Tolerance of uncertainty: Conceptual analysis, integrative model, and implications for healthcare

Social Science & Medicine 180 (2017) 62e75

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Social Science & Medicine journal homepage: www.elsevier.com/locate/socscimed

Review article

Tolerance of uncertainty: Conceptual analysis, integrative model, and implications for healthcare Marij A. Hillen a, 1, Caitlin M. Gutheil b, Tania D. Strout c, Ellen M.A. Smets a, Paul K.J. Han b, *, 1 a b c

Department of Medical Psychology - Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands Center for Outcomes Research and Evaluation, Maine Medical Center Research Institute, Portland ME, USA Department of Emergency Medicine, Maine Medical Center, Portland ME, USA

a r t i c l e i n f o

a b s t r a c t

Article history: Received 24 August 2016 Received in revised form 27 February 2017 Accepted 12 March 2017 Available online 14 March 2017

Rationale: Uncertainty tolerance (UT) is an important, well-studied phenomenon in health care and many other important domains of life, yet its conceptualization and measurement by researchers in various disciplines have varied substantially and its essential nature remains unclear. Objective: The objectives of this study were to: 1) analyze the meaning and logical coherence of UT as conceptualized by developers of UT measures, and 2) develop an integrative conceptual model to guide future empirical research regarding the nature, causes, and effects of UT. Methods: A narrative review and conceptual analysis of 18 existing measures of Uncertainty and Ambiguity Tolerance was conducted, focusing on how measure developers in various fields have defined both the “uncertainty” and “tolerance” components of UTdboth explicitly through their writings and implicitly through the items constituting their measures. Results: Both explicit and implicit conceptual definitions of uncertainty and tolerance vary substantially and are often poorly and inconsistently specified. A logically coherent, unified understanding or theoretical model of UT is lacking. To address these gaps, we propose a new integrative definition and multidimensional conceptual model that construes UT as the set of negative and positive psychological responsesdcognitive, emotional, and behavioraldprovoked by the conscious awareness of ignorance about particular aspects of the world. This model synthesizes insights from various disciplines and provides an organizing framework for future research. We discuss how this model can facilitate further empirical and theoretical research to better measure and understand the nature, determinants, and outcomes of UT in health care and other domains of life. Conclusion: Uncertainty tolerance is an important and complex phenomenon requiring more precise and consistent definition. An integrative definition and conceptual model, intended as a tentative and flexible point of departure for future research, adds needed breadth, specificity, and precision to efforts to conceptualize and measure UT. © 2017 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Keywords: Uncertainty Ambiguity Tolerance Intolerance Conceptual analysis Narrative review

1. Introduction Uncertainty is interwoven in daily life and in virtually all clinical situations experienced by patients and health professionals. Uncertainty in health care pertains to numerous unknowns: whether

* Corresponding author. Center for Outcomes Research and Evaluation, Maine Medical Center Research Institute, 509 Forest Avenue, Suite 200, Portland, ME 04101, USA. E-mail address: [email protected] (P.K.J. Han). 1 These authors have contributed equally to this work.

a patient has or will develop a particular condition; how that condition will evolve; to what extent a particular treatment is beneficial; and whether a patient is receiving the right care, in the right place, at the right time, from the right people. The sheer number and variety of these unknowns make uncertainty a ubiquitous problem in health care. Uncertainty in health care is also a growing problem due to the increasingly rapid emergence of new medical technologies that outpace the development of evidence regarding their benefits, harms, and implications. Meanwhile, public awareness of limitations in medical knowledge has been heightened by expanding

http://dx.doi.org/10.1016/j.socscimed.2017.03.024 0277-9536/© 2017 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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mass media coverage of medical controversies and the rise of the evidence-based medicine and shared decision making movements. Uncertainty is a critical phenomenon in health care because of its many potential psychological effects, both negative and positive. Uncertainty can be aversive; large bodies of research from multiple disciplines, both in and outside of the health care domain, have demonstrated that uncertainty provokes fear, worry and anxiety, perceptions of vulnerability, and avoidance of decision-making. Individuals thus engage in a variety of different responses to both minimize the negative effects and maximize the positive effects of uncertainty. Some reduce uncertainty by seeking information, while others minimize or ignore uncertainty by focusing their attention elsewhere. Other individuals acknowledge uncertainty but become fearful and disempowered. Still others seek out uncertainty and ultimately derive benefit from it. Depending on the specific context, the same individuals may exhibit varied responses to uncertainty (Durrheim and Foster, 1997). Understanding these individual differences in people's responses todor tolerance ofduncertainty is an increasingly important focus of research, particularly in the domain of health care. The capacity of patients and health care professionals to tolerate uncertainty can affect the extent to which both parties form therapeutic relationships, seek and exchange information, and engage in shared decision making. Individual differences in uncertainty tolerance may influence health behaviors, the quality of health care, and health outcomes. Uncertainty tolerance (UT) has thus become a growing area of interest among health care researchers, dating back to seminal work by Gerrity et al. (1990) and Geller et al. (1990, 1993), and including more recent efforts (Han et al., 2009; Hancock et al., 2015; Kuhn et al., 2009; Schneider et al., 2010; Schor et al., 2000). Past research on UT in health care, however, has focused mainly on health care professionals rather than patients, and on empirical research rather than theory development. It has also been disconnected from important research outside of the health care domain. A large and rich body of work from several disciplines (e.g., economics, clinical, organizational, and social psychology, sociology), has produced several insights on the nature of UT and multiple measures of the phenomenon (Furnham and Marks, 2013). Yet this research, although much larger and more mature than similar work in the health care domain, has suffered from a similar lack of a unified theory and approach to measurement. UT has been conceptualized and measured in numerous ways. A prime example is the substantial body of research on tolerance and intolerance of ambiguityda closely related phenomenon, which FrenkelBrunswik (1949) originally defined as “a tendency to resort to black white solutions, to arrive at premature closure as to evaluative aspects, often at the neglect of reality, and to seek for unqualified and unambiguous overall acceptance and rejection of other people” (p. 115). This work spawned numerous efforts to conceptualize and measure tolerance of both ambiguity and uncertainty, leading to imprecision and overlap in these efforts (Grenier et al., 2005). The end result has been confusion about the nature of UT and its appropriate assessment. Compounding these problems has been the distributed nature of UT research across multiple disciplines, which has impeded development of a shared, logically coherent conceptual understanding of the phenomenon. Consequently, although past research has produced important insights on UT (Durrheim and Foster, 1997; Furnham and Marks, 2013; Grenier et al., 2005; Rosen et al., 2014), many fundamental questions remain. What is the meaning and nature of the “uncertainty” that constitutes the object of people's tolerance? Exactly what does it mean to “tolerate” uncertainty? What are the essential elements of UT, and to what extent are these elements determined by characteristics of individuals versus

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characteristics of situations? Lack of consensus on these foundational conceptual questions limits our ability to answer several empirical questions of importance for health care: Is UT stable across the health care continuum (e.g., including prevention, screening, treatment, and end-of-life care)? How does the UT of health professionals influence the way they communicate, manage uncertainty, and care for their patients? Can effective strategies be developed to enhance UT among patients and health professionals? Past reviews of the literature on UT and ambiguity tolerance (AT) have been limited, however, to examining the psychometric properties of existing measures (Furnham and Marks, 2013; Furnham and Ribchester, 1995), or comparing UT and AT with existing psychological constructs (Grenier et al., 2005; Rosen et al., 2014). A critical analysis of the meaning of the concept of UT itself has been unaddressed in these efforts (Birrell et al., 2011; Furnham and Marks, 2013; McLain et al., 2015). The overarching objective of the current study was to conduct such an analysis, focusing on the definitions of UT put forth by developers of existing measures that have driven empirical research in the field. Our specific objectives were to: (1) analyze the meaning and logical coherence of UT, and (2) develop an integrative conceptual model to guide future empirical research regarding the nature, causes, and effects of UT. In the current paper we report the findings from this conceptual study. We first identify and review existing measures of UT and AT produced by researchers from diverse disciplines. We analyze measure developers’ explicit definitions of the “uncertainty” and “tolerance” components of UT, contained in their writings about the topic, as well as their implicit definitions manifest indirectly and non-explicitly through the items they created to measure the construct. We then summarize the various existing definitions of both the uncertainty and tolerance components of UT, identifying important conceptual problems and unanswered questions raised by these definitions. Finally, we derive a new integrative conceptual model of UT, and discuss how this model can facilitate further empirical and theoretical research to measure and better understand the phenomenon of uncertainty tolerance in health care and other domains of life. 2. Method 2.1. Measure review and selection Our selection of measures for our analysis was purposefully broad, including measures of both uncertainty and ambiguity tolerance, and encompassing research in health care as well as other disciplines. One author (MH) identified existing measures by searching the research databases PsycINFO, Medline, PubMed, and Business Source Complete using all combinations of the following keywords: ‘Uncertainty/Ambiguity,’ ‘Tolerance/intolerance/aversion/coping/dealing’ and ‘Scale/Measure/Measurement/Questionnaire/Instrument’. Our search yielded 2874 non-duplicate records, which were screened for relevance on title and abstract (Fig. 1). Potentially eligible records were examined in full-text. Additionally, we performed a secondary search using these potentially relevant records and previous psychometric reviews of uncertainty tolerance (UT) and ambiguity tolerance (AT) scales. We included any empirically validated or tested measures without time or language restriction. Questionnaire translations, cross-cultural validations, measures unavailable in the public domain, ad hoc measures (i.e., used only once without any empirical testing or validation), and measures that were not quantitative and paper-based were excluded. Measures of unclear eligibility were assessed by a second author (PH). Although some previous analyses have treated uncertainty and ambiguity as distinct (Grenier et al., 2005), we included both UT

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Fig. 1. Flow diagram for article search and selection.

and AT measures in our analysis for several reasons. First, as other researchers have noted, existing measures of UT and AT overlap substantially (Rosen et al., 2014). Second, conceptual distinctions between uncertainty and ambiguity made by measure developers are debatable (Birrell et al., 2011; Grenier et al., 2005). An example is the distinction, made by some researchers, that AT pertains to uncertainty about the present, whereas UT pertains to uncertainty about the future (Greco and Roger, 2001; Grenier et al., 2005). Yet the logical justification for this distinction is not clear and may simply reflect historical or disciplinary differences in the conceptualization of UT. We also initially included measures of two constructsdneed for closure (NFC) and uncertainty orientation (UO)dwhich are conceptually related to UT and AT. NFC refers to an individual's “desire for a firm answer to a question and an aversion toward ambiguity,” (Kruglanski and Webster, 1996, p. 264) manifest by a tendency to seek structure, to simplify complex information, and avoid ambiguity (Kruglanski, 1990, 1996; Kruglanski et al., 2009; Webster and Kruglanski, 1994). The concept of NFC is part of a more general theory of epistemic motivations underlying information seeking, and its conceptual overlap with UT has been acknowledged by other researchers (Berenbaum et al., 2008; Rosen et al., 2014). Another related construct, Uncertainty Orientation (UO), refers to people's information-processing style and approach to handling uncertainty (Sorrentino and Short, 1986). UO is also part of a more general psychological theory of the motivations underlying responses to uncertainty (Sorrentino et al., 1995, 2009; Sorrentino and Short, 1986), and its conceptual overlap with UT and

AT has also been acknowledged by other researchers (Rosen et al., 2014). Finally, to guide our analysis, we adopted an operational definition of uncertainty as a broader, more overarching construct than ambiguity, based on theoretical work that construes “uncertainty” as a fundamental metacognitive state consisting of the conscious awareness of ignorance (Han et al., 2011; Smithson, 1999). In this view, ambiguity represents not a psychological state but a subordinate phenomenon, a specific feature of information that produces uncertainty. This conceptualization is consistent with the work of the decision theorist Ellsberg, who specifically defined ambiguity as a feature of informationdnamely, its lack of reliability, credibility, or adequacy (Ellsberg, 1961). The broader implication is that “uncertainty” is the more fundamental psychological state constituting the ultimate referent of tolerance, and “uncertainty tolerance” is an all-encompassing phenomenon including tolerance of ambiguity as well as other potential sources of uncertainty, such as probability (the indeterminacy or randomness of future outcomes) and complexity (features of information that make it difficult to understand) (Han et al., 2011). Although we acknowledge that not all measure developers would have shared this operational framework, we believe it provides a useful framework for the current analysis. 2.2. Analysis As a general analytic strategy, we deconstructed UT into two logically independent constituent elementsduncertainty (or

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ambiguity) and (in)tolerancedand analyzed each element separately. We then focused our analysis on evaluating the content and logical coherence of measure developers' 1) explicit definitions of the construct of UT, and 2) implicit definitions of UT contained in individual items of UT measures. For the latter task, we used an empirical, inductive, qualitative analytic approach that, to our knowledge, has not been previously employed for similar purposes. We first listed all individual items comprising existing UT measures. Next, three authors (MH, PH and CG) independently identified the specific source of uncertainty to which each item referred (the ‘uncertainty’ component) and the specific response to uncertainty (the ’tolerance’ component). Afterward, we compared and discussed coding decisions until consensus was reached for all individual items. Next, both sources and responses to uncertainty were categorized and classified within a logical coding framework. Finally, we evaluated the logical coherence and consistency of our classifications and achieved consensus on a final conceptual framework. 3. Results In total, 22 candidate measures of uncertainty and ambiguity tolerance, need for closure, and uncertainty orientation were identified. Four scales were excluded from analysis, because they were not used or validated following initial development (Dalbert, 2010; Martin and Westie, 1959; O'Connor, 1952; Webster et al., 1955). We also excluded the only published measure assessing “uncertainty orientation” because of the complexity and qualitative nature of its administration procedure, which requires coding of “uncertainty imagery” contained in individuals' open-ended responses to story-telling exercises (Brouwers and Sorrentino, 1993; Rosen et al., 2014). The 18 included measures were developed by researchers from behavioral, personality, and decision psychology (n ¼ 12), management (n ¼ 1), and health care (n ¼ 5). Seven scales measured uncertainty tolerance (UT), ten measured ambiguity tolerance (AT) and one measured need for closure. Scales were developed in the US (n ¼ 11), Canada (n ¼ 2), the UK (n ¼ 2), Germany, South Africa and Italy/US (n ¼ 1 each). Measures contained between 6 and 61 items. Table 1 displays all 18 measures, and the corresponding explicit and implicit construct definitions of their developers. We now separately present the results of our analysis of the uncertainty and tolerance components of UT. 3.1. Empirical analysis: the concepts of uncertainty and tolerance 3.1.1. Explicit definitions of uncertainty Measure developers’ definitions of uncertainty varied substantially (Table 1), ranging from the possibility that a negative or potentially harmful event may occur (Dugas et al., 2001; Freeston et al., 1994), to the period of anticipation prior to such an event (Greco and Roger, 2001; Monat et al., 1972), to the “notion that negative events may occur and there is no definitive way of predicting such events” (Carleton et al., 2010, p. 106). These disparate definitions, however, share a common focus on uncertainty arising from the indeterminacy of future outcomes. Gerrity et al. (1990), in contrast, defined uncertainty as situations that are “unfamiliar or not easily resolved” (p. 726). Two UT measures did not include explicit definitions of uncertainty (Comer et al., 2009; Schneider et al., 2010). Explicit definitions of ‘ambiguity’ referenced greater breadth in sources of uncertainty. For example, Budner (1962) defined ambiguity in terms of three fundamental aspects of a stimulus: novelty, complexity, and insolubility. Several other AT measures put forth similar definitions (Geller et al., 1993; MacDonald, 1970). Furnham and Ribchester (1995) defined ambiguity in terms of the

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unfamiliarity, complexity, and incongruity of situations, while McLain (1993) defined ambiguity as “unfamiliar, complex, dynamically uncertain or subject to multiple conflicting interpretations” (p. 184). Norton (1975) identified even more sources of ambiguity: vagueness, incompleteness, fragmentation, probability, lack of structure, lack of information, uncertainty, inconsistency, contradiction, contrariness, and lack of clarity. Other explicit definitions of ambiguity included the simultaneous existence of positive and negative features in the same object (Durrheim and Foster, 1997), “vagueness and uncertainty of meaning” (Hancock et al., 2015, p. 114), and the lack of “reliability, credibility, or adequacy of information about risks and the potential outcomes of decisions” (Han et al., 2009, p. 557)dfollowing the decision theory conceptualization of Ellsberg (1961). Two AT measure developers offered no explicit definition of ambiguity (Herman et al., 2010; Lauriola et al., 2015). 3.1.2. Implicit definitions of uncertainty Table 2 details measure developers' implicit definitions of uncertainty contained in individual measure items. Once again, there was substantial variability in definitions of uncertainty both within and between scales. Most scales assessed multiple sources of uncertainty. For example, the measurement of Ambiguity Tolerance50 (MAT-50) (Norton, 1975) included 13 different sources, ranging from ‘unfamiliarity’ to ‘tentativeness’. A few scales had a narrower focus. For example, the Intolerance of Uncertainty Index (Gosselin et al., 2008) distinguished only ‘unpredictability’ and ‘uncertainty’ (in a general, non-specified sense), and the Attitudinal Ambiguity Tolerance scale (Durrheim and Foster, 1997) and the AA-Med (Han et al., 2009) focused only on ‘inconsistency’ of information. Furthermore, implicit definitions of uncertainty frequently did not correspond with measure developers' explicit definitions. For example, the explicit definition of uncertainty as a perceived ‘potentially harmful/negative future event’ was implicitly operationalized in measure items as encompassing as many as 12 different sources of uncertainty, ranging from ‘incomprehensibility’ to ‘disorder’ (Greco and Roger, 2001). Similarly, the Tolerance of Ambiguity scale (Budner, 1962) explicitly defined only novelty, complexity, and insolubility as sources of ambiguity; however, eight different sources of uncertainty were distinguishable in individual measure items. Lack of a clear distinction between ambiguity and uncertainty was an even greater problem for measure items than for measure developers' explicit definitions. AT and UT measures referenced identicaldor substantially similardsources of uncertainty. For example, items in the Uncertainty Response Scale tapped 12 different sources of uncertainty including ‘unfamiliarity’, ‘incomprehensibility’, and ‘instability’das did several AT scales (Greco and Roger, 2001). Some UT scales even included ‘ambiguity’ as a potential source of ‘uncertainty’ (Buhr and Dugas, 2002; Freeston et al., 1994) although they did not specify the meaning of either term. Finally, some items tapped phenomena that, strictly speaking, represent not sources but consequences of uncertainty (e.g., ‘error’, ‘lack of autonomy’, ‘indecision’). 3.1.3. Explicit definitions of tolerance There was also substantial variability in measure developers' explicit definitions of the ‘tolerance’ component of UT (Table 1). Some definitions focused squarely on the cognitive domain, defining tolerance as a person's way of perceiving (and processing) a given phenomenon (Furnham and Ribchester, 1995; Ladouceur et al., 1998). For example, Budner (1962) defined tolerance as the assessment of uncertainty as desirable versus threatening, while McLain (1993) defined tolerance as viewing uncertainty as attractive, and Buhr and Dugas (2002) focused on a person's belief that

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Table 1 Four exemplary measures of tolerance and intolerance of uncertainty and ambiguity. Scale

1. Tolerance of Ambiguity scale; Budner (1962) 16 items

Sources of uncertaintyy

Responses to uncertaintyy

Explicit definition

Implicit definition

Explicit definition

Implicit definition

Novel, complex and insoluble stimuli

1. Complexity 2. Incompleteness 3. Inconsistency 4. Indefinitiveness 5. Insolubility 6. Nontransparency 7. Unfamiliarity 8. Unpredictability 1. Complexity 2. Error 3. Incompleteness 4. Insolubility 5. Uncertainty (unspecified)

Appraisal as desirable vs. threatening

Cognitive 1. Attraction/Aversion 2. Denial/Acknowledgement Emotional 3. Enjoyment

2. Physicians' Reactions to Situations that are unfamiliar or not easily resolved Uncertainty scale (PRU); Gerrity et al. (1990) 22 items

Emotional reactions and concerns as Cognitive well as behaviors used to cope 1. Aversion 2. Catastrophizing 3. Confusion Emotional 4. Anger 5. Comfort/ Discomfort 6. Stress 7. Worry/ Anxiety A tendency to perceive or interpret Cognitive 3. MAT50; Norton (1975) Information marked by vague, incomplete, fragmented, 1. Ambiguity 1. Attraction/ (unspecified) as actual or potential sources of 61 items multiple, probable, unstructured, uncertain, 2. Complexity psychological discomfort or threat. Aversion inconsistent, contrary, contradictory, or unclear 3. Disorder 2. Denial meanings 4. Emotional Incompleteness 3. Anger 5. Inconsistency 4. Discomfort 6. 5. Enjoyment Indefinitiveness 6. Worry/ 7. Insolubility Anxiety 8. Lack of autonomy 9. Nontransparency 10. Polysemousness 11. Tentativeness 12. Unfamiliarity 13. Unpredictability Intolerance Cognitive The notion that negative events may occur and there is 1. 4. Intolerance of 1. Aversion Incompleteness Uncertainty Scale short no definitive way of predicting such events Emotional 2. Uncertainty form (IUS-12); Carleton 2. Anger (unspecified) et al. (2007) 3. Discomfort 3. 12 items Unpredictability

Behavioral 8. Disclosure/ Nondisclosure Unspecified 9. Tolerance

Behavioral 7. Approach/ Avoidance 8. Dysfunction Unspecified 9. Tolerance

Behavioral 4. Avoidance 5. Paralysis

Note. See online supplement S1 for full table of all 18 measures included in analysis. The term ‘uncertainty’ is used to refer to both ambiguity and uncertainty.

y

uncertainty is unfair. These definitions all construe tolerance as a cognitive appraisal process. Other measure developers, however, defined tolerance more expansively as encompassing additional emotional and behavioral processes, although they did so without further characterizing these processes (Comer et al., 2009; Gerrity et al., 1990; Ladouceur et al., 1998). Some described tolerance generically, in terms of people's ‘response’ to, ‘attitude’ towards, or ‘dealing’ with the phenomena of uncertainty or ambiguity. Furthermore, some measure developers focused on ‘tolerance’ of uncertainty or ambiguity, while others focused on ‘intolerance’.

3.1.4. Implicit definitions of tolerance Implicit definitions of tolerance, revealed by existing UT and AT measures, also demonstrated wide variation (Table 3). Individual measures referenced between one (Durrheim and Foster, 1997) and 18 (Greco and Roger, 2001) different psychological responses. In some cases, measure items simply used terms like ‘tolerating’ or ‘managing’, without further specification. All other referenced responses can be categorized along two conceptual dimensions: type and valence. Three main types of responses can be distinguished (Table 3). Cognitive responses, assessed by all 18 measures, included acknowledging vs. denying uncertainty, or being attracted vs. repelled by it. Emotional responses, assessed by all but one scale,

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Table 2 Multiple sources of uncertainty defined implicitly in measure items. Source of uncertainty

Definition

Inconsistency

divergence, non-conformity, dissimilarity, incoherence, contradictoriness, incommensurability

Exemplary measure items

The sooner we all acquire similar values and ideals the better (Budner, 1962; Herman et al., 2010) Sometimes I rather enjoy going against the rules and doing things I'm not supposed to do (MacDonald, 1970) Variety diversity, multiplicity, manifoldness, plenitude I can be comfortable with nearly all kinds of people (Herman et al., 2010) Variation between individual patients is a frustrating aspect of medicine (Hancock et al., 2015) Unpredictability indeterminacy or randomness of future outcomes There is something exciting about being kept in suspense (Greco and Roger, 2001) I do not like to get started in group projects unless I feel assured that the project will be successful (Norton, 1975) Insolubility resistance to being worked out or explained I like the mystery that there are some things in medicine we'll never know (Hancock et al., 2015) A problem has little attraction for me if I don't think it has a solution (MacDonald, 1970) Polysemousness susceptibility to multiple meanings or interpretations; Generally, the more meanings a poem has, the better I like it (Lauriola et al., 2015; Norton, 1975) equivocacy I'm drawn to situations which can be interpreted in more than one way (Lauriola et al., 2015; McLain, 1993) Incomprehensibility resistance to being understood or known If I don't get the punch line of a joke, I don't feel right until I understand it (Lauriola et al., 2015; Norton, 1975) When I can't clearly discern situations, I get apprehensive (Greco and Roger, 2001) Indefinitiveness resistance to having a single, precise, or invariant A person either knows the answer to a question or he doesn't (Lauriola et al., 2015) answer; irresoluteness; inconclusiveness I have a lot of respect for consultants who always come up with a definite answer (Hancock et al., 2015) Tentativeness resistance to final, unchanging, definitive answers; I am dissatisfied when the specialist does not make a diagnosis (Schneider et al., 2010) unsettledness; undecidedness The best part of working a jigsaw puzzle is putting in that last piece (Geller et al., 1993; MacDonald, 1970; Norton, 1975) Complexity intricateness, multi-dimensionality, multi-facetedness It is more fun to tackle a complicated problem than to solve a simple one (Budner, 1962; Lauriola et al., 2015) I would rather avoid solving a problem that must be viewed from several different perspectives (McLain, 1993) Unfamiliarity unacquaintedness, strangeness, novelty, I often find myself looking for something new, rather than trying to hold things constant in my life unrecognizability (Lauriola et al., 2015; McLain, 1993) I like to fool around with new ideas, even if they turn out later to be a total waste of time (MacDonald, 1970) Disorder lack of regularity, structure, organization, or balance Usually, the more clearly defined rules a society has, the better off it is (Norton, 1975) I like things to be ordered and in place, both at work and at home (Greco and Roger, 2001) Incompleteness insufficiency, inadequacy of information I usually like to know what time it is (Norton, 1975) If I am unsure about a diagnosis I apply time, if possible, until the reason of illness becomes clearer (Schneider et al., 2010) Non-transparency inexplicitness, obscurity, hiddenness of information I would be comfortable if a clinical teacher set me a vague assignment or task (Hancock et al., 2015) A good job is one where what is to be done and how it is to be done are always clear (Budner, 1962; Hancock et al., 2015; Herman et al., 2010; Lauriola et al., 2015) Impermanence instability, evanescence, mutability, changeability; I find the prospect of change exciting and stimulating (Greco and Roger, 2001) temporariness I feel anxious when things are changing (Greco and Roger, 2001)

included discomfort, anxiety, stress, or depression to excitement, pleasure, and relief. No further pattern emerged in the type or variety of emotions included in the various scales, and the same emotional responses were ascertained by both UT and AT measures. For example, seven out of ten AT measures and five out of seven UT measures referenced anxiety (Table 1). Behavioral responses were assessed by 14 of the 18 scales. In general, AT measures were less behaviorally focused than UT measures; four of the ten AT measures did not include any behavioral response. Behavioral responses varied in their goal-orientation: some were aimed at preventing uncertainty's adverse effectsdfor example, by avoiding uncertain situationsdwhereas others were aimed at mitigating uncertainty's adverse effects after one was confronted with it (e.g., decision paralysis). The valence of different responses to uncertainty represented the second major conceptual dimension of tolerance (Table 3). Most implicit definitions of tolerance were negativedthat is, aimed at preventing, avoiding, or mitigating uncertainty's adverse effects. However, a few measures ascertained positively valenced responses that were aimed at deriving benefit from uncertainty. The width of the valence dimension also varied depending on the measure. For some measures, the highest level of tolerance was the absence of a negative evaluation or the acceptance of existing uncertainty; in other words, the width ranged from negative to

neutral. Other measures, however, implicitly defined UT more broadly and included positive evaluations and responses (e.g., ‘enjoyment’, ‘approach’), which implicitly defined tolerance as encompassing a broader spectrum of responses ranging from negative to positive. 3.2. Conceptual analysis The foregoing empirical analysis has demonstrated that the concept of uncertainty tolerance (UT), as put forth by scholars who have attempted to measure it from multiple disciplinary perspectives, is rich and varied. However, it lacks conceptual coherence and specification. Consensus is lacking regarding the meaning of both the uncertainty and tolerance components of UT. Researchers use both the same terms to signify different things, and different terms to signify the same thing. At this time, several key questions of conceptual breadth remain unanswered. 3.2.1. How broad is the phenomenon of uncertainty? The first question concerns the breadth of the uncertainty that constitutes the object of UT. Existing measures affirm that this uncertainty is not a monolithic phenomenon, and shed light on its important types and sources (Table 2)dsome of which, such as disorder and impermanence, have received conspicuously little

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Table 3 Multiple responses to uncertainty identified implicitly in measure items. Positive

Exemplary measure item

Negative

Exemplary measure item

COGNITIVE Acknowledgement People who insist upon a yes or no answer just don't Denial know how complicated things really are (Budner, 1962) Attraction

I prefer a situation in which there is some ambiguity (McLain, 1993)

There's a right way and a wrong way to almost everything (Lauriola et al., 2015; MacDonald, 1970) The sooner we all acquire similar values and ideas, the better (Budner, 1962; Herman et al., 2010) I would not have confidence in a medical test or treatment if experts had conflicting opinions about it (Han et al., 2009)

Aversion

Distrust

Curiosity (wonder, The idea of taking a trip to a new country fascinates me fascination) (Greco and Roger, 2001) Self-disvaluation (self-doubting) Confusion

Doubt

Catastrophizing

Comfort (wellbeing, relaxation) Enjoyment (pleasure) Excitement (passion, stimulation)

Discomfort (unease, emotional upset, feeling It really disturbs me when I am unable to follow overwhelmed) another person's train of thought (Geller et al., 1993; Norton, 1975) It is more fun to tackle a complicated problem than to solve a simple one (Budner, 1962; Lauriola et al., 2015) I think a mid-life career change is an exciting idea (Greco and Roger, 2001) Stress

Worry/Anxiety (nervousness, sense of vulnerability or threat) Fear Depression (sadness) Anger (irritation, frustration, annoyance) Approach (confrontation, pursuit, seeking) Function Adaptation Deliberation

Disclosure (tendency to disclose)

Being uncertain means that I am not first rate (Buhr and Dugas, 2002; Freeston et al., 1994) When trying to solve a problem I often see so many possible options that it’s confusing (Webster and Kruglanski, 1994) When I find myself in an uncertain situation, I tend to have doubts about what I am doing (Gosselin et al., 2008) I often exaggerate the odds that the worst will happen when something unexpected occurs (Gosselin et al., 2008)

Uncertainty makes me uneasy, anxious and distressed (Buhr and Dugas, 2002; Freeston et al., 1994) Problems which cannot be considered from just one point of view are a little threatening (McLain, 1993) I fear being held accountable for the limits of my knowledge (Gerrity et al., 1990) Thinking about uncertainty makes me feel depressed (Greco and Roger, 2001) I find it frustrating when I can't find the answer to a clinical question (Hancock et al., 2015)

Avoidance (of the source of uncertainty) I often find myself looking for something new, rather than trying to hold things constant in my life (Lauriola et al., 2015; McLain, 1993)

I avoid settings where people don't share my values (Herman et al., 2010)

I think that I would learn best in a class that lacks clearly Dysfunction (debilitation) stated objectives and requirements. I easily adapt to unfamiliarity (Greco and Roger, 2001) When I feel uncertain about something, I try to rationally weigh up all the information I have (Greco and Roger, 2001) Paralysis (decision avoidance, hesitance, deferral, or cautiousness) I always share my uncertainty with my patients (Gerrity Non-disclosure (reluctance to acknowledge) et al., 1990)

When I am uncertain, I can't function very well (Buhr and Dugas, 2002; Freeston et al., 1994)

When it's time to act, uncertainty paralyzes me (Carleton et al., 2010; Freeston et al., 1994) I almost never tell other physicians about diagnoses I have missed (Gerrity et al., 1990)

Resignation (non-perseverance)

I tend to give up easily when I don't clearly understand a situation (Greco and Roger, 2001) Control-orientation (consequence or source I prefer to control everything in order to decrease uncertainties (Gosselin et al., 2008) mitigation, problem-solving, informationseeking, support-seeking)

attention in the specific context of health care. The remaining question, however, is how expansive the concept of uncertainty ought to be. Studies that focus only on particular types of uncertainties seem necessarily incomplete; yet, it is not clear that all conceivable uncertainties that might exist in a particular situation

can or should be measured. Furthermore, different sources of uncertainty might provoke different responses or levels of tolerancedjustifying efforts to systematically distinguish and measure them (Hamilton et al., 2013).

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3.2.2. How broad is the phenomenon of tolerance? A second important question pertains to the conceptual breadth of the phenomenon of tolerance. This breadth applies first to UT's valence (i.e., whether uncertainty should be construed as fundamentally positive, negative, or both). On the one hand, one can construe uncertainty as a fundamentally negative phenomenon, and tolerance as a response aimed at avoiding or mitigating its harms. In its most literal sense captured by dictionary definitions, to ‘tolerate’ means ‘to allow (something that is bad, unpleasant, etc.) to exist, happen or be done’, or ‘to experience (something harmful or unpleasant) without being harmed’ (Merriam Webster Online). In this conception, the most one might achieve is to avoid uncertainty's negative effectsdthat is, the width of the UT spectrum ranges from negative to neutral at best. Most UT measures adopt this negative perspective on uncertainty and UT (Table 3). On the other hand, one can construe uncertainty as a beneficial state to be desired and soughtdand tolerance as a response aimed at obtaining benefit from uncertainty. In this conception, its phenomenological spectrum is inherently broader, extending from negative to positive. The responses encompassed by UT also raise questions of conceptual breadth. The first question concerns the spatial breadth of UT; UT potentially spans many different types of responsesdcognitive, emotional, and behavioral (Table 3)dand exactly which of these responses are essential to the phenomenon is unclear. The second question concerns the temporal breadth of UT; among the various potential responses to uncertainty, exactly which responses should be regarded as constituting the phenomenon of UT, as opposed to merely resulting from it, is also unclear. Both the spatial and temporal breadth of the phenomenological spectrum of psychological responses that constitute UT are debatable. On the one hand, one might construe UT as encompassing the full set of one's possible psychological responses, beginning with the initial perception and appraisal of uncertainty during the process of information exchange (cognitive response), to a final health care decision (behavioral response). On the other hand, one might restrict the boundaries of tolerance to the patient's initial perception and appraisal of uncertainty, and exclude health decisions as more distal downstream manifestations of UT. Exactly where the boundaries of UT begin and enddi.e., which of these processes constitute “tolerance” per se, as opposed to sequelae of tolerancedis conceptually arbitrary. 3.2.3. Should UT be viewed as a trait or state? A related unresolved question pertains to whether or not UT represents a stable personality trait that predisposes individuals to specific psychological responses. Both the UT and AT literature have treated UT as a stable trait and some evidence suggests this is the case (Koerner and Dugas, 2008). AT has been consistently related to other personality traits such as authoritarianism, dogmatism, and openness to experience (Feather, 1967; Kirton, 1981; Kohn, 1974; MacDonald, 1970; Rigby and Rump, 1982; Shaffer and Hendrick, 1974). In line with this ‘trait’-level view of UT, measures of the construct have generally been domain-general (i.e., excluding situation-specific manifestations of tolerance). Alternatively, one can view UT as a state determined by situational or contextual factors that need to be measured and accounted for (Durrheim and Foster, 1997; Herman et al., 2010). In line with this view, domainspecific measures for AT and UT have been developed to assess, for example, how well people acknowledge their own ambiguous attitudes towards authority figures (Durrheim and Foster, 1997), or respond to medical uncertainty (Gerrity et al., 1990) (Table 1). More work, using both domain-specific and domain-general UT measures, is needed to ascertain how differences in both individual personalities and situational factors interact in producing varying levels of UT.

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3.2.4. Relation to associated theories Existing theoretical models of UT and related concepts, originating from different disciplines, place the concept of UT in a broader context and provide useful building blocks for a unifying perspective. In particular, several models focus on the cognitive dimension of uncertainty and the information-processing aspect of uncertainty tolerance. Examples include the lay epistemic theory of Kruglanski (1990); Kruglanski et al. (2009), which construes the need for closure (NFC)dthe desire for any firm belief on a given topic, as opposed to uncertaintydas a fundamental motivation for human behavior. According to this theory, NFC is determined by both individual differences and situational factors, and produces a cognitive-motivational orientation toward the social world that is either open or closed. NFC in the face of uncertainty influences people's information-seeking behavior, causing people to ‘seize’ on information that yields closure and to ‘freeze’ on closure once attained. In a similar fashion, the uncertainty orientation (UO) theory of Sorrentino (Sorrentino et al., 1995) focuses on the cognitive dimension of uncertainty and conceives the management of uncertainty as a fundamental behavioral motivation. UO represents a self-regulatory style pertaining to how individuals approach and manage uncertainty; uncertainty-oriented individuals seek and engage with new information while certainty-oriented individuals avoid it. Individual differences in UO are posited to interact with situational characteristics, affective motivations, and other factors to determine how people process information. Finally, in the health communication literature the ‘theory of problematic integration,’ advanced by Babrow, addresses how people perceive, evaluate, and respond to information that poses uncertainty regarding either the probability or value of an object of concern (Babrow, 1992; Babrow et al., 1998). According to this theory, the integration of a person's beliefs about the probability of a given object of concern and its value to the person is a fundamental motivation that drives information seeking and exchange. This theory, like the lay epistemic theory of Kruglanski and the uncertainty orientation theory of Sorrentino, focuses on the cognitive and informational dimensions of uncertainty and uncertainty tolerance. Other theoretical models, in contrast, emphasize the emotional dimension of uncertainty and focus more broadly on not only informational but non-informational responses aimed at coping with uncertainty. Much of the work on measuring intolerance of uncertainty beginning with Freeston et al. (1994) originated from efforts to understand the determinants of worry and the pathogenesis of anxiety disorders. This work construes uncertainty intolerance as a “basic dysfunctional cognitive schema [… that] contributes to the development and maintenance of worry through both direct and indirect effects” (Freeston et al., 1994, p. 799). The influential transactional model of stress and coping, proposed by Lazarus and Folkman, conceives individuals' responses to stressful environmental stimuli (e.g., uncertainty) as consisting of primary and secondary appraisal processes in which the individual assesses the significance and controllability of the stressor (Lazarus and Folkman, 1984). Depending on these appraisals, individuals then engage in coping strategies that are either problem-focused (aimed at changing or resolving the stressful situation) or emotion-focused (aimed at changing the way one thinks or feels, controlling one's emotional response to the situation) (Lazarus, 1993, 1998). This emotional coping paradigm provides a useful framework for conceptualizing UT, and has been employed by Krohne and other theorists in this manner (Krohne, 1989; Krohne and Hock, 2011; Lazarus and Folkman, 1984). Related theories have focused on ‘sense-making’dpeople's efforts to explain novel, unexpected, and emotional eventsdas a means of emotional adaptation (Wilson et al., 2003). The underlying premise is that sense-making

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reduces the emotional power of novel, unexpected, and attentiondemanding events by turning them into ordinary ones that are “no longer focal in people's thoughts and no longer trigger intense reactions” (Wilson et al., 2005, p. 6). Still other theoretical models emphasize the behavioral dimension of uncertainty and uncertainty tolerance. A large body of behavioral decision research has focused on rational decision making under conditions of uncertaintydwhere rationality is defined in terms of both the logical coherence of judgments and decisions, and their correspondence with objective reality (Bell et al., 1988; Hammond, 1996). Theoretical accounts from this research tradition have primarily been concerned with describing the manifold judgmental biases, cognitive illusions, and heuristics that cause deviations from rational decision making, and with developing prescriptive strategiesdsuch as decision analysisdto minimize these deviations. From this theoretical perspective, uncertainty tolerance represents the capacity to “close the gap between the normative ideal and the descriptive reality” of decision making under uncertainty (Fischhoff, 1988, p.724). Integrative theoretical models from health care research include the influential work of Mishel, who has drawn on the transactional model of stress and coping of Lazarus and Folkman to develop her ‘uncertainty in illness’ theory. This theory posits that patients appraise the uncertainty arising from their experience of illness as representing either danger or opportunity, and that these appraisals lead to either emotion-focused or problem-focused coping strategies, respectively (Mishel, 1988; Mishel and Sorenson, 1991). In the health communication literature, Brashers' ‘uncertainty management theory’ also builds on the transactional model of stress and coping in positing that people respond to uncertainty first by forming appraisals that reflect their emotional response to it. When people experience anxiety or negative emotions they appraise uncertainty as a threat, and when they experience positive emotions they appraise it as an opportunity (Brashers, 2001). These appraisals, in turn, lead individuals to either avoid or seek information as a means of managing their uncertainty. These and other theoretical accounts represent interpretive lenses that focus on different fundamental psychological processes and outcomes; however, they are not mutually exclusive. Interpretive frameworks that alternatively construe UT in terms of epistemic motivations vs. emotional coping strategies vs. rational judgment and decision making, for example, shed light on different aspects of a broader essential phenomenon. Each perspective privileges a particular dimension of psychological realitydcognitive vs. emotional vs. behavioraldand regards uncertainty and its tolerance as subservient to more fundamental psychological needs and motivations (epistemic, emotional, existential), or to the ultimate achievement of different goals (e.g., cognitive closure, emotional well-being, maximization of subjective expected utility in decision making). Tolerance of uncertainty encompasses all of these psychological dimensions, needs, motivations, and goals; however, there is currently no overarching, synthetic theory that integrates them all. 4. Discussion 4.1. Rethinking uncertainty tolerance: an integrative, flexible, multidimensional model The main finding of our analysis is that existing efforts to understand the phenomenon of uncertainty tolerance lack consistency, specificity, and breadth. No single conceptual definition or empirical measure captures the full spectrum of uncertainty

tolerance articulated through the collective efforts of the field. As an initial step towards addressing this problem, we now build upon past efforts to propose an integrative, flexible, multi-dimensional model of UT that can help guide future empirical and theoretical research on UT in both health care and other domains of life. In doing so, however, we emphasize several important caveats. By necessity, any overarching model that is flexible enough to be utilized across disciplinesdand to simultaneously accommodate different theories, concerns, and perspectivesdmust sacrifice some desired conceptual depth for breadth, and may thus be less useful for some investigators. All theories privilege particular concerns, aspects of reality, and explanatory frameworks specific to the disciplines from which they originate. What is important to a cognitive psychologist, a sociologist, a philosopher, and a physician, for example, may differ in fundamental ways. We thus present our integrative model not as a “grand unifying theory” that completely explains uncertainty tolerance, but as a foundation for a more synthetic program of theoretical and empirical research to better understand the phenomenon. 4.2. A working definition of uncertainty tolerance We first propose an integrative working definition of UT: The set of negative and positive psychological responsesdcognitive, emotional, and behavioraldprovoked by the conscious awareness of ignorance about particular aspects of the world. This expansive, multi-dimensional definition integrates insights from past work on tolerance and intolerance of both ambiguity and uncertainty (Birrell et al., 2011; Freeston et al., 1994; Ladouceur et al., 1997). Fig. 2 visual represents this definition and provides a conceptual model that is theoretically neutral, provisional, and flexible, enabling UT to be construed in different ways depending on one's research questions and conceptual assumptions. Accordingly, our integrative model does not prescribe phenomenological boundaries, but rather provides an organizational framework that helps make explicit the underlying assumptions, goals, and tradeoffs of different approaches. Our new integrative model of UT builds upon and advances previous conceptual analyses in several important ways (Durrheim and Foster, 1997; Furnham and Marks, 2013; Grenier et al., 2005; Rosen et al., 2014). It provides a comprehensive model encompassing both the ‘uncertainty’ and ‘tolerance’ components of UT. At the same time, it defines these constituent phenomena precisely and explicitly. It coherently distinguishes between uncertainty and ambiguity and accommodates the breadth of responses to these phenomenadboth positive (adaptive) as well as negative (maladaptive). It acknowledges the potential role of contextual (moderating) factors. It is flexible with respect to the conceptual breadth of UT, enabling researchers to delimit the phenomenon according to their specific interests and perspectives. 4.3. The concept of uncertainty Our integrative model of UT begins from a definition of uncertainty as the conscious, metacognitive awareness of ignorance (Smithson, 1999). Uncertainty is thus distinct from ambiguity. Uncertainty is the overarching, superordinate construct, the seminal metacognition pertaining to some object and produced by some source. In contrast, ambiguity is the subordinate construct, one of uncertainty's principal sources, a property of information pertaining to its lack of reliability, credibility, or adequacy (Curley et al., 1986; Ellsberg, 1961; Han et al., 2011; Keren and Gerritsen, 1999). Aside from ambiguity, the other principal sources of uncertainty are

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Fig. 2. Integrative model of uncertainty tolerance. Adapted from Han et al. (2011).

probability and complexity (Fig. 3). Probability refers to the fundamental indeterminacy or randomness of future events, and gives rise to what has been termed ‘aleatory’ or ‘first-order’ uncertainty (Han et al., 2011). Complexity refers to features of a phenomenon that make it difficult to comprehendde.g., multiplicity in its component characteristics, causal determinants, or effects. Probability, ambiguity, and complexity each produce uncertainty once individuals perceive themdthat is, when they become consciously aware of them as sources of ignorancedthrough processes that manifest UT. Several items comprising existing UT measures ascertain more fundamental sources of ambiguity and complexity. For example, several constructs assessed by individual measure itemsde.g., inconsistency, polysemousness, incompleteness, variety, indefinitiveness, tentativeness, disorder, and impermanencedcan be construed as more fundamental sources of ambiguity, given that they ultimately diminish the reliability, credibility, or adequacy of information. Similarly, other constructsde.g., ‘incomprehensibility’ and ‘lack of transparency’eecan be construed as more fundamental sources of

complexity (Han et al., 2011). 4.4. The concept of tolerance Our integrative conceptual model depicts UT as a multi-faceted phenomenon, encompassing a variety of not only uncertainties, but also responses to these uncertainties (Fig. 2). These responses are initiated by a stimulus, which must first be perceived by an individual as unknown (in order for uncertainty to exist). UT may operate at this step through fundamental attentional biases influencing the likelihood that an uncertain situation will be perceived as such. Following this perception of uncertainty are cognitive, emotional, and behavioral responses, which may co-occur temporally (Dugas et al., 2001; Ladouceur et al., 1998). Cognitive responses include a variety of appraisals, ranging from negative to positive. Emotional responses include a wide variety of states including discomfort, anxiety, anger and excitement. Behavioral responses include information seeking and decision avoidance. These behavioral responses can be further categorized temporally as

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Fig. 3. Sources of uncertainty.

either source- or consequence-focused. Source-focused responses aim at avoiding or altering the uncertain situation. Consequencefocused responses aim at mitigating or palliating the consequences of an uncertain situation. Importantly, any of these various responses can be either conscious and intentional or unconscious and reflexive. Although uncertainty is by definition always consciousdinsofar as it represents a metacognitive awareness of ignorancedin their consequent responses to uncertainty people may have variable awareness of the extent to which their uncertainty is causing them to think, feel, or behave in particular ways. As Fig. 2 depicts, our conceptual model is flexible, encompassing UT responses that range in valence from negative to positivedi.e., focused on both the avoidance or mitigation of harm from uncertainty, but the derivation of benefit from it. Research interests will dictate how narrowly or broadly UT is defined along this spectrum. If withstanding the adverse effects of uncertainty is of interest, then UT will be defined in its most narrow, literal sense as the capacity to avoid these effects. Or, if deriving benefit from uncertainty is of interest, then UT will be defined more expansively, encompassing outcomes beyond mere avoidance. Importantly, our conceptual model of UT also enables the phenomenon to be approached as either a personality trait or a state contingent on context and situation (Frenkel-Brunswik, 1949; Jonassen and Grabowski, 1993). A trait-focused approach enables research to characterize and understand individual differences in UT, and to explore tailoring interventions (e.g., decision support in health care) according to these differences. A state-focused approach enables research to better understand contextual factors that influence UT and to develop interventions to influence these factors. Finally, our model is flexible in its spatial and temporal breadth. It accommodates cognitive, emotional, and behavioral responses occurring at any point in the sequence of events, both downstream and upstream (McLain et al., 2015). The fuzzy borders of Fig. 2 emphasize that exactly where one draws the conceptual boundaries around UT depends on one's conceptual assumptions and goals. Researchers who wish to elucidate the causal pathways that determine the broadest variety of responses to the broadest variety of uncertainties will want to draw the conceptual boundaries of UT expansively. Disciplinary concerns, however, may incline researchers to focus their attention on specific parts of the model. For example, psychologists may focus on the upstream perceptual processes that determine when and how uncertainty arises as a

conscious state, while health services researchers may focus on downstream outcomes such as decision making and the utilization of medical interventions in the face of scientific uncertainty. Exactly where ‘uncertainty tolerance’ conceptually begins and ends is ultimately driven by practical and methodological concerns. Irrespective of these concerns, however, our integrative conceptual model can help researchers reach a shared, coherent, holistic perspective on the phenomenon of uncertainty tolerance, and place their own work in the context of a larger program of research. 4.5. Limitations and future research directions This study had several limitations that point to future research needs. First, we did not conduct a systematic review of all studies that have utilized UT measures, given that our primary focus was to analyze the concept of UT. It is thus possible that relevant UT measures were excluded; however, we believe our search was sufficiently thorough to justify confidence that major existing UT measures were included in our analysis. Second, given the conceptual focus of the current study, we did not systematically review the large and growing body of empirical evidence on the relationship between UT and both health and healthcare-related outcomes. However, we are currently undertaking such a review in another study that will be reported separately. Above all, we believe that any integrative conceptual model of UT must be necessarily tentative. Our modeldlike all scientific constructsdis based on past theory and empirical evidence that is incomplete, provisional, and subject to change. It remains for further research to demonstrate the value of our model, and its ability to inform future research on UT. Nevertheless, we believe our new integrative model fosters needed conceptual precision in how researchers define and measure uncertainty tolerance, and highlights critical knowledge gaps that need to be addressed. Our deconstruction of both the specific sources of uncertainty, and the essential processes that comprise tolerance, raises several questions for further research within the health care domain and beyond. 4.5.1. Empirical research across disciplines Our integrative model can facilitate broad, domain-general empirical research to elucidate the determinants, mechanisms, and outcomes of UT. It provides a framework for refining UT measures to assess the full temporal and spatial spectrum of the

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phenomenon as it occurs in different contexts. Given that no existing UT measure samples this full spectrum, measure refinement and testing is a much-needed next step for the field. Our integrative model can further facilitate such endeavors by providing an organizing framework to help measure developers identify the range of responses their measures should ascertain. Several overarching questions about UT remain. Which sources of uncertainty are most prevalent and relevant, and to what extent do different sources and types of uncertainty provoke different psychological responses? What factors influence the extent to which uncertainty becomes appraised in a negative vs. positive manner, or engenders negative vs. positive psychological effects? What factors moderate and mediate these effects and ultimately determine people's capacity to tolerate uncertainty? By laying out a provisional temporal sequence of processesdbeginning with the perception and appraisal of uncertainty and ending with various downstream cognitive, emotional, and behavioral responsesdour integrative model suggests testable hypotheses about specific causal pathways of UT. For example, future research could elucidate the factors that moderate the link between the perception of uncertainty and negative vs. positive appraisals, or the pathway from negative appraisals of uncertainty to negative emotional or behavioral responses (see Fig. 2). Potential moderators may include characteristics of the uncertainty stimulus, personality traits, characteristics of the clinician-patient interaction, and others. Individual differences in literacy, numeracy, dispositional optimism, and other personality traits, for example, may moderate the extent to which people perceive uncertainty as well as their behavioral responses to it. Our conceptual model may someday enable not only the generation of testable hypotheses about these pathways, but the development of new interventions to improve people's tolerance of uncertainty (Tyreman, 2015). 4.5.2. Empirical research in the health care domain Our integrative model can also facilitate focused empirical research on UT in the domain of health care. Currently, five health care-specific measures of UT existdall of which were designed to assess either physicians' or medical students' UT, and vary in conceptual width and psychometric properties. Their reliability and validity for assessing UT in different clinical contexts and for different stakeholders (patients, physicians, other clinicians, caregivers) is an important question for future research. More work is needed to determine how best to assess both patients' and clinicians’ tolerance of the uncertainties that pertain to specific clinical tasks (e.g., diagnosis, prognosis, treatment) and types of interventions (e.g., genomic and non-genomic interventions). The complexity of health care may demand that measures of UT be problem- and person-specific. For example, the relationship between UT and psychological coping among advanced cancer patients might best be assessed by measures that specifically focus on the unpredictability of future cancer recurrence as the primary uncertainty source, and emotions and behaviors such as anxiety, fear, hope, social engagement, and decision making. The optimal approach to the measurement of UT in health care settings, however, remains to be determined. Within the health care context, more research is also needed to elucidate the many factors that influence UT among patients and clinicians (Fig. 2). Potential factors include characteristics of the uncertainty stimulus (e.g., the source or type of uncertainty); characteristics of individuals (e.g., genetic variants, physiologic states, personality traits, abilities, motivations); situational factors (e.g., aspects of clinical encounters including time, patient-clinician communication, and informational, emotional, and relational support); cultural factors (e.g., values, norms, religious beliefs); and

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social factors (e.g., community and institutional resources, structures, and processes). Our integrative conceptual model could facilitate such work by helping researchers generate causal hypotheses and organize studies to test these hypotheses. More work is also needed to elucidate outcomes of UT among patients and clinicians, including patient experiences with care (e.g., extent and nature of patient-clinician communication, extent of patient engagement and informed and shared decision making) as well as technical health outcomes (e.g., morbidity, mortality, health-related quality of life) and the quality of care (e.g., underutilization, overutilization, appropriate utilization of health services). UT may influence any of these outcomes, and in diverse ways for different disease conditions or health care endeavors (e.g., prevention, treatment, palliative and end-of-life care). Another important focus for further health care research is to identify modifiable factors that can enhance UT. For example, the degree to which clinicians engage in shared decision making may determine the extent to which patients are aware of various sources of uncertainty. The way in which the clinician engages in the shared decision making process may modify patients’ ability to deal with that uncertainty. Our integrative conceptual model can help researchers identify and assess how these and other factors influence UT. Research to develop and evaluate interventions to improve uncertainty tolerance in health care is a final, critically important need given the increasing exposure of both health providers and patients to uncertain situations and information (Logan and Scott, 1996). Initial efforts in this direction suggest that patients’ UT can be increased by providing personal feedback, resulting in reduced levels of anxiety (Dugas and Ladouceur, 2000). These and other efforts, however, need to be replicated and evaluated in other practices and settings. 4.5.3. Theory building We believe our integrative model can ultimately facilitate transdisciplinary theory building on the nature and mechanisms of UT. Past research on UT has been either atheoretical or focused narrowly on particular dimensions of UT that reflect the primary interests of particular disciplines. Tolerance of uncertainty encompasses all of these psychological dimensions, needs, motivations, and goals. This is both the challenge and opportunity posed by the problem of UT: it is so ubiquitous and fundamental, cross-cutting virtually all life domains and experiences, that it affords the opportunity fordand indeed necessitatesda synthetic theoretical perspective. We believe our integrative model of UT can facilitate such a perspective by helping researchers identify, define, and organize the temporal and spatial components that a comprehensive theory needs to address. The construct of UT provides a unique scientific opportunity; it reveals the commonalities of many different bodies of theoretical and empirical research, and serves as a conceptual focal point that can unify disparate theories. 5. Conclusion Although uncertainty tolerance is critical in health care and other domains of life, we have demonstrated that its meaning has remained unclear and its measurement has varied substantially. We currently possess several insights on the nature of UT and its antecedents and sequelae, and yet the basic question remains: What is the essence, or fundamental nature of uncertainty tolerance? We have shown that the answer depends on the eye of the beholder. Conceptualizations of the phenomenon have varied depending on how both ‘uncertainty’ and ‘tolerance’ have been construed, and these construals have reflected the concerns and

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disciplinary perspectives of different theorists and measure developers. In our conceptual analysis, we have tried to minimize reducible ambiguity and confusion arising from inadequate precision and logical incoherencies in different conceptualizations of UT. What we are left with, however, are definitional ambiguities that are less reducibledhinging upon unresolved questions about the breadth and nature of UT. We propose a tentative, integrative, flexible conceptual model of UT, to guide further inquiry. We believe this model has great potential value for future empirical, theoretical, and applied empirical research on UT, both in and outside of health care. Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.socscimed.2017.03.024. References Babrow, A.S., 1992. Communication and problematic integration: understanding diverging probability and value, ambiguity, ambivalence, and impossibility. Commun. Theory 2, 95e130. Babrow, A.S., Kasch, C.R., Ford, L.A., 1998. The many meanings of uncertainty in illness: toward a systematic accounting. Health Commun. 10, 1e23. Bell, D.E., Raiffa, H., Tversky, A., 1988. Descriptive, normative, and prescriptive interactions in decision making. Decis. Mak. Descr. Norm. Prescriptive Interact. 1, 9e32. Berenbaum, H., Bredemeier, K., Thompson, R.J., 2008. Intolerance of uncertainty: exploring its dimensionality and associations with need for cognitive closure, psychopathology, and personality. J. Anxiety Disord. 22, 117e125. Birrell, J., Meares, K., Wilkinson, A., Freeston, M., 2011. Toward a definition of intolerance of uncertainty: a review of factor analytical studies of the Intolerance of Uncertainty Scale. Clin. Psychol. Rev. 31, 1198e1208. Brashers, D.E., 2001. Communication and uncertainty management. J. Commun. 51, 477e497. Brouwers, M.C., Sorrentino, R.M., 1993. Uncertainty orientation and protection motivation theory: the role of individual differences in health compliance. J. Personal. Soc. Psychol. 65, 102. Budner, S., 1962. Intolerance of ambiguity as a personality variable. J. Pers. 30, 29e50. Buhr, K., Dugas, M.J., 2002. The intolerance of uncertainty scale: psychometric properties of the english version. Behav. Res. Ther. 40, 931e945. Carleton, R.N., Gosselin, P., Asmundson, G.J., 2010. The intolerance of uncertainty index: replication and extension with an English sample. Psychol. Assess. 22, 396e406. Carleton, R.N., Norton, M.A., Asmundson, G.J., 2007. Fearing the unknown: a short version of the intolerance of uncertainty scale. J. Anxiety Disord. 21, 105e117. Comer, J.S., Roy, A.K., Furr, J.M., Gotimer, K., Beidas, R.S., Dugas, M.J., et al., 2009. The intolerance of uncertainty scale for children: a psychometric evaluation. Psychol. Assess. 21, 402e411. Curley, S.P., Yates, J.F., Abrams, R.A., 1986. Psychological sources of ambiguity avoidance. Organ. Behav. Hum. Decis. Process. 38, 230e256. Dalbert, C., 2010. The Uncertainty Tolerance Scale (UTS). Dugas, M.J., Gosselin, P., Ladouceur, R., 2001. Intolerance of uncertainty and worry: investigating specificity in a nonclinical sample. Cogn. Ther. Res. 25, 551e558. Dugas, M.J., Ladouceur, R., 2000. Treatment of GAD. Targeting intolerance of uncertainty in two types of worry. Behav. Modif. 24, 635e657. Durrheim, K., Foster, D., 1997. Tolerance of ambiguity as a content specific construct. Person. Indiv. Diff. 22, 741e750. Ellsberg, D., 1961. Risk, ambiguity, and the Savage axioms. Q. J. Econ. 643e669. Feather, N., 1967. An expectancy-value model of information-seeking behavior. Psychol. Rev. 74, 342. Fischhoff, B., 1988. Judgment and decision making. In: Sternberg, R.J., Smith, E.E. (Eds.), The Psychology of Human Thought. Cambridge University Press, New York (NY), United States, pp. 153e187. aume, J., Letarte, H., Dugas, M.J., Ladouceur, R., 1994. Why do Freeston, M.H., Rhe people worry? Personal. Individ. Differ. 17, 791e802. Frenkel-Brunswik, E., 1949. Intolerance of ambiguity as an emotional and perceptual personality variable. J. Personal. 18, 108e143. Furnham, A., Marks, J., 2013. Tolerance of ambiguity: a review of the recent literature. Psychology 4, 717e728. Furnham, A., Ribchester, T., 1995. Tolerance of ambiguity: a review of the concept, its measurement and applications. Curr. Psychol. 14, 179e199. Geller, G., Faden, R.R., Levine, D.M., 1990. Tolerance for ambiguity among medical students: implications for their selection, training and practice. Soc. Sci. Med. 31, 619e624. Geller, G., Tambor, E.S., Chase, G.A., Holtzman, N.A., 1993. Measuring physicians' tolerance for ambiguity and its relationship to their reported practices regarding genetic testing. Med. Care 31, 989e1001.

Gerrity, M.S., DeVellis, R.F., Earp, J.A., 1990. Physicians' reactions to uncertainty in patient care. A new measure and new insights. Med. Care 28, 724e736. Gosselin, P., Ladouceur, R., Evers, A., Laverdiere, A., Routhier, S., Tremblay-Picard, M., 2008. Evaluation of intolerance of uncertainty: development and validation of a new self-report measure. J. Anxiety Disord. 22, 1427e1439. Greco, V., Roger, D., 2001. Coping with uncertainty: the construction and validation of a new measure. Pers. Indiv. Differ. 31, 519e534. Grenier, S., Barrette, A., Ladouceur, R., 2005. Intolerance of uncertainty and intolerance of ambiguity: similarities and differences. Pers. Indiv. Differ. 39, 593e600. Hamilton, J.G., Hutson, S.P., Moser, R.P., Kobrin, S.C., Frohnmayer, A.E., Alter, B.P., et al., 2013. Sources of uncertainty and their association with medical decision making: exploring mechanisms in Fanconi anemia. Ann. Behav. Med. 46, 204e216. Hammond, K.R., 1996. Human Judgment and Social Policy: Irreducible Uncertainty, Inevitable Error, Unavoidable Injustice. Oxford University Press, New York. Han, P.K., Klein, W.M., Arora, N.K., 2011. Varieties of uncertainty in health care: a conceptual taxonomy. Med. Decis. Mak. 31, 828e838. Han, P.K.J., Reeve, B.B., Moser, R.P., Klein, W.M.P., 2009. Aversion to ambiguity regarding medical tests and treatments: measurement, prevalence, and relationship to sociodemographic factors. J. Health Commun. 14, 556e572. Hancock, J., Roberts, M., Monrouxe, L., Mattick, K., 2015. Medical student and junior doctors' tolerance of ambiguity: development of a new scale. Adv. Health Sci. Educ. Theory Pract. 20, 113e130. Herman, J.L., Stevens, M.J., Bird, A., Mendenhall, M., Oddou, G., 2010. The Tolerance for Ambiguity Scale: towards a more refined measure for international management research. Int. J. Intercult. Relat. 34, 58e65. Jonassen, D., Grabowski, B., 1993. Individual Differences and Instruction. Allen & Bacon, New York. Keren, G., Gerritsen, L.E., 1999. On the robustness and possible accounts of ambiguity aversion. Acta Psychol. 103, 149e172. Kirton, M.J., 1981. A reanalysis of two scales of tolerance of ambiguity. J. Personal. Assess. 45, 407e414. Koerner, N., Dugas, M.J., 2008. An investigation of appraisals in individuals vulnerable to excessive worry: the role of intolerance of uncertainty. Cogn. Ther. Res. 32, 619e638. Kohn, P.M., 1974. Authoritarianism, rebelliousness, and their correlates among British undergraduates. Br. J. Soc. Clin. Psychol. 13, 245e255. Krohne, H.W., 1989. The concept of coping modes: relating cognitive person variables to actual coping behavior. Adv. Behav. Res. Ther. 11, 235e248. Krohne, H.W., Hock, M., 2011. Anxiety, coping strategies, and the processing of threatening information: investigations with cognitive-experimental paradigms. Personality Individ. Differ. 50, 916e925. Kruglanski, A.W., 1990. Lay epistemic theory in social-cognitive psychology. Psychol. Inq. 1, 181e197. Kruglanski, A.W., 1996. A Motivated Gatekeeper of Our Minds: Need-for-closure Effects on Interpersonal and Group Processes. Kruglanski, A.W., Webster, D.M., 1996. Motivated closing of the mind: “seizing” and “freezing”. Psychol. Rev. 103, 263e283. Kruglanski, A.W., Dechesne, M., Orehek, E., Pierro, A., 2009. Three decades of lay epistemics: the why, how, and who of knowledge formation. Eur. Rev. Soc. Psychol. 20, 146e191. Kuhn, G., Goldberg, R., Compton, S., 2009. Tolerance for uncertainty, burnout, and satisfaction with the career of emergency medicine. Ann. Emerg. Med. 54, 106e113 e106. Ladouceur, R., Blais, F., Freeston, M.H., Dugas, M.J., 1998. Problem solving and problem orientation in generalized anxiety disorder. J. Anxiety Disord. 12, 139e152. Ladouceur, R., Talbot, F., Dugas, M.J., 1997. Behavioral expressions of intolerance of uncertainty in worry. Experimental findings. Behav. Modif. 21, 355e371. Lauriola, M., Foschi, R., Mosca, O., Weller, J., March-29-2015. Attitude toward ambiguity: empirically robust factors in self-report personality scales. Assessment 23 (3), 353e373. http://dx.doi.org/10.1177/1073191115577188. Lazarus, R., 1998. From psychological stress to the emotions: a history of changing outlooks. Personal. Crit. Concepts Psychol. 4, 179. Lazarus, R.S., 1993. Coping theory and research: past, present, and future. Psychosom. Med. 55, 234e247. Lazarus, R.S., Folkman, S., 1984. Stress, Appraisal, and Coping. Springer publishing company. Logan, R.L., Scott, P.J., 1996. Uncertainty in clinical practice: implications for quality and costs of health care. Lancet 347, 595e598. MacDonald, A.P., 1970. Revised scale for ambiguity tolerance: reliability and validity. Psychol. Rep. 26, 791e798. Martin, J.G., Westie, F.R., 1959. The tolerant personality. Am. Sociol. Rev. 521e528. McLain, D.L., 1993. The MSTAT-I: a new measure of an individual's tolerance for ambiguity. Educ. Psychol. Meas. 53, 183e189. McLain, D.L., Kefallonitis, E., Armani, K., 2015. Ambiguity tolerance in organizations: definitional clarification and perspectives on future research. Front. Psychol. 6, 344. Merriam Webster Online http://www.merriam-webster.com/dictionary/tolerate. Mishel, M.H., 1988. Uncertainty in illness. Image J. Nurs. Sch. 20, 225e232. Mishel, M.H., Sorenson, D.S., 1991. Uncertainty in gynecological cancer: a test of the mediating functions of mastery and coping. Nurs. Res. 40, 167e171. Monat, A., Averill, J.R., Lazarus, R.S., 1972. Anticipatory stress and coping reactions under various conditions of uncertainty. J. Personality Soc. Psychol. 24, 237.

M.A. Hillen et al. / Social Science & Medicine 180 (2017) 62e75 Norton, R.W., 1975. Measurement of ambiguity tolerance. J. Pers. Assess. 39, 607e619. O'Connor, P., 1952. Ethnocentrism, “intolerance of ambiguity,” and abstract reasoning ability. J. Abnorm. Soc. Psychol. 47, 526e530. Rigby, K., Rump, E., 1982. Attitudes toward authority and authoritarian personality characteristics. J. Soc. Psychol. 116, 61e72. Rosen, N.O., Ivanova, E., Knauper, B., 2014. Differentiating intolerance of uncertainty from three related but distinct constructs. Anxiety Stress Coping 27, 55e73. Schneider, A., Lowe, B., Barie, S., Joos, S., Engeser, P., Szecsenyi, J., 2010. How do primary care doctors deal with uncertainty in making diagnostic decisions? The development of the 'Dealing with Uncertainty Questionnaire' (DUQ). J. Eval. Clin. Pract. 16, 431e437. Schor, R., Pilpel, D., Benbassat, J., 2000. Tolerance of uncertainty of medical students and practicing physicians. Med. Care 38, 272e280. Shaffer, D.R., Hendrick, C., 1974. Dogmatism and tolerance for ambiguity as determinants of differential reactions to cognitive inconsistency. J. Personal. Soc. Psychol. 29, 601. Smithson, M., 1999. Conflict aversion: preference for ambiguity vs conflict in sources and evidence. Organ Behav. Hum. Decis. Process 79, 179e198.

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Sorrentino, R.M., Holmes, J.G., Hanna, S.E., Scharp, A., 1995. Uncertainty orientation and trust in close relationships: individual differences in cognitive styles. Personal. Process. Individ. Differ. 68, 314e327. Sorrentino, R.M., & Short, J. (1986). Uncertainty orientation, motivation, and cognition. Handbook of motivation and cognition: Foundations of social behavior, 1, 379e403. Sorrentino, R.M., Ye, Y., Szeto, A.C., 2009. Uncertainty management: to fear of not to fear? Psychol. Inq. 20, 240e244. Tyreman, S., 2015. Trust and truth: uncertainty in health care practice. J. Eval. Clin. Pract. 21, 470e478. Webster, D.M., Kruglanski, A.W., 1994. Individual differences in need for cognitive closure. J. Pers. Soc. Psychol. 67, 1049e1062. Webster, H., Sanford, N., Freedman, M., 1955. A new instrument for studying authori-tarianism in personality. J. Psychol. 40, 73e84. Wilson, T.D., Centerbar, D.B., Kermer, D.A., Gilbert, D.T., 2005. The pleasures of uncertainty: prolonging positive moods in ways people do not anticipate. J. Personal. Soc. Psychol. 88, 5. Wilson, T.D., Gilbert, D.T., Centerbar, D.B., 2003. Making sense: the causes of emotional evanescence. Psychol. Econ. Decis. 1, 209e233.