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Risk information is increasingly available to health care providers and patients thanks to a growing body of health outcomes research and clinical prediction models. Meanwhile, communicating such information is encouraged for a variety of reasons. Yet clinicians often struggle to communicate risk information—or forego the task altogether due to various challenges. The challenges are real, and this paper briefly discusses six of them: (1) Clinician reliance on verbal risk descriptions, (2) Low patient numeracy; (3) Lack of meaningful numeric evidence; (4) Patient use of heuristics; (5) Uncertain risk information; and (6) The curse of knowledge. Specific strategies exist for clinicians, though, to overcome these complex challenges. In the paper, we present evidence-based best practices with examples of what clinicians can do to effectively communicate risk information to their patients (and what they should not do). The best practices include communicating with numbers, not only words; decreasing cognitive effort for patients; providing the meaning of numeric risk data important to decisions; acknowledging uncertainty; and testing communication with patients through teach-back techniques. We conclude by recommending that clinicians integrate these strategies into their existing scripts for patient encounters. 

Abstract BackgroundWildfire smoke contributes substantially to the global disease burden and is a major cause of air pollution in the US states of Oregon and Washington. Climate change is expected to bring more wildfires to this region. Social media is a popular platform for health promotion and a need exists for effective communication about smoke risks and mitigation measures to educate citizens and safeguard public health. MethodsUsing a sample of 1,287 Tweets from 2022, we aimed to analyze temporal Tweeting patterns in relation to potential smoke exposure and evaluate and compare institutions’ use of social media communication best practices which include (i) encouraging adoption of smoke-protective actions; (ii) leveraging numeric, verbal, and Air Quality Index risk information; and (iii) promoting community-building. Tweets were characterized using keyword searches and the Linguistic Inquiry and Word Count (LIWC) software. Descriptive and inferential statistics were carried out. Results44% of Tweets in our sample were authored between January-August 2022, prior to peak wildfire smoke levels, whereas 54% of Tweets were authored during the two-month peak in smoke (September-October). Institutional accounts used Twitter (or X) to encourage the adoption of smoke-related protective actions (82% of Tweets), more than they used it to disseminate wildfire smoke risk information (25%) or promote community-building (47%). Only 10% of Tweets discussed populations vulnerable to wildfire smoke health effects, and 14% mentioned smoke mitigation measures. Tweets from Washington-based accounts used significantly more verbal and numeric risk information to discuss wildfire smoke than Oregon-based accounts (p = 0.042 andp = 0.003, respectively); however, Tweets from Oregon-based accounts on average contained a higher percentage of words associated with community-building language (p < 0.001). ConclusionsThis research provides practical recommendations for public health practitioners and researchers communicating wildfire smoke risks on social media. As exposures to wildfire smoke rise due to climate change, reducing the environmental disease burden requires health officials to leverage popular communication platforms, distribute necessary health-related messaging rapidly, and get the message right. Timely, evidence-based, and theory-driven messaging is critical for educating and empowering individuals to make informed decisions about protecting themselves from harmful exposures. Thus, proactive and sustained communications about wildfire smoke should be prioritized even during wildfire “off-seasons.” 

Abstract Innumeracy (lack of math skills) among nonscientists often leads climate scientists and others to avoid communicating numbers due to concerns that the public will not understand them and may disengage. However, people often report preferring to receive numbers; providing them also can improve decisions. Here, we demonstrated that the presence vs. absence of at least one Arabic integer in climate-related social-media posts increased sharing up to 31.7% but, counter to hypothesis, decreased liking of messages 5.2% in two preregistered observational studies (climate scientists on Twitter, N  > 8 million Tweets; climate subreddit, N  > 17,000 posts and comments). We speculated that the decreased liking was due, not to reduced engagement, but to more negative feelings towards climate-related content described with numeric precision. A preregistered within-participant experiment (N = 212) then varied whether climate consequences were described using Arabic integers (e.g. “90%”) or another format (e.g. verbal terms, “almost all”). The presence of Arabic integers about consequences led to more sharing, wanting to find out more, and greater trust and perceptions of an expert messenger; perceived trust and expertise appeared to mediate effects on sharing and wanting to find out more. Arabic integers about consequences again led to more negative feelings about the Tweets as if numbers clarified the dismaying magnitude of climate threats. Our results indicate that harnessing the power of numbers could increase public trust and concern regarding this defining issue of our time. Communicators, however, should also consider counteracting associated negative feelings—that could halt action—by providing feasible solutions to increase people's self-efficacy. 

Abstract Background. Wildfire smoke events are increasing in frequency and intensity due to climate change. Children are especially vulnerable to health effects even at moderate smoke levels. However, it is unclear how parents respond to Air Quality Indices (AQIs) frequently used by agencies to communicate air pollution health risks.Methods. In an experiment (3 × 2 × 2 factorial design), 2,100 parents were randomly assigned to view one of twelve adapted AQI infographics that varied by visual (table, line, gauge), index type (AQI [0-500], AQHI [1-11+]), and risk level (moderate, high). Participants were told to imagine encountering the infographic in a short-term exposure scenario. They reported worry about wildfire smoke, intentions to take risk-mitigating actions (e.g., air purifier use), and support for various exposure reduction policies. Subsequently, participants were told to imagine encountering the same infographic daily during a school week in a long-term exposure scenario and again reported worry, action intentions, and policy support.Results. Parents’ responses significantly differentiated between risk levels that both pose a threat to children’s health; worry and action intentions were much higher in the high-risk group than the moderate-risk group in both short-exposure (F = 748.68 p<.001; F = 411.59, p<.001) and long-exposure scenarios (F = 470.51, p<.001; F = 212.01, p<.001). However, in the short-exposure scenario, when shown the AQHI [1-11+] with either the line or gauge visuals, parents’ action intentions were more similar between moderate- and high-risk level groups (3-way interaction, F = 6.03, p = .002).Conclusions. These results suggest some index formats such as the AQHI—rather than the AQI—may better attune parents to moderate levels of wildfire smoke being dangerous to children’s health. Our research offers insights for agencies and officials seeking to improve current public education efforts during wildfire smoke events and speaks to the critical need to educate parents and help them act short-term and long-term to protect children’s health. 

Abstract Whether to undergo genome sequencing in a clinical or research context is generally a voluntary choice. Individuals are often motivated to learn genomic information even when clinical utility—the possibility that the test could inform medical recommendations or health outcomes—is low or absent. Motivations to seek one's genomic information can be cognitive, affective, social, or mixed (e.g., cognitive and affective) in nature. These motivations are based on the perceived value of the information, specifically, itsclinicalutility andpersonalutility. We suggest that motivations to learn genomic information are no different from motivations to learn other types of personal information, including one's health status and disease risk. Here, we review behavioral science relevant to motivations that may drive engagement with genome sequencing, both in the presence of varying degrees of clinical utility and in the absence of clinical utility. Specifically, we elucidate 10 motivations that are expected to underlie decisions to undergo genome sequencing. Recognizing these motivations to learn genomic information will guide future research and ultimately help clinicians to facilitate informed decision making among individuals as genome sequencing becomes increasingly available.