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Chapter 1
in making a decision about whether to participate in a clinical trial, i.e., individuals could
choose between two options: (1) participate in the clinical trial or (2) receive standard
treatment [32]. Those two options have certain characteristics. For example, participation in
the clinical trial allows participants to receive an innovative treatment with potential new
benefits, while receiving standard treatment does not come with the burden of additional
monitoring [32]. The VCM could then help individuals to identify their personal values and
preferences, e.g., the VCM might help someone to identify that they highly value the
potential benefits which would reveal their overall preference for the clinical trial as opposed
to standard treatment [32].
Generally speaking, VCMs tend to be divided into implicit VCMs that lack interactive
elements (e.g., tables that make options' characteristics explicit and leave the actual value
clarification to the user), and explicit VCMs that include interactive elements for value
clarification [34]. In general, explicit VCMs are much more studied [34] and several studies
have found promising effects compared to implicit VCMs. In a study by Abhyankar et al. [30],
no VCM was compared with both an implicit VCM and an explicit VCM in the previously
described decision-making context (i.e., whether to participate in a clinical trial). It was found
that the decisions of participants in the explicit VCM group showed more characteristics of
informed decision making, that participants felt less ambivalent about the options, and less
uncertain about the decision and their values overall [32]. Other evidence especially points
to positive long-term effects: In a study by Feldman-Stewart et al. [37] it was shown that
decisional conflict was unaffected by their explicit VCM immediately after having used the
DA. On the other hand, positive effects emerged on feeling prepared for decision making
after the decision had been made, while positive effects on decisional regret only emerged
after a year [37].
Explicit VCMs can be further subdivided and range from methods such as rating scales (e.g.,
by making use of Likert scales) to math model-based methods [38]. Recent evidence has
shown that the potential effect of explicit VCMs on value-consistent decision making is
moderated by whether users are supported in understanding the implications of their values
[39,40]. To illustrate, Witteman et al. have shown in a series of studies that the most effective
VCMs in terms of value-consistent decision making show VCM-users to what extent options
(fail to) align with their identified values [40]. For example, in one of their studies,
participants were given an online application consisting of dynamic web sliders representing
both values and preferences. Those web sliders were then linked, meaning that for
participants who indicated that a particular value is very important to them, the slider
representing the preference moved equally [40]. Another possible way to support users in
understanding the implications of their values would be to provide tailored advice based on
these values that shows users which options match their values and can thus be considered
preferred options. However, all of this has been tested in a healthcare context only and up
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