Decision
Abhyankar and others²⁷	1	Choice between standard adjuvant chemotherapy for early-stage breast cancer and clinical trial testing new chemotherapy
Abreu and others²⁸	1	Choice between 2 different treatments (oral and intravenous) for systemic lupus erythematosus
Achaval and others²⁹	1	Whether or not to have total knee arthroplasty to treat knee osteoarthritis in cases of pain and disability unresponsive to medical treatment
Akl and others³⁰	1	Whether or not to take inhaled steroids to treat chronic obstructive pulmonary disease
Alfaleh and others³¹	1	Whether to use prophylactic indomethacin therapy or wait and treat symptomatic patent ductus arteriosus in extremely-low-birth-weight infants
Allen and others³²	1	Whether or not to have PSA testing to screen for prostate cancer
Bastian and others³³	1	Whether or not to take hormone replacement therapy
Bekker and others³⁴	1	Whether or not to have a diagnostic test for Down syndrome after receiving a positive screen in maternal serum screening
Berger and others³⁵	1	What lifestyle changes to implement in the context of a cardiac condition
Brenner and others³⁶	2	Whether or not to be screened for colorectal cancer, and, if yes, what screening test to use (4 unlabeled screening tests designed to simulate fecal occult blood testing, sigmoidoscopy, colonoscopy, or a radiological test such as computed tomography colonography)
Breslin and others^37,38	1	Choice between treatments for type 2 diabetes
Brundage and others^39,40	1	Choice between combined modality treatment or radiation alone for non–small-cell lung cancer
Chiew and others⁴¹	1	Whether or not to have chemotherapy in addition to supportive care in the context of advanced breast cancer
Clancy and others⁴²	1	Choice between being immunized for hepatitis B, screened for antibodies and immunized if negative, or not immunized unless exposed
Costanza and others⁴³	1	Whether or not to have PSA testing to screen for prostate cancer
Culver and others⁴⁴	1	Whether to have a risk-reducing mastectomy, risk-reducing salpingo-oopherectomy before age 50, or take tamoxifen for 5 y
Dolan and Frisina⁴⁵	1	Choice between 5 active options (annual FOBT, flexible sigmoidoscopy every 5 y, annual FOBT + flexible sigmoidoscopy every 5 y, barium enema every 5 y, colonoscopy every 10 y) and a wait-and-see approach to colorectal screening
Dolan and others⁴⁶	1	Which medication to use for treatment of knee osteoarthritis pain
Dorfman and others⁴⁷	2	Whether or not to have PSA testing to screen for prostate cancer
Drake and others⁴⁸	1	Whether or not to have prenatal screening for major fetal chromosome abnormalities, and if yes, choice between different tests or combinations of tests (maternal serum screening, chorionic villus sampling, amniocentesis)
Durand and others⁴⁹	2	Whether or not to have amniocentesis after having been identified as high risk via less invasive screening tests
Emmett and others⁵⁰	1	Choice between a repeat cesarean or vaginal birth after cesarean (VBAC)
Evans and others⁵¹	1	Whether or not to have PSA testing to screen for prostate cancer
Feldman-Stewart and others⁵²	1	Choice between watchful waiting, radiation, and surgery for treatment of early-stage prostate cancer
Feldman-Stewart and others^20,53	2	Choice between 4 main options for early-stage prostate cancer (watchful waiting, surgery, external beam radiation, and brachytherapy)
Fraenkel and others⁵⁴	1	Choice between treatments for knee pain (capsaicin, acetaminophen, anti-inflammatory drugs, intra-articular injections, exercise, exercise + medications)
Fraenkel and others^55,56	1	Whether or not to take warfarin, aspirin, or neither to prevent stroke
Frosch and others^57,58	1	Whether or not to be screened for prostate cancer
Garvelink and others⁵⁹	1	Whether or not to use fertility preservation methods and, if so, which (cryopreservation of embryos, cryopreservation of ovarian tissue and cryopreservation of oocytes)
Gattellari and Ward⁶⁰	1	Whether or not to have PSA testing to screen for prostate cancer
Goodlin and others⁶¹	1	A variety of decisions relevant to heart failure
Green and Levi⁶²	1	Preferences for end-of-life care
Hawley and others⁶³	1	Choice between methods for colorectal screening
Hunter and others²²	1	Whether or not to have prenatal screening for major fetal chromosome abnormalities and, if yes, choice between different tests or combinations of tests (maternal serum screening, chorionic villus sampling, amniocentesis)
Jackson and others^64,65	1	Whether or not to immunize one’s child with MMR vaccine
Jibaja-Weiss and others^66–68	1	Choices between treatments for early breast cancer
Karel and others⁶⁹	1	Preferences (values and goals) for advance care planning in case of decisional incapacity
Karel and others⁷⁰	3	Preferences for advance care planning in case of decisional incapacity
Kasper and others⁷¹	1	Whether or not to have immunotherapy for multiple sclerosis, and if yes, what kind of therapy to have
Kennedy and others⁷²	1	Choice between treatment options for menorrhagia (advice and reassurance, addressing possible iatrogenic causes, drug therapy, or surgery such as hysterectomy or endometrial destruction)
Labrecque and others⁷³	1	Whether or not to have a vasectomy
Lalonde and others^74,75	1	Whether or not to start lifestyle changes and antihypertensive or lipid-lowering pharmacotherapy
Legare and others⁷⁶	1	Whether or not to use natural health products for menopausal symptoms (and, if so, which one[s])
Leighl and others⁷⁷	1	Choice between supportive care only, supportive care plus usual care chemotherapy, or supportive care plus clinical trial participation in the context of metastatic non–small-cell lung cancer
Lepore and others⁷⁸	1	Whether or not to be screened for prostate cancer
Lerman and others⁷⁹	1	Whether or not to have genetic testing for BRCA1
Lewis and others⁸⁰	1	Whether or not to be screened for colorectal cancer
Llewellyn-Thomas and others^81,82	1	Choice between watchful waiting, alpha blocker, and transurethral resection of the prostate in the context of benign prostatic hyperplasia
Matheis-Kraft and Roberto⁸³	1	Preferences for care in case of decisional incapacity
Mathieu and others⁸⁴	1	Whether or not to continue or stop mammography screening at age 70
Mathieu and others⁸⁵	1	Whether to begin mammography screening at age 40–49 or wait until age 50
Montgomery and others⁸⁶	1	Whether or not to start drug therapy for hypertension
Moumjid and others⁸⁷	1	Choice between treatments and combinations of treatments for breast cancer (mastectomy or lumpectomy, axillary dissection or sentinel node biopsy, adjuvant chemotherapy or not)
Myers⁸⁸	3	Whether or not to participate in a prostate cancer prevention clinical trial
Nassar and others^89,90	1	Whether or not to have an external cephalic version for a breech-presenting baby
O’Connor and others^13,91–93	1	Whether or not to take hormone replacement therapy after menopause
van Peperstraten and others¹⁸	1	Choice of how many embryos to transfer during in vitro fertilization (either 1 or 2)
Peshkin and others⁹⁴	1	Whether or not to disclose BRCA1/2 genetic testing results to one’s minor children and, if so, how
Pieterse and others⁹⁵	1	Whether or not to have preoperative radiotherapy (prior to surgery) for treating rectal cancer
Pignone and others⁹⁶	2	Whether or not to be screened for colorectal cancer, and, if yes, what screening test to use (4 unlabeled screening tests designed to simulate fecal occult blood testing, sigmoidoscopy, colonoscopy, or a radiological test such as computed tomography colonography)
Pignone and others⁹⁷	2	Whether or not to be screened for prostate cancer
Protheroe and others⁹⁸	1	Whether or not to initiate active treatment for menorrhagia, choice between treatments
Raats and others⁹⁹	1	Generic design, not specific to a given decision
Rimer and others^100,101	1	Whether or not to have mammography to screen for breast cancer
van Roosmalen and others¹⁰²	1	Choice between intensive screening and prophylactic surgery for breasts and/or ovaries
Rothert and others¹⁰³	1	Whether or not to take hormone replacement therapy
Ruffin and others¹⁰⁴	1	Whether or not to be screened for colorectal cancer, and, if yes, what screening test to use (FOBT, flexible sigmoidoscopy, colonoscopy, or double contrast barium enema)
Sawka and others^105,106	1	Choice between lumpectomy with radiation and mastectomy for early breast cancer
Schapira and others¹⁰⁷	1	Whether or not to take hormone replacement therapy
Schonberg and others¹⁰⁸	1	Whether or not to have mammography to screen for breast cancer
Schwalm and others¹⁰⁹	1	Whether to use femoral or radial access for coronary angiography
Segal and Shahar¹¹⁴	1	Whether or not to have a triple screen test, amniocentesis, and second trimester ultrasound
Sheridan and others¹⁹	1	Whether or not to initiate behaviors to prevent coronary heart disease, and, if so, which behaviors
Shorten and others^111,112	1	Choice between a repeat cesarean or vaginal birth after cesarean
Singer¹¹³	1	Choice between ways to deal with an unplanned pregnancy (abortion, adoption, parenting)
Smith and others^114,115	2	Whether or not to have screening for bowel cancer via FOBT screening every 2 y
Sorenson and others¹¹⁶	1	Whether or not to have hemophilia A genetic carrier testing
Thomson and others¹¹⁷	1	Whether or not to take warfarin to prevent stroke
Thomson and others¹¹⁸	1	Choice between different treatments for hypertension
Tiller and others^119,120	1	Choice of risk management strategies for ovarian cancer (annual transvaginal ovarian ultrasound then annual CA125 serum testing after menopause, prophylactic surgery, chemoprevention, prophylactic oophorectomy)
Vandemheen and others^121,122	1	Whether or not to be screened for prostate cancer
Volk and others²¹	1	Whether or not to be screened for prostate cancer
Wakefield and others^124–125	2	Whether to not to undergo genetic testing or defer decision
Wallace and others¹²⁶	1	Whether or not to immunize one’s child with MMR vaccine
Wong and others¹²⁷	1	Whether to have antiestrogens, radiation, both, or neither after lumpectomy
Wroe and others¹²⁸	1	Whether or not to immunize one’s baby with standard first childhood vaccines (diphtheria, tetanus, pertussis, hepatitis B, haemophilus influenzae type b, and polio)

Note: Articles are presented alphabetically by the last name of the first author of the first publication in the group of articles. PSA = prostate-specific antigen; FOBT = fecal occult blood test; MMR = measles, mumps, and rubella.

Table 2

Decision Contexts (n = 98 Values Clarification Methods)

	n (%)
Types of decisions^a
Treatment	42 (43)
Screening	31 (32)
Prevention	19 (19)
Genetic testing	6 (6)
Diagnostic testing	4 (4)
Decision structure
Whether or not to accept an option	45 (46)
Whether or not to accept an option + choice between options	27 (28)
Choice between 2 or more options	24 (24)
Other	2 (2)
Number of options
2	51 (52)
Between 3 and 6	26 (27)
More than 6	17 (17)
Other	4 (4)
Clinical context
Cancer	48 (49)
Reproductive health	19 (19)
Cardiovascular health	9 (9)
Other chronic conditions	8 (8)
Advance care planning	6 (6)
Vaccine preventable diseases	4 (4)
Other	4 (4)
Clinical context: cancer type
Breast and/or ovarian	17 (17)
Prostate	16 (16)
Colorectal	13 (13)
Lung	2 (2)
Reproductive health
Female	17 (17)
Male	1 (1)
Both	1 (1)
Designed primarily for use by
Both women and men	44 (45)
Women	35 (36)
Men	19 (19)

Not mutually exclusive: 5 methods classified in 2 categories each, 1 method classified in all 5 categories.

Methods in this review addressed a wide range of decisions. Cancer was the most common clinical context (49%), followed by reproductive health (19%). Screening and treatment decisions dominated the types of decisions, representing 75% of contexts. Nearly half of methods (46%) were designed to support a decision of whether or not to accept an option, while others supported a decision between 2 or more options (24%) or a combination (28%) in which users would decide whether or not to pursue an option (for example, a screening test) and would then choose from among types of that option (for example, different screening tests). Among included methods, 45% were designed for use by both men and women, 36% only by women, and 19% only by men. The difference between these latter 2 statistics is attributable to differences in the clinical context of reproductive health, in which 17 methods addressed issues relevant to women’s reproductive health and 1 addressed an issue (vasectomy) relevant to men’s reproductive health.

Foundations

Using a broad, inclusive definition, only 38% of explicit values clarification methods were built on a foundation such as a theory, framework, model, or theoretically based approach applicable to values clarification. Among those that did, most (28/38, or 74%) referenced or implied theories or theoretically based approaches such as expected utility theory or conjoint analysis, which are not descriptive theories of values clarification, meaning they do not describe the details of how people engage in the process of values clarification. Few methods (21%) were based on a previous design of a values clarification method. Considering the full set of published methods, most (64%) cited no relevant guidelines. Of those that did, the IPDAS, first published in 2006, was the most frequently used overall (26%). Of the 78 methods described in articles published in 2007 or later, after these standards were published, 56% (44/78) still cited no guideline. Table 3 gives details about Foundations.

Table 3

Foundations (n = 98 Values Clarification Methods)

	n (%)
Theories, models, frameworks used for values clarification method^a
None	61 (62)
Expected utility theory	12 (12)
Conjoint analysis	7 (7)
Analytic hierarchy process	4 (4)
Differentiation and consolidation theory	3 (3)
Fast and frugal heuristics	3 (3)
Multiattribute utility theory	2 (2)
Other (information processing paradigm, Rath’s valuing process, treatment tradeoff approach, ask-tell-ask model of communication, probability-tradeoff technique, Bayes theory, active listening, decision conflict theory)	8 (8)
Previous work or design^b
Intervention draws on previous work	66 (67)
Previous work specifically about values clarification	26 (27)
Intervention based on previous design	30 (31)
Previous design specifically for values clarification	21 (21)
No previous work or design cited	2 (2)
Guidelines used^c
No guidelines used or cited	63 (64)
IPDAS	25 (26)
IPDAS cited, but not used	4 (4)
CREDIBLE	4 (4)
National Health and Medical Research Council guidelines on presenting evidence to consumers	2 (2)
American College of Physicians Guidelines (1992) for counseling postmenopausal women about preventive hormone therapy	1 (1)
Guidelines for dashboard design (Few S. Information Dashboard Design. Sebastopol, CA: O’Reilly Media; 2005.)	1 (1)

Not mutually exclusive: 1 method used 2 theories.

Indentations represent subcategories contained within the category.

Not mutually exclusive: 1 method used both CREDIBLE and International Patient Decision Aid Standards (IPDAS).

Taxonomy of Design Features

The categories within each design feature are described and illustrated with examples in Table 4. Most categories are mutually exclusive. The distinction between mutually exclusive and non–mutually exclusive frequencies is noted for each entry in the table, and details are provided in the table. Pairwise comparisons revealed that no design feature entirely determined any of the others, and no latent factor was identified.

Table 4

Design Features (n = 98 Values Clarification Methods)^a

		n (%)
Type of values clarification^b
Pros and cons		35 (36)
With weighting^c	User explicitly weighs pros and cons in some way, e.g., Likert scales, rating likelihood, rating importance, ranking importance, shading of boxes, or other comparative weighing method	29^c (30)
With binary	User gives a binary response to each pro or con, for example, “Does this sound like you?”	7 (7)
Viewing or listing only	Involves only listing or viewing pros and cons; no explicit weighing or other response required	1 (1)
Math model based		19 (19)
Decision analysis	Involves utility estimation via standard gamble or other methods and a decision analytic model; may include viewing a decision tree	11 (11)
Conjoint	User responds to multiple sets of attributes with varying levels, either one set at a time or choosing between sets via discrete choice analysis	7 (7)
Analytical hierarchy process	Involves decomposing decision into its attributes and weighing attributes against each other	1 (1)
Rating	Involves rating attributes or outcomes, e.g., using importance ranking scales (however, does not involve explicit identification of each attribute as a pro or con; in such a case, type is “pros and cons with weighting”)	18 (18)
Prioritization	Involves ranking a full list of concerns and/or selecting the top n concerns	16 (16)
List of concerns		13 (13)
List and discuss	Involves discussing concerns from one’s own list or from a preidentified list of concerns, possibly using a semistructured interview format	6 (6)
List only	Involves listing one’s concerns or going through a preidentified list of concerns without discussing them	4 (4)
Discuss only	Involves a discussion that takes place without a predefined list or list developed by the individual	3 (3)
Threshold^d		9 (9)
Attributes	Involves considering tradeoffs according to individual attributes, e.g., comparing medications to treat type 2 diabetes according to their impacts on blood sugar, weight, and required frequency of testing³⁷	4 (4)
Probability	Involves selecting between 2 or more different probabilities	2 (2)
Time	Involves selecting between 2 or more different lengths of time in given health states	2 (2)
Outcomes or processes	Involves considering tradeoffs according to individual outcomes or processes, e.g., choosing between statements, “It is alright if my family or doctor makes medical decisions for me,” and “If I am able, I want to make medical decisions for myself”⁷⁰	1 (1)
Social matching	User observes different characters’ decisions and/or decision-making processes and identifies 1 or more characters with whom she or he identifies	2 (2)
Other	Any other type not described in above list	1 (1)
Position in decision aid (DA)
After information	Values clarification method placed after information section about the decision	66 (67)
Before information	Values clarification method placed before information section about the decision	2 (2)
Between information sections	Values clarification method placed between different information sections about the decision	1 (1)
Throughout DA as an additional component	Values clarification method placed throughout DA in addition to separate information sections	5 (5)
Throughout DA as entirety of intervention	Values clarification method forms entirety of intervention	5 (5)
N/A (not a DA)	Not applicable because values clarification method not contained within a DA	18 (18)
Unclear from article	Position is not clear from published description	1 (1)
Solo activity
Independently	User completes values clarification independently	58 (59)
With research staff	User completes values clarification with research staff, e.g., research assistant	17 (17)
With a health care provider	User completes values clarification with a health care provider, e.g., physician, nurse, nurse educator, counselor, etc.	14 (14)
With family or friends	User completes values clarification with personal contacts, e.g., spouse, caregiver, friend, or family member	2 (2)
With person not specified	User completes values clarification with someone else, but that person’s role or relationship with the user is not clear	1 (1)
Combinations (independently and/or with others)	Values clarification is completed partly independently, partly with others	5 (5)
Other	Any other setup not described above	1 (1)
Media
Paper	Paper media describes pamphlets, worksheets, workbooks, and the like and may be accompanied by audiotapes and verbal components, e.g., a telephone consultation after completing the paper-based exercise	38 (39)
Computer	Values clarification completed on a desktop computer, laptop, smartphone, tablet, or other device with an interface and operating system; this includes online or web-based exercises or those using CD-ROM, and may include multimedia such as audio, video, or animation	37 (38)
Verbal	Values clarification designed for verbal administration rely primarily on discussion and may also include paper exercises, technology to perform calculations, or visual aids such as cards or decision boards; verbal administration may be done in person or by telephone	23 (23)
Tradeoffs
Implied	Design presents tradeoffs implicitly, e.g., a table of pros and cons and asking people to indicate the importance of each by shading in boxes	42 (43)
Explicit	Design presents tradeoffs explicitly, e.g., a discrete choice task, ranking task, or a task in which users must choose between a list of pros and cons	31 (32)
Neither implied nor shown explicitly	Tradeoffs not presented at all, e.g., the design allows people to rate all factors as “very important” without giving cues about the need to consider competing priorities	24 (24)
Other	Any other presentation not described in above list	1 (1)
Visual metaphors^e
None	No visual metaphor	58 (59)
Strength of response	E.g., feeling thermometers, rules, star rating systems	18 (18)
Tradeoffs	E.g., balance scales, weigh scales	12 (12)
Proportions	E.g., pie graphs, bar graphs	5 (5)
Time	E.g., line graphs with time axis	3 (3)
Other	E.g., jewelry box, cards	3 (3)
Open- or closed-ended
Closed-ended	All elements (e.g., attributes of options) are preset; user cannot add new elements and instead responds to a preset list of attributes	60 (61)
Open-ended	Values clarification method is entirely open-ended; user defines elements, e.g., generates one’s own list of attributes	12 (12)
Mixed	Some elements are preset, but user may add additional elements, e.g., starting with a list of pros and cons with the possibility of adding to it	25 (26)
Other	Any other setup not described above	1 (1)
Elicitation process^f
Direct scaling (all)	User assigns a value on a scale	57 (58)
Direct scaling (no shading)	Scaling is performed by some non–shading-related method, e.g., choosing a number on a Likert scale or marking a point on a visual analog scale	49 (50)
Direct scaling (shading)	Scaling is performed by shading in a box fully, partially, or not at all	8 (8)
Ranking	User ranks items, e.g., from most to least important; this can include completely ranking a list of issues or simply selecting the top 3 issues from a larger set	19 (19)
Discrete choice	User chooses between 2 or more sets of options. For example, users may be asked whether they would prefer a treatment that requires injections, has minor side effects, and a moderate out-of-pocket cost or a treatment taken by pill, with moderate side effects and a low out-of-pocket cost. Or, they may be asked to choose between 2 cards, one representing a reason to have a screening test and the other describing a related reason not to take it and are then asked to choose which is more important to them.	12 (12)
Answer questions	User answers open-ended questions verbally or in other ways	11 (11)
Standard gamble or similar	User completes a standard gamble or other similar technique that involves iterating values to find a point of indifference	7 (7)
Personal relevance	Users respond on the basis of personal relevance, identifying issues that apply to them or statements that “feel like me”	6 (6)
List items	Users are asked to make a list of items that are relevant to the decision and matter to them	4 (4)
Choose character	User chooses a character from a story-based exposition with whom she or he identifies	1 (1)
Unclear	Response process is not entirely clear from description	3 (3)
Other	Response process is some other type not captured by categories above	2 (2)
Response measures^f
Ordinal/Interval	Ordinal or interval data	38 (39)
Ranking	Results from placing items in an ordered list	10 (10)
Likert scale or nonspecified “rating scale”	Numeric value on a linear scale	32 (33)
Ratio	Ratio data	36 (37)
Visual weight	Proportion of shading in a box	8 (8)
Visual analog scale rating	Rating on a visual analog scale	14 (14)
Utility-related	E.g., relative preference, time tradeoff	3 (3)
Utility	Utility value	12 (12)
Nonnumeric	Nonnumeric data, e.g., open-ended answers to questions	20 (20)
Nominal	Nominal data	18 (18)
Binary	Choice between 2 items	15 (15)
Categorical choice	Choice between 3 or more items	3 (3)
Other	Some other type of data not captured by categories above	1 (1)
Values exploration
Revision technically possible but not explicitly supported or encouraged	User may technically reflect and revise (e.g., may complete paper-based values clarification method in pencil, back button not disabled in computer-based version), but this is not explicitly encouraged	64 (65)
No revision, one shot	User may not revise stated values	21 (21)
Encouraged to revise	All users encouraged to reflect on and revise stated values	9 (9)
Encouraged to revise if decision inconsistent with stated values	Users encouraged to reflect and revise only if decision intentions fail to align with stated values	2 (2)
Unclear	Possibility for values exploration unclear from published description	4 (4)
Implications
Not presented explicitly, but implications might be inferred	Implications might be inferred because of the way values are elicited or displayed, e.g., shaded boxes on a weigh scale	39 (40)
Not presented	Implications are not presented	29 (30)
Presented explicitly	Implications of stated values (e.g., “According to your answers, the best choice for you is . . .”) explicitly presented to user	28 (29)
Before indicating decision	Implications presented before user indicates his or her decision intentions	22 (22)
After indicating decision	Implications presented after user indicates his or her decision intentions	4 (4)
If a given option was selected	Implications presented only if user selects specific option within values clarification method	2 (2)
Unclear	Presentation of implications is not clear from published description	2 (2)
Decision intentions
No	User is not asked for her or his decision intentions	44 (45)
Yes (leaning)	User is asked which way she or he is leaning; this includes methods that ask for a decision but allow some ambiguity by having an option to indicate “unsure” or “uncertain”	37 (38)
Yes (decision)	User is asked for her or his decision, with no option for uncertainty	16 (16)
Unclear	Presence or absence of this step is not clear from published description	1 (1)

Categories are mutually exclusive unless indicated otherwise.

Not mutually exclusive: 11 methods classified as 2 types, 3 methods classified as 3 types, and 2 combine 2 subtypes under pros and cons.

Indentations represent subcategories contained within the category.

These types may have similar user experiences as decision analysis and conjoint analysis, particularly discrete choice analysis. However, types classified under “Threshold” do not involve calculating utilities in any way, nor do they involve decision analytic modeling.

Not mutually exclusive: 1 method used proportions and time visual metaphors together.

Not mutually exclusive.

Type of Values Clarification Method

Prior to data extraction, we drafted a list of possible types, based on previous typology in the literature.¹²⁹ This list was then refined through the extraction process by 2 authors (H.O.W., L.D.S.) using iterative discussions and revisions. The final list consists of 7 broad types and 17 possible subtypes in total. Values clarification methods using a multistep process may be classified under more than 1 type. Values clarification methods in this review represented a diverse range. The majority were pros and cons (36% of total), math model based (19%), or rating (18%) methods.

Position in Decision Aid

For values clarification methods that were contained within a decision aid, we extracted data about where in the decision aid the method was placed, for example, before or after an information section, between information sections, or throughout the intervention. Most methods in the review (79/98, 81%) were contained in a decision aid; of these, most (66/79, 83%) came after a complete information section.

Solo Activity

Most values clarification methods (59%) were designed to be completed independently by the patient or person making the decision. Of methods designed to be completed with others, the most common other person was a research assistant (17%) followed by a health care provider (14%). A small number (2%) were designed to be completed with a spouse, caregiver, friend, or family member.

Media

We extracted the medium used for each values clarification method, specifically, whether the method was designed to be completed on paper, a computer, or verbally. We note that although the information in a decision aid might be presented via another format such as a DVD, an explicit values clarification method requires an interactive medium. Methods in the review were roughly balanced between paper (39%), computer based (38%), and verbal (23%).

Tradeoffs

The need for values clarification methods arises out of the challenges of making preference-sensitive decisions in which tradeoffs exist. Thus, an important aspect of the decision-making process involves understanding and determining how one feels about the relevant tradeoffs. Tradeoffs were represented explicitly in less than a third of methods (32%).

Visual Metaphors

We examined whether or not each values clarification method used any sort of visual metaphor as part of the design. By visual metaphor, we mean any sort of graphical element that was part of the values clarification method itself, for example, a set of weigh scales to illustrate the concept of a tradeoff.¹³ This categorization does not apply to graphics within a decision aid that were not part of the values clarification method, such as an icon array displaying risks. Most values clarification methods (59%) contained no visual metaphor.

Open- or Closed-Ended

We noted whether the sets of attributes presented to users were closed-ended, open-ended, or mixed. Closed-ended means that users could not add concerns that were not already listed, whereas open-ended and mixed allowed people to include additional items of concern. The majority of methods (61%) were closed-ended, meaning that users could not add decision attributes that were not prespecified by the designers.

Elicitation Process

We examined what process the user of the values clarification method might go through to give responses about her or his values relevant to the decision. For example, the process might involve answering questions, completing standard gamble exercises, or directly rating the importance of each attribute of a decision. The majority of methods (58%) used direct scaling.

Response Measure

The response measure refers to the type of data obtained via the elicitation process. For example, data elicited might be a categorical choice yielding nominal data or a utility (i.e., a number on the interval [0,1] that represents the value of a health state or outcome) yielding ratio data. This design feature has implications for what can be done with a user’s responses. For example, utilities can be used in decision analytic models, which might subsequently feed back a recommended option to the user, whereas verbal statements cannot be as easily integrated into a recommendation by an algorithm. Most methods generated ordinal/interval (39%) or ratio (37%) data.

Values Exploration

Values clarification is often an iterative discovery process,^130,131 and it can take time for preferences to stabilize.^4,16 To establish how and whether values clarification methods supported such exploration, we extracted data about whether each design explicitly encouraged an iterative process of revision, implicitly allowed such a process but did not encourage it, or did not support iteration and required users to identify and express their values in a single attempt. Very few methods explicitly encouraged all users (9%) or users who expressed decision intentions that were incongruent with their stated values (2%) to explore their values in an iterative discovery process. Most methods (65%) were designed such that iterative revision was technically possible (e.g., users could go back within a website or could complete a paper worksheet in pencil) but not explicitly encouraged.

Implications

We extracted whether or not the design of each values clarification method showed users the implications of their expressed values. For example, a method that explicitly presents implications might give a recommended option or might present scores to show how well or poorly each option fits with the user’s responses. Alternatively, a method may not explicitly show implications but may allow people to infer such information, for example, by roughly comparing the weights they have assigned to the pros versus the cons of a choice. Less than one-third of methods (29%) explicitly presented users with the implications of their stated values.

Decision Intentions

We extracted data about whether or not each method included a step in which the user is asked to indicate his or her decision intentions, whether a clear decision or a direction in which he or she is leaning. This did not include cases in which decision intentions were recorded as an outcome during a study; it refers specifically to cases in which the user was asked to express her or his decision intentions within the values clarification process itself. Slightly more than half of methods in total (54%) asked users about their decision intentions, either by asking for their decision (16%) or toward which decision they are leaning (38%).

Discussion

This review demonstrates that a diverse array of explicit values clarification methods are used across a range of health decisions. It is unknown whether a given values clarification method might be equally effective for different decisions. In other words, are designs and design features specifically suited to particular decisions, or can their use in one context be justified by empirical results in another? Such comparisons are difficult because of structural differences between different decisions. For example, choosing between 2 options is fundamentally different from choosing between 3 options,¹³² and different designs are possible for 2 versus 3 options. Further research is needed regarding how best to support values clarification across different decisions.

Despite our use of an inclusive and generous description of theory, framework, model, or theoretically based approach, we found that few methods built on such a foundation. The overall low use of theories, frameworks, or models may be problematic, as such foundations can help structure hypotheses that might ultimately allow researchers to understand why and how a given values clarification method does or does not work. Similarly, few explicit values clarification methods were specifically based on previous designs. This may be a reflection of the lack of designs that have been demonstrated to be effective or simply that the field is relatively new. Further research is needed into optimal designs of explicit values clarification methods to identify and advance the use of effective design features.

The 12 design features within the taxonomy describe the heterogeneity of values clarification methods. These design features are sufficiently independent that each one should be considered when designing values clarification methods, and they should be included in reports.

Limitations

While we endeavored to capture all published accounts of explicit values clarification methods, it is possible that some were missed. Similarly, for reasons of scope, we did not search gray literature, nor did we contact authors to request copies of values clarification methods that were insufficiently described in the literature. Second, although our data extraction process was such that each element was examined by at least 2 authors and we contacted authors in cases in which a description was unclear, it is possible that we misunderstood some descriptions. Finally, although we developed our taxonomy using rigorous methods, our choices of design features in the taxonomy were based on the authors’ judgment and may not represent the entirety of important design features.

Conclusions

This systematic review formally demonstrates that there is a diverse array of explicit values clarification methods in use, most with neither theoretical nor empirical basis for their design. Given the growing social, legislative, and policy imperatives to help people make health-related decisions that reflect what is important to them, more research is needed into optimal designs of values clarification methods.

To build an evidence base and help move this emerging field forward, we encourage developers of values clarification methods to design with awareness of relevant theory¹⁴ and previous designs, publish adequate descriptions of the design of their values clarification method using the taxonomy described in this review, and provide clear rationales for their design choices. There is a need for empirical evidence about the different choices for design features in this taxonomy. We advocate for more research to isolate the effects of different design features in order to better equip researchers and practitioners in medical decision making to help people clarify their values and make their best possible health decisions.

Footnotes

Acknowledgements

The authors would like to thank Whitney Townsend, MLIS, and William Witteman, MIS, for their work in developing and executing the search strategy for this review, as well as Peter Ubel, MD, for his comments on early draft versions of this article.

This work was partly supported by Robert Derzon postdoctoral grants awarded by the Informed Medical Decisions Foundation (now Healthwise) to Holly Witteman and Laura Scherer. During most of the time of this research, Arwen Pieterse was supported by a postdoctoral research fellowship from the Dutch Cancer Society. Holly Witteman is supported by a Research Scholar Junior 1 career development award from the Fonds de recherche du Québec–Santé. The funders had no role in study design, collection, analysis and interpretation of data, writing of the report, or the decision to submit the article for publication.

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