Do colored cells in risk matrices affect decision-making and risk perception? Insights from randomized controlled studies

Risk matrices communicate the likelihood and potential impact of risks and are often used to inform decision-making around risk mitigations. The merits and demerits of risk matrices in general have been discussed extensively, yet little attention has been paid to the potential influence of color in risk matrices on their users. We draw from fuzzy-trace theory and hypothesize that when color is present, individuals are likely to place greater value on reducing risks that cross color boundaries (i.e., the boundary-crossing effect), leading to sub-optimal decision making. In two randomized controlled studies, employing forced-choice and willingness-to-pay measures to investigate the boundary-crossing effect in two different color formats for risk matrices, we find preliminary evidence to support our hypotheses that color can influence decision making. The evidence also suggests that the boundary-crossing effect is only present in, or is stronger for, higher numeracy individuals. We therefore recommend that designers should consider avoiding color in risk matrices, particularly in situations where these are likely to be used by highly numerate individuals, if the communication goal is to inform in an unbiased way

Stochasticity of Cosmic Rays from Supernova Remnants and the Ionization Rates in Molecular Clouds

Cosmic rays are the only agent able to penetrate into the interior of dense molecular clouds. Depositing (part of) their energy through ionisation, cosmic rays play an essential role in determining the physical and chemical evolution of star-forming regions. To a first approximation their effect can be quantified by the cosmic-ray induced ionization rate. Interestingly, theoretical estimates of the ionization rate assuming the cosmic-ray spectra observed in the local interstellar medium result in an ionization rate that is one to two orders of magnitude below the values inferred from observations. However, due to the discrete nature of sources, the local spectra of MeV cosmic rays are in general not representative for the spectra elsewhere in the Galaxy. Such stochasticity effects have the potential of reconciling modelled ionization rates with measured ones. Here, we model the distribution of low-energy cosmic-ray spectra expected from a statistical population of supernova remnants in the Milky Way. The corresponding distribution for the ionization rate is derived and confronted with data. We find that the stochastic uncertainty helps with explaining the surprisingly high ionization rates observed in many molecular clouds.Comment: 14 pages, 5 figure

How People Understand Risk Matrices, and How Matrix Design Can Improve their Use: Findings from Randomized Controlled Studies.

Risk matrices are a common way to communicate the likelihood and potential impacts of a variety of risks. Until now, there has been little empirical work on their effectiveness in supporting understanding and decision making, and on how different design choices affect these. In this pair of online experiments (total n = 2699), we show that risk matrices are not always superior to text for the presentation of risk information, and that a nonlinear/geometric labeling scheme helps matrix comprehension (when the likelihood/impact scales are nonlinear). To a lesser degree, results suggested that changing the shape of the matrix so that cells increase in size nonlinearly facilitates comprehension as compared to text alone, and that comprehension might be enhanced by integrating further details about the likelihood and impact onto the axes of the matrix rather than putting them in a separate key. These changes did not affect participants' preference for reducing impact over reducing likelihood when making decisions about risk mitigation. We recommend that designers of risk matrices consider these changes to facilitate better understanding of relationships among risks

Communicating personalized risks from COVID-19: guidelines from an empirical study.

As increasing amounts of data accumulate on the effects of the novel coronavirus SARS-CoV-2 and the risk factors that lead to poor outcomes, it is possible to produce personalized estimates of the risks faced by groups of people with different characteristics. The challenge of how to communicate these then becomes apparent. Based on empirical work (total n = 5520, UK) supported by in-person interviews with the public and physicians, we make recommendations on the presentation of such information. These include: using predominantly percentages when communicating the absolute risk, but also providing, for balance, a format which conveys a contrasting (higher) perception of risk (expected frequency out of 10 000); using a visual linear scale cut at an appropriate point to illustrate the maximum risk, explained through an illustrative 'persona' who might face that highest level of risk; and providing context to the absolute risk through presenting a range of other 'personas' illustrating people who would face risks of a wide range of different levels. These 'personas' should have their major risk factors (age, existing health conditions) described. By contrast, giving people absolute likelihoods of other risks they face in an attempt to add context was considered less helpful. We note that observed effect sizes generally were small. However, even small effects are meaningful and relevant when scaled up to population levels

Communicating personalised risks from COVID-19: guidelines from an empirical study

As increasing amounts of data accumulate on the effects of the novel coronavirus Sars-CoV-2 and the risk factors that lead to poor outcomes, it is possible to produce personalised estimates of the risks faced by groups of people with different characteristics. The challenge of how to communicate these then becomes apparent. Based on empirical work (total n=5,520, UK) supported by in-person interviews with the public and physicians, we make recommendations on the presentation of such information. These include: using predominantly percentages when communicating the absolute risk, but also providing, for balance, a format which conveys a contrasting (higher) perception of risk (expected frequency out of 10,000); using a visual linear scale cut at an appropriate point to illustrate the maximum risk, explained through an illustrative ‘persona’ who might face that highest level of risk; and providing context to the absolute risk through presenting a range of other ‘personas’ illustrating people who would face risks of a wide range of different levels. These ‘personas’ should have their major risk factors (age, existing health conditions) described. By contrast, giving people absolute likelihoods of other risks they face in an attempt to add context was considered less helpful

Communicating personalized risks from COVID-19: guidelines from an empirical study.

As increasing amounts of data accumulate on the effects of the novel coronavirus SARS-CoV-2 and the risk factors that lead to poor outcomes, it is possible to produce personalized estimates of the risks faced by groups of people with different characteristics. The challenge of how to communicate these then becomes apparent. Based on empirical work (total n = 5520, UK) supported by in-person interviews with the public and physicians, we make recommendations on the presentation of such information. These include: using predominantly percentages when communicating the absolute risk, but also providing, for balance, a format which conveys a contrasting (higher) perception of risk (expected frequency out of 10 000); using a visual linear scale cut at an appropriate point to illustrate the maximum risk, explained through an illustrative 'persona' who might face that highest level of risk; and providing context to the absolute risk through presenting a range of other 'personas' illustrating people who would face risks of a wide range of different levels. These 'personas' should have their major risk factors (age, existing health conditions) described. By contrast, giving people absolute likelihoods of other risks they face in an attempt to add context was considered less helpful. We note that observed effect sizes generally were small. However, even small effects are meaningful and relevant when scaled up to population levels

Susceptibility to misinformation about COVID-19 around the world.

Misinformation about COVID-19 is a major threat to public health. Using five national samples from the UK (n = 1050 and n = 1150), Ireland (n = 700), the USA (n = 700), Spain (n = 700) and Mexico (n = 700), we examine predictors of belief in the most common statements about the virus that contain misinformation. We also investigate the prevalence of belief in COVID-19 misinformation across different countries and the role of belief in such misinformation in predicting relevant health behaviours. We find that while public belief in misinformation about COVID-19 is not particularly common, a substantial proportion views this type of misinformation as highly reliable in each country surveyed. In addition, a small group of participants find common factual information about the virus highly unreliable. We also find that increased susceptibility to misinformation negatively affects people's self-reported compliance with public health guidance about COVID-19, as well as people's willingness to get vaccinated against the virus and to recommend the vaccine to vulnerable friends and family. Across all countries surveyed, we find that higher trust in scientists and having higher numeracy skills were associated with lower susceptibility to coronavirus-related misinformation. Taken together, these results demonstrate a clear link between susceptibility to misinformation and both vaccine hesitancy and a reduced likelihood to comply with health guidance measures, and suggest that interventions which aim to improve critical thinking and trust in science may be a promising avenue for future research

Correlates of intended COVID-19 vaccine acceptance across time and countries: results from a series of cross-sectional surveys.

Funder: David and Claudia Harding FoundationOBJECTIVE: Describe demographical, social and psychological correlates of willingness to receive a COVID-19 vaccine. SETTING: Series of online surveys undertaken between March and October 2020. PARTICIPANTS: A total of 25 separate national samples (matched to country population by age and sex) in 12 different countries were recruited through online panel providers (n=25 334). PRIMARY OUTCOME MEASURES: Reported willingness to receive a COVID-19 vaccination. RESULTS: Reported willingness to receive a vaccine varied widely across samples, ranging from 63% to 88%. Multivariate logistic regression analyses reveal sex (female OR=0.59, 95% CI 0.55 to 0.64), trust in medical and scientific experts (OR=1.28, 95% CI 1.22 to 1.34) and worry about the COVID-19 virus (OR=1.47, 95% CI 1.41 to 1.53) as the strongest correlates of stated vaccine acceptance considering pooled data and the most consistent correlates across countries. In a subset of UK samples, we show that these effects are robust after controlling for attitudes towards vaccination in general. CONCLUSIONS: Our results indicate that the burden of trust largely rests on the shoulders of the scientific and medical community, with implications for how future COVID-19 vaccination information should be communicated to maximise uptake