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OBJECTIVE: To make informed health decisions, patients must understand and recall risks, which often involve ratios with large denominators. Grasping the meaning of such numbers may be difficult, because of limited exposure to large groups of people in either our evolutionary history or daily life. METHODS: In an experiment (n=98), we investigated whether medical risks are easier to understand and recall if their representation is based on small, evolutionarily plausible groups of people, and whether this representation especially helps patients with low numeracy. RESULTS: Participants-especially those with low numeracy-often disregarded and incorrectly recalled denominators of ratios representing medical risks when the denominators involved were large. Risks were easier to understand and recall if their representation was based on smaller, evolutionarily plausible groups of people. CONCLUSIONS: Our results extend previous literature on the role of numeracy in understanding health-relevant risk communications by showing the importance of using plausible group sizes to communicate these risks to people with low numeracy. Our results also support the notion that problems in risk perception occur because of inappropriate presentation formats rather than cognitive biases. PRACTICE IMPLICATIONS: Our findings suggest suitable ways to communicate quantitative medical data-especially to people with low numeracy. [ABSTRACT FROM AUTHOR]

Many people have difficulties grasping numerical concepts that are prerequisites for understanding treatment risk reduction. Visual aids have been proposed as a promising method for enhancing comprehension. In a survey of probabilistic, nationally representative samples in two different countries (United States and Germany), we compared the effectiveness of adding different types of visual aids (icon arrays and bar graphs representing either affected individuals only or the entire population at risk) to the numerical information in either an absolute or a relative risk reduction format. We also analyzed whether people's numeracy and graphical literacy skills affected the efficacy of the visual aids. Our results showed large improvements in accuracy both when icon arrays and when bar graphs were added to numerical information. Highest increases were achieved when the visual aids depicted the entire population at risk. Importantly, visual aids were most useful for the participants who had low numeracy but relatively high graphical literacy skills. Building on previous research showing that problems with understanding numerical information often do not reside in people's minds, but in the representation of the problem, our results show that visual aids help to modify incorrect expectations about treatment risk reduction. Our results have important implications for medical practice. [ABSTRACT FROM AUTHOR]

Belief dynamics has an important role in shaping our responses to natural and societal phenomena, ranging from climate change and pandemics to immigration and conflicts. Researchers often base their models of belief dynamics on analogies to other systems and processes, such as epidemics or ferromagnetism. Similar to other analogies, analogies for belief dynamics can help scientists notice and study properties of belief systems that they would not have noticed otherwise (conceptual mileage). However, forgetting the origins of an analogy may lead to some less appropriate inferences about belief dynamics (conceptual baggage). Here, we review various analogies for modeling belief dynamics, discuss their mileage and baggage, and offer recommendations for using analogies in model development., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

We present a theory of belief dynamics that explains the interplay between internal beliefs in people's minds and beliefs of others in their external social environments. The networks of belief theory goes beyond existing theories of belief dynamics in three ways. First, it provides an explicit connection between belief networks in individual minds and belief dynamics on social networks. The connection, absent from most previous theories, is established through people's social beliefs or perceived beliefs of others. Second, the theory recognizes that the correspondence between social beliefs and others' actual beliefs can be imperfect, because social beliefs are affected by personal beliefs as well as by the actual beliefs of others. Past theories of belief dynamics on social networks do not distinguish between perceived and actual beliefs of others. Third, the theory explains diverse belief dynamics phenomena parsimoniously through the differences in attention and the resulting felt dissonances in personal, social, and external parts of belief networks. We implement our theoretical assumptions in a computational model within a statistical physics framework and derive model predictions. We find support for our theoretical assumptions and model predictions in two large survey studies ( N ₁ = 973, N ₂ = 669). We then derive insights about diverse phenomena related to belief dynamics, including group consensus and polarization, group radicalization, minority influence, and different empirically observed belief distributions. We discuss how the theory goes beyond different existing models of belief dynamics and outline promising directions for future research. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

We comment on the limits of relying on prior literature when constructing the design space for an integrative experiment; the adaptive nature of social and behavioral phenomena and the implications for the use of theory and modeling when constructing the design space; and on the challenges of measuring random errors and lab-related biases in measurement without replication.

Women remain underrepresented among faculty in nearly all academic fields. Using a census of 245,270 tenure-track and tenured professors at United States-based PhD-granting departments, we show that women leave academia overall at higher rates than men at every career age, in large part because of strongly gendered attrition at lower-prestige institutions, in non-STEM fields, and among tenured faculty. A large-scale survey of the same faculty indicates that the reasons faculty leave are gendered, even for institutions, fields, and career ages in which retention rates are not. Women are more likely than men to feel pushed from their jobs and less likely to feel pulled toward better opportunities, and women leave or consider leaving because of workplace climate more often than work-life balance. These results quantify the systemic nature of gendered faculty retention; contextualize its relationship with career age, institutional prestige, and field; and highlight the importance of understanding the gendered reasons for attrition rather than focusing on rates alone.

We develop a conceptual framework for studying collective adaptation in complex socio-cognitive systems, driven by dynamic interactions of social integration strategies, social environments and problem structures. Going beyond searching for 'intelligent' collectives, we integrate research from different disciplines and outline modelling approaches that can be used to begin answering questions such as why collectives sometimes fail to reach seemingly obvious solutions, how they change their strategies and network structures in response to different problems and how we can anticipate and perhaps change future harmful societal trajectories. We discuss the importance of considering path dependence, lack of optimization and collective myopia to understand the sometimes counterintuitive outcomes of collective adaptation. We call for a transdisciplinary, quantitative and societally useful social science that can help us to understand our rapidly changing and ever more complex societies, avoid collective disasters and reach the full potential of our ability to organize in adaptive collectives.

Collectives adapt their network structure to the challenges they face. It has been hypothesized that collectives experiencing a real or imagined threat from an outgroup tend to consolidate behind a few group members, and that network structures in which a few members attract most of the attention are more likely in politically extreme groups. These hypotheses have not been tested in large-scale real-world settings. We reconstruct networks of tens of thousands of commenters participating in comment sections of high-profile U.S. political news websites spanning the political spectrum from left to right, including Mother Jones, The Atlantic, The Hill, and Breitbart. We investigate the relationship between different indices of inequality of attention in commenters' networks and perceived group threat associated with significant societal events, from elections and political rallies to mass shootings. Our findings support the hypotheses that groups facing a real or imagined outgroup threat and groups that are more politically extreme are more likely to attend to a few high-profile members. These results provide an extensive real-world test of theoretical accounts of collective adaptation to outgroup threats., (© 2023. The Author(s).)

Cognitive science has been traditionally organized around the individual as the basic unit of cognition. Despite developments in areas such as communication, human-machine interaction, group behavior, and community organization, the individual-centric approach heavily dominates both cognitive research and its application. A promising direction for cognitive science is the study of augmented intelligence, or the way social and technological systems interact with and extend individual cognition. The cognitive science of augmented intelligence holds promise in helping society tackle major real-world challenges that can only be discovered and solved by teams made of individuals and machines with complementary skills who can productively collaborate with each other., (© 2022 Cognitive Science Society LLC.)

Despite the special role of tenure-track faculty in society, training future researchers and producing scholarship that drives scientific and technological innovation, the sociodemographic characteristics of the professoriate have never been representative of the general population. Here we systematically investigate the indicators of faculty childhood socioeconomic status and consider how they may limit efforts to diversify the professoriate. Combining national-level data on education, income and university rankings with a 2017-2020 survey of 7,204 US-based tenure-track faculty across eight disciplines in STEM, social science and the humanities, we show that faculty are up to 25 times more likely to have a parent with a Ph.D. Moreover, this rate nearly doubles at prestigious universities and is stable across the past 50 years. Our results suggest that the professoriate is, and has remained, accessible disproportionately to the socioeconomically privileged, which is likely to deeply shape their scholarship and their reproduction., (© 2022. The Author(s).)

SignificanceMuch of online conversation today consists of signaling one's political identity. Although many signals are obvious to everyone, others are covert, recognizable to one's ingroup while obscured from the outgroup. This type of covert identity signaling is critical for collaborations in a diverse society, but measuring covert signals has been difficult, slowing down theoretical development. We develop a method to detect covert and overt signals in tweets posted before the 2020 US presidential election and use a behavioral experiment to test predictions of a mathematical theory of covert signaling. Our results show that covert political signaling is more common when the perceived audience is politically diverse and open doors to a better understanding of communication in politically polarized societies.

Individual differences in demographics, personality, and other related beliefs are associated with coronavirus disease 2019 (COVID-19) threat beliefs. However, the relative contributions of these different types of individual differences to COVID-19 threat beliefs are not known. In this study, a total of 1,700 participants in Croatia (68% female; age 18-86 years) completed a survey that included questions about COVID-19 risks, questions about related beliefs including vaccination beliefs, trust in the health system, trust in scientists, and trust in the political system, the HEXACO 60 personality inventory, as well as demographic questions about gender, age, chronic diseases, and region. We used hierarchical regression analyses to examine the proportion of variance explained by demographics, personality, and other related beliefs. All three types of individual differences explained a part of the variance of COVID-19 threat beliefs, with related beliefs explaining the largest part. Personality facets explained a slightly larger amount of variance than personality factors. These results have implications for communication about COVID-19., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Butkovic and Galesic.)

A key question concerning collective decisions is whether a social system can settle on the best available option when some members learn from others instead of evaluating the options on their own. This question is challenging to study, and previous research has reached mixed conclusions, because collective decision outcomes depend on the insufficiently understood complex system of cognitive strategies, task properties, and social influence processes. This study integrates these complex interactions together in one general yet partially analytically tractable mathematical framework using a dynamical system model. In particular, it investigates how the interplay of the proportion of social learners, the relative merit of options, and the type of conformity response affect collective decision outcomes in a binary choice. The model predicts that, when the proportion of social learners exceeds a critical threshold, a bistable state appears in which the majority can end up favoring either the higher- or lower-merit option, depending on fluctuations and initial conditions. Below this threshold, the high-merit option is chosen by the majority. The critical threshold is determined by the conformity response function and the relative merits of the two options. The study helps reconcile disagreements about the effect of social learners on collective performance and proposes a mathematical framework that can be readily adapted to extensions investigating a wider variety of dynamics., Competing Interests: The authors declare no competing interest., (Copyright © 2021 the Author(s). Published by PNAS.)

Collective behavior provides a framework for understanding how the actions and properties of groups emerge from the way individuals generate and share information. In humans, information flows were initially shaped by natural selection yet are increasingly structured by emerging communication technologies. Our larger, more complex social networks now transfer high-fidelity information over vast distances at low cost. The digital age and the rise of social media have accelerated changes to our social systems, with poorly understood functional consequences. This gap in our knowledge represents a principal challenge to scientific progress, democracy, and actions to address global crises. We argue that the study of collective behavior must rise to a "crisis discipline" just as medicine, conservation, and climate science have, with a focus on providing actionable insight to policymakers and regulators for the stewardship of social systems., Competing Interests: The authors declare no competing interest., (Copyright © 2021 the Author(s). Published by PNAS.)

The ability to 'sense' the social environment and thereby to understand the thoughts and actions of others allows humans to fit into their social worlds, communicate and cooperate, and learn from others' experiences. Here we argue that, through the lens of computational social science, this ability can be used to advance research into human sociality. When strategically selected to represent a specific population of interest, human social sensors can help to describe and predict societal trends. In addition, their reports of how they experience their social worlds can help to build models of social dynamics that are constrained by the empirical reality of human social systems.

Belief change and spread have been studied in many disciplines-from psychology, sociology, economics and philosophy, to biology, computer science and statistical physics-but we still do not have a firm grasp on why some beliefs change more easily and spread faster than others. To fully capture the complex social-cognitive system that gives rise to belief dynamics, we first review insights about structural components and processes of belief dynamics studied within different disciplines. We then outline a unifying quantitative framework that enables theoretical and empirical comparisons of different belief dynamic models. This framework uses a statistical physics formalism, grounded in cognitive and social theory, as well as empirical observations. We show how this framework can be used to integrate extant knowledge and develop a more comprehensive understanding of belief dynamics.

Across academia, men and women tend to publish at unequal rates. Existing explanations include the potentially unequal impact of parenthood on scholarship, but a lack of appropriate data has prevented its clear assessment. Here, we quantify the impact of parenthood on scholarship using an extensive survey of the timing of parenthood events, longitudinal publication data, and perceptions of research expectations among 3064 tenure-track faculty at 450 Ph.D.-granting computer science, history, and business departments across the United States and Canada, along with data on institution-specific parental leave policies. Parenthood explains most of the gender productivity gap by lowering the average short-term productivity of mothers, even as parents tend to be slightly more productive on average than nonparents. However, the size of productivity penalty for mothers appears to have shrunk over time. Women report that paid parental leave and adequate childcare are important factors in their recruitment and retention. These results have broad implications for efforts to improve the inclusiveness of scholarship., (Copyright © 2021 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).)

Purpose. "False consensus" refers to individuals with (v. without) an experience judging that experience as more (v. less) prevalent in the population. We examined the role of people's perceptions of their social circles (family, friends, and acquaintances) in shaping their population estimates, false consensus patterns, and vaccination intentions. Methods. In a national online flu survey, 351 participants indicated their personal vaccination and flu experiences, assessed the percentage of individuals with those experiences in their social circles and the population, and reported their vaccination intentions. Results. Participants' population estimates of vaccination coverage and flu prevalence were associated with their perceptions of their social circles' experiences, independent of their own experiences. Participants reporting less social circle "homophily" (or fewer social contacts sharing their experience) showed less false consensus and even "false uniqueness." Vaccination intentions were greater among nonvaccinators reporting greater social circle vaccine coverage. Discussion. Social circle perceptions play a role in population estimates and, among individuals who do not vaccinate, vaccination intentions. We discuss implications for the literature on false consensus, false uniqueness, and social norms interventions.

Objective: Flu vaccinations are recommended for almost everyone, but uptake may vary because of perceived social norms. We aimed to examine the relationship between perceived social circle vaccine coverage (including family, friends, and acquaintances) and own vaccination behavior as well as potential mediators., Methods: In 2011, 357 participants from RAND's American Life Panel reported perceived social circle vaccine coverage for the 2010-2011 flu season, own vaccination behavior for the 2009-2010 and 2010-2011 flu seasons, perceived flu risk without and with vaccination, and perceived vaccine safety. In 2012 and 2016, respectively, participants returned to report their own vaccination behavior for the 2011-2012 flu season (N = 338) and 2015-2016 flu season (N = 216)., Results: Perceiving greater percentage of 2010-2011 social circle vaccine coverage was associated with a greater likelihood of getting vaccinated in the 2010-2011 flu season (odds ratio [OR] = 1.03, 95% confidence interval [CI] = 1.01, 1.04) and the subsequent 2011-2012 flu season (OR = 1.02, 95% CI = 1.01, 1.03) but not the 2015-2016 flu season (OR = 1.00, 95% CI = .99, 1.01), as seen in logistic regressions that controlled for demographics and 2009-2010 vaccination behavior. All significant relationships between social circle vaccine coverage and own vaccination behavior were mediated by perceived flu risk without vaccination., Conclusions: Perceived social circle vaccine coverage is associated with own vaccination behavior in the current and subsequent flu season, establishing behavior patterns that may persist into the future. People's vaccination decisions may be informed by their perceptions of their peers' beliefs and behaviors. We discuss intervention strategies for promoting vaccine uptake by counteracting negative and promoting positive perceived social norms. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

People's perceptions about the size of minority groups in social networks can be biased, often showing systematic over- or underestimation. These social perception biases are often attributed to biased cognitive or motivational processes. Here we show that both over- and underestimation of the size of a minority group can emerge solely from structural properties of social networks. Using a generative network model, we show that these biases depend on the level of homophily, its asymmetric nature and on the size of the minority group. Our model predictions correspond well with empirical data from a cross-cultural survey and with numerical calculations from six real-world networks. We also identify circumstances under which individuals can reduce their biases by relying on perceptions of their neighbours. This work advances our understanding of the impact of network structure on social perception biases and offers a quantitative approach for addressing related issues in society.

Background: Visual displays can facilitate risk communication and promote better health choices. Their effectiveness in improving risk comprehension is influenced by graph literacy. However, the construct of graph literacy is still insufficiently understood, partially because existing objective measures of graph literacy are either too difficult or too long., Objectives: We constructed a new 4-item Short Graph Literacy (SGL) scale and examined how SGL scores relate to key cognitive, affective, and conative precursors of health behavior change described in common health behavior theories., Methods: We performed secondary analyses to adapt the SGL scale from an existing 13-item scale. The initial construction was based on data collected in a laboratory setting in Germany ( n = 51). The scale was then validated using data from nationally representative samples in Germany ( n = 495) and the United States ( n = 492). To examine how SGL scores relate to precursors of health behavior change, we performed secondary analyses of a third study involving a nationwide US sample with 47% participants belonging to racial/ethnic minorities and 46% with limited formal education ( n = 835)., Results: Graph literacy was significantly associated with cognitive precursors in theoretically expected ways (e.g., positive associations with risk comprehension and response efficacy and a negative association with cognitive risk perception). Patterns for affective precursors generally mirrored those for cognitive precursors, although numeracy was a stronger predictor than graph literacy for some affective factors (e.g., feelings of risk). Graph literacy had predictive value for most cognitive and affective precursors beyond numeracy. In addition, graph literacy (but not numeracy) predicted key conative precursors such as defensive processing., Conclusions: Our data suggest that the SGL scale is a fast and psychometrically valid method for measuring objective graph literacy. Our findings also highlight the theoretical and practical relevance of graph literacy.

Studies of social judgments have demonstrated a number of diverse phenomena that were so far difficult to explain within a single theoretical framework. Prominent examples are false consensus and false uniqueness, as well as self-enhancement and self-depreciation. Here we show that these seemingly complex phenomena can be a product of an interplay between basic cognitive processes and the structure of social and task environments. We propose and test a new process model of social judgment, the social sampling model (SSM), which provides a parsimonious quantitative account of different types of social judgments. In the SSM, judgments about characteristics of broader social environments are based on sampling of social instances from memory, where instances receive activation if they belong to a target reference class and have a particular characteristic. These sampling processes interact with the properties of social and task environments, including homophily, shapes of frequency distributions, and question formats. For example, in line with the model's predictions we found that whether false consensus or false uniqueness will occur depends on the level of homophily in people's social circles and on the way questions are asked. The model also explains some previously unaccounted-for patterns of self-enhancement and self-depreciation. People seem to be well informed about many characteristics of their immediate social circles, which in turn influence how they evaluate broader social environments and their position within them. (PsycINFO Database Record, ((c) 2018 APA, all rights reserved).)

Patients and doctors often need to make decisions based on the results of medical tests. When these results are presented in the form of conditional probabilities, even doctors find it difficult to interpret them correctly. There is over 20 y of research supporting the finding that people are better able to calculate the correct positive predictive value of a test when given information in natural frequencies, as opposed to conditional probabilities. Natural frequencies are one of a few psychological tools that have made it into evidence-based medicine. Recently, Pighin and others (Med Decis Making 2016;36:686-91) argued that natural frequencies could hinder informed decision making, a critique based on a single task and a crude scoring criterion we refer to as the 50%-Split. Our commentary addresses these criticisms based on three analyses. First, we show how the 50%-Split scoring used by Pighin and others misclassifies known errors, such as solely attending to the hit rate (true-positive rate) of the test, as strategies that support understanding. Second, we reanalyze data from 21 additional problems completed by various participant groups to show that their scoring criterion does not support their results in 19 out of 21 cases. Third, we apply the mean deviation scoring method and show that, when given information in natural frequency formats, participants provide estimates that are closer to the correct Bayesian solution than for conditional probability formats. In each analysis, natural frequencies lead to more correct judgements and therefore promote informed decision making relative to conditional probabilities. We welcome further discussions of performance metrics that can provide insight into how the public and therefore patients understand the implications of medical test results.

Objective: A shared decision-making approach is suggested for multiple sclerosis (MS) patients. To properly evaluate benefits and risks of different treatment options accordingly, MS patients require sufficient numeracy - the ability to understand quantitative information. It is unknown whether MS affects numeracy. Therefore, we investigated whether patients' numeracy was impaired compared to a probabilistic national sample., Methods: As part of the larger prospective, observational, multicenter study PERCEPT, we assessed numeracy for a clinical study sample of German MS patients (N=725) with a standard test and compared them to a German probabilistic sample (N=1001), controlling for age, sex, and education. Within patients, we assessed whether disease variables (disease duration, disability, annual relapse rate, cognitive impairment) predicted numeracy beyond these demographics., Results: MS patients showed a comparable level of numeracy as the probabilistic national sample (68.9% vs. 68.5% correct answers, P=0.831). In both samples, numeracy was higher for men and the highly educated. Disease variables did not predict numeracy beyond demographics within patients, and predictability was generally low., Conclusion: This sample of MS patients understood quantitative information on the same level as the general population., Practice Implications: There is no reason to withhold quantitative information from MS patients., (Copyright © 2017 Elsevier B.V. All rights reserved.)

The constructs of attitudes, emotions, and sentiments are often only verbally defined and therefore somewhat vague. The sentiment construct might be fruitfully modeled as a result of sampling processes, complementing the Attitude-Scenario-Emotion model in explaining similarities and differences in sentiments across different cultures.

The structure of communication networks is an important determinant of the capacity of teams, organizations and societies to solve policy, business and science problems. Yet, previous studies reached contradictory results about the relationship between network structure and performance, finding support for the superiority of both well-connected efficient and poorly connected inefficient network structures. Here we argue that understanding how communication networks affect group performance requires taking into consideration the social learning strategies of individual team members. We show that efficient networks outperform inefficient networks when individuals rely on conformity by copying the most frequent solution among their contacts. However, inefficient networks are superior when individuals follow the best member by copying the group member with the highest payoff. In addition, groups relying on conformity based on a small sample of others excel at complex tasks, while groups following the best member achieve greatest performance for simple tasks. Our findings reconcile contradictory results in the literature and have broad implications for the study of social learning across disciplines.

Decisions in the environmental and in particular the climate domain are burdened with uncertainty. Here, we focus on uncertainties faced by individuals when making decisions about environmental behavior, and we use the statistical sampling framework to develop a classification of different sources of uncertainty they encounter. We then map these sources to different public policy strategies aiming to help individuals cope with uncertainty when making environmental decisions., (Copyright © 2015 Cognitive Science Society, Inc.)

Background: In the past decade, the number of lawsuits for medical malpractice has risen significantly. This could affect the way doctors make decisions for their patients., Objective: To investigate whether and why doctors practice defensive medicine with their patients., Design: A questionnaire study was conducted in general practice departments of eight metropolitan hospitals in Spain, between January and February 2010., Setting and Participants: Eighty general practitioners (48% men; mean age 52 years) with an average of 15.3 years of experience and their 80 adult patients (42% men; mean age 56 years) participated in the study., Main Outcome Measurements: Participants completed a self-administered questionnaire involving choices between a risky and a conservative treatment. One group of doctors made decisions for their patients. Another group of doctors predicted what their patients would decide for themselves. Finally, all doctors and patients made decisions for themselves and described the factors they thought influenced their decisions., Results: Doctors selected much more conservative medical treatments for their patients than for themselves. Most notably, they did so even when they accurately predicted that the patients would select riskier treatments. When asked about the reasons for their decisions, most doctors (93%) reported fear of legal consequences., Discussion and Conclusions: Doctors' decisions for their patients are strongly influenced by concerns of possible legal consequences. Patients therefore cannot blindly follow their doctor's advice. Our study, however, suggests a plausible method that patients could use to get around this problem: They could simply ask their doctor what he or she would do in the patient's situation., (© 2012 John Wiley & Sons Ltd.)

Few empirical data exist on how decision making about health differs from that in other crucial life domains with less threatening consequences. To shed light on this issue we conducted a study with 175 young adults (average age 19 years). We presented the participants with scenarios involving advisors who provided assistance in making decisions about health, money, and career. For each scenario, participants were asked to what extent they wanted the advisor to exhibit several leadership styles and competencies and what role (active, collaborative, or passive) they preferred to play when making decisions. Results show that decision making about health is distinct from that in the other domains in three ways. First, most of the participants preferred to delegate decision making about their health to their physician, whereas they were willing to collaborate or play an active role in decision making about their career or money. Second, the competencies and leadership style preferred for the physician differed substantially from those desired for advisors in the other two domains: Participants expected physicians to show more transformational leadership--the style that is most effective in a wide range of environments--than those who provide advice about financial investments or career. Finally, participants' willingness to share medical decision making with their physician was tied to how strongly they preferred that the physician shows an effective leadership style. In contrast, motivation to participate in decision making in the other domains was not related to preferences regarding advisors' leadership style or competencies. Our results have implications for medical practice as they suggest that physicians are expected to have superior leadership skills compared to those who provide assistance in other important areas of life.

Background: Making evidence-based decisions often requires comparison of two or more options. Research-based evidence may exist which quantifies how likely the outcomes are for each option. Understanding these numeric estimates improves patients' risk perception and leads to better informed decision making. This paper summarises current "best practices" in communication of evidence-based numeric outcomes for developers of patient decision aids (PtDAs) and other health communication tools., Method: An expert consensus group of fourteen researchers from North America, Europe, and Australasia identified eleven main issues in risk communication. Two experts for each issue wrote a "state of the art" summary of best evidence, drawing on the PtDA, health, psychological, and broader scientific literature. In addition, commonly used terms were defined and a set of guiding principles and key messages derived from the results., Results: The eleven key components of risk communication were: 1) Presenting the chance an event will occur; 2) Presenting changes in numeric outcomes; 3) Outcome estimates for test and screening decisions; 4) Numeric estimates in context and with evaluative labels; 5) Conveying uncertainty; 6) Visual formats; 7) Tailoring estimates; 8) Formats for understanding outcomes over time; 9) Narrative methods for conveying the chance of an event; 10) Important skills for understanding numerical estimates; and 11) Interactive web-based formats. Guiding principles from the evidence summaries advise that risk communication formats should reflect the task required of the user, should always define a relevant reference class (i.e., denominator) over time, should aim to use a consistent format throughout documents, should avoid "1 in x" formats and variable denominators, consider the magnitude of numbers used and the possibility of format bias, and should take into account the numeracy and graph literacy of the audience., Conclusion: A substantial and rapidly expanding evidence base exists for risk communication. Developers of tools to facilitate evidence-based decision making should apply these principles to improve the quality of risk communication in practice.