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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.

 

To identify what patient-related characteristics have been reported to be associated with the occurrence of shared decision-making (SDM) about treatment.

Scoping review.

Peer-reviewed articles in English or Dutch reporting on associations between patient-related characteristics and the occurrence of SDM for actual treatment decisions.

COCHRANE Library, Embase, MEDLINE, PsycInfo, PubMed and Web of Science were systematically searched for articles published until 25 March 2019.

The search yielded 5289 hits of which 53 were retained. Multiple categories of patient characteristics were identified: (1) sociodemographic characteristics (eg, gender), (2) general health and clinical characteristics (eg, symptom severity), (3) psychological characteristics and coping with illness (eg, self-efficacy) and (4) SDM style or preference. Many characteristics showed no association or unclear relationships with SDM occurrence. For example, for female gender positive, negative and, most frequently, non-significant associations were seen.

A large variety of patient-related characteristics have been studied, but for many the association with SDM occurrence remains unclear. The results will caution often-made assumptions about associations and provide an important step to target effective interventions to foster SDM with all patients.

 

Objective numeracy appears to support better medical decisions and health outcomes. The more numerate generally understand and use numbers more and make better medical decisions, including more informed medical choices. Numeric self-efficacy—an aspect of subjective numeracy that is also known as numeric confidence—also relates to decision making via emotional reactions to and inferences from experienced difficulty with numbers and via persistence linked with numeric comprehension and healthier behaviors over time. Furthermore, it moderates the effects of objective numeracy on medical outcomes.

We briefly review the numeracy and decision-making literature and then summarize more recent literature on 3 separable effects of numeric self-efficacy. Although dual-process theories can account for the generally superior decision making of the highly numerate, they have neglected effects of numeric self-efficacy. We discuss implications for medical decision-making (MDM) research and practice. Finally, we propose a modification to dual-process theories, adding a “motivational mind” to integrate the effects of numeric self-efficacy on decision-making processes (i.e., inferences from experienced difficulty with numbers, greater persistence, and greater use of objective-numeracy skills) important to high-quality MDM.

The power of numeric self-efficacy (confidence) has been little considered in MDM, but many medical decisions and behaviors require persistence to be successful over time (e.g., comprehension, medical-recommendation adherence). Including numeric self-efficacy in research and theorizing will increase understanding of MDM and promote development of better decision interventions.

Research demonstrates that objective numeracy supports better medical decisions and health outcomes. The power of numeric self-efficacy (aka numeric confidence) has been little considered but appears critical to emotional reactions and inferences that patients and others make when encountering numeric information (e.g., in decision aids) and to greater persistence in medical decision-making tasks involving numbers. The present article proposes a novel modification to dual-process theory to account for newer findings and to describe how numeracy mechanisms can be better understood. Because being able to adapt interventions to improve medical decisions depends in part on having a good theory, future research should incorporate numeric self-efficacy into medical decision-making theories and interventions.

 

Numeracy—the ability to understand and use numeric information—is linked to good decision-making. Several problems exist with current numeracy measures, however. Depending on the participant sample, some existing measures are too easy or too hard; also, established measures often contain items well-known to participants. The current article aimed to develop new numeric understanding measures (NUMs) including a 1-item (1-NUM), 4-item (4-NUM), and 4-item adaptive measure (A-NUM). In a calibration study, 2 participant samples (n = 226 and 264 from Amazon’s Mechanical Turk [MTurk]) each responded to half of 84 novel numeracy items. We calibrated items using 2-parameter logistic item response theory (IRT) models. Based on item parameters, we developed the 3 new numeracy measures. In a subsequent validation study, 600 MTurk participants completed the new numeracy measures, the adaptive Berlin Numeracy Test, and the Weller Rasch-Based Numeracy Test, in randomized order. To establish predictive and convergent validities, participants also completed judgment and decision tasks, Raven’s progressive matrices, a vocabulary test, and demographics. Confirmatory factor analyses suggested that the 1-NUM, 4-NUM, and A-NUM load onto the same factor as existing measures. The NUM scales also showed similar association patterns to subjective numeracy and cognitive ability measures as established measures. Finally, they effectively predicted classic numeracy effects. In fact, based on power analyses, the A-NUM and 4-NUM appeared to confer more power to detect effects than existing measures. Thus, using IRT, we developed 3 brief numeracy measures, using novel items and without sacrificing construct scope. The measures can be downloaded as Qualtrics files (https://osf.io/pcegz/).

 

Climate change poses a multifaceted, complex, and existential threat to human health and well-being, but efforts to communicate these threats to the public lag behind what we know how to do in communication research. Effective communication about climate change’s health risks can improve a wide variety of individual and population health-related outcomes by: (1) helping people better make the connection between climate change and health risks and (2) empowering them to act on that newfound knowledge and understanding. The aim of this manuscript is to highlight communication methods that have received empirical support for improving knowledge uptake and/or driving higher-quality decision making and healthier behaviors and to recommend how to apply them at the intersection of climate change and health. This expert consensus about effective communication methods can be used by healthcare professionals, decision makers, governments, the general public, and other stakeholders including sectors outside of health. In particular, we argue for the use of 11 theory-based, evidence-supported communication strategies and practices. These methods range from leveraging social networks to making careful choices about the use of language, narratives, emotions, visual images, and statistics. Message testing with appropriate groups is also key. When implemented properly, these approaches are likely to improve the outcomes of climate change and health communication efforts.

 

Driving evaluations aim to ensure adequate skills; however, feedback beyond pass/fail is needed for improvement. Therefore, the goal of this study was to inform driving feedback report design to ensure ease of use and understandability while motivating improvements. Participants ages 18–25 years (n = 521) were recruited from CloudResearch Prime Panels to rate one of nine report design conditions with various combinations of five key features: performance summary presence, action plan (AP) length, AP order, AP grading system, and peer comparison presence; they then completed questionnaires. Participants were more motivated to improve when a summary was present (p = 0.02); they rated reports easier to use if they had a long AP (p = 0.01), a short AP paired with a summary (p = 0.007), or an AP with a number grade (p = 0.016); and they rated reports easier to understand if they had a short AP (p = 0.002) or an AP ordered by worst-to-best performance (p = 0.05). These results suggest that feedback reports designed with a performance summary and short, targeted action plan starting with the biggest area for improvement are likely to motivate action to improve driving skills while being easy to use and understand. Future research should evaluate the effect of such a redesigned report on driving outcomes among young drivers.

 

Four studies demonstrate that the public’s understanding of government budgetary expenditures is hampered by difficulty in representing large numerical magnitudes. Despite orders of magnitude difference between millions and billions, study participants struggle with the budgetary magnitudes of government programs. When numerical values are rescaled as smaller magnitudes (in the thousands or lower), lay understanding improves, as indicated by greater sensitivity to numerical ratios and more accurate rank ordering of expenses. A robust benefit of numerical rescaling is demonstrated across a variety of experimental designs, including policy relevant choices and incentive-compatible accuracy measures. This improved sensitivity ultimately impacts funding choices and public perception of respective budgets, indicating the importance of numerical cognition for good citizenship.