Exploring the differences in SPC convective outlook interpretation using categorical and numeric information

While previous work has shown the Storm Prediction Center (SPC) convective outlooks accurately capture meteorological outcomes, evidence suggests stakeholders and the public may misinterpret the categorical words currently used in the product. This work attempts to address this problem by investigating public reactions to alternative information formats that include numeric information: (1) numeric risk levels (i.e., “Level 2 of 5”) and (2) numeric probabilities (i.e., “a 5% chance”). In addition, it explores how different combinations of the categorical labels with numeric information may impact public reactions to the product. Survey data comes from the 2020 Severe Weather and Society Survey, a nationally representative survey of US adults. Participants were shown varying combinations of the information formats of interest, and then rated their concern about the weather and the likelihood of changing plans in response to the given information. Results indicate that providing numeric information (in the form of levels or probabilities) increases the likelihood of participants correctly interpreting the convective outlook information relative to categorical labels alone. Including the categorical labels increases misinterpretation, regardless of whether numeric information was included alongside the labels. Finally, findings indicate participants’ numeracy (or their ability to understand and work with numbers) had an impact on correct interpretation of the order of the outlook labels. Although there are many challenges to correctly interpreting the SPC convective outlook, using only numeric labels instead of the current categorical labels may be a relatively straightforward change that could improve public interpretation of the product.

Imprecision and Preferences in Interpretation of Verbal Probabilities in Health: a Systematic Review

Introduction Many health providers and communicators who are concerned that patients will not understand numbers instead use verbal probabilities (e.g., terms such as “rare” or “common”) to convey the gist of a health message. Objective To assess patient interpretation of and preferences for verbal probability information in health contexts. Methods We conducted a systematic review of literature published through September 2020. Original studies conducted in English with samples representative of lay populations were included if they assessed health-related information and elicited either (a) numerical estimates of verbal probability terms or (b) preferences for verbal vs. quantitative risk information. Results We identified 33 original studies that referenced 145 verbal probability terms, 45 of which were included in at least two studies and 19 in three or more. Numerical interpretations of each verbal term were extremely variable. For example, average interpretations of the term “rare” ranged from 7 to 21%, and for “common,” the range was 34 to 71%. In a subset of 9 studies, lay estimates of verbal probability terms were far higher than the standard interpretations established by the European Commission for drug labels. In 10 of 12 samples where preferences were elicited, most participants preferred numerical information, alone or in combination with verbal labels. Conclusion Numerical interpretation of verbal probabilities is extremely variable and does not correspond well to the numerical probabilities established by expert panels. Most patients appear to prefer quantitative risk information, alone or in combination with verbal labels. Health professionals should be aware that avoiding numeric information to describe risks may not match patient preferences, and that patients interpret verbal risk terms in a highly variable way.

Math predictors of numeric health and non-health decision-making problems

People frequently encounter numeric information in medical and health contexts. In this paper, we investigated the math factors that are associated with decision-making accuracy in health and non-health contexts. This is an important endeavor given that there is relatively little cross-talk between math cognition researchers and those studying health decision making. Ninety adults (M = 37 years; 86% White; 51% male) answered hypothetical health decision-making problems, and 93 adults (M = 36 years; 75% White; 42% males) answered a non-health decision-making problem. All participants were recruited from an online panel. Each participant completed a battery of tasks involving objective math skills (e.g., whole number and fraction estimation, comparison, arithmetic fluency, objective numeracy, etc.) and subjective ratings of their math attitudes, anxiety, and subjective numeracy. In separate regression models, we identified which objective and subjective math measures were associated with health and non-health decision-making accuracy. Magnitude comparison accuracy, multi-step arithmetic accuracy, and math anxiety accounted for significant variance in health decision-making accuracy, whereas attention to math, as illustrated in open-ended strategy reports, was the only significant predictor of non-health decision-making accuracy. Importantly, reliable and valid measures from the math cognition literature were more strongly related to health decision-making accuracy than were commonly used subjective and objective measures of numeracy. These results have a practical implication: Understanding the math factors that are associated with health decision-making performance could inform future interventions to enhance comprehension of numeric health information.

Applying Skimming Strategy in Teaching Reading Descriptive Text

Reading is an important activity to enrich knowledge. However, it is considered as a difficult skill to learn. There are many students facing difficulties in comprehending the content of the reading text, including descriptive text. A strategy which can solve such problems is needed. Skimming strategy is the strategy which enable students to quickly find the main idea and relevant information of the text. Based on those reasons, the research questions are proposed as follows: (1) How can skimming strategy be implemented in teaching reading descriptive texts? and (2) How can the second year students’ reading comprehension ability be improved using skimming strategy? This study used two research methods. Those were qualitative and quantitative research design. They were collected to prove the implementation and the significant improvement by using skimming strategy. Both numeric information and the real observation were evaluated to gain clear and strong data. The researcher acted as the observer to see the process of teaching and learning descriptive texts in three stages. The results of the research showed that the teacher and students implemented four steps of skimming strategy, which are: (1) read the first several paragraph; (2) leave out material; (3) find the main ideas; (4) read fast. The t-test of this research was also calculated to see whether there was a significant difference between control and experimental class and the mean was also measured to see the students’ improvement score. The mean score of experimental class was better than the mean score of control class. However, the t-test result showed that there was no significant difference between experimental and control class.

Current Challenges When Using Numbers in Patient Decision Aids: Advanced Concepts

Background Decision aid developers have to convey complex task-specific numeric information in a way that minimizes bias and promotes understanding of the options available within a particular decision. Whereas our companion paper summarizes fundamental issues, this article focuses on more complex, task-specific aspects of presenting numeric information in patient decision aids. Methods As part of the International Patient Decision Aids Standards third evidence update, we gathered an expert panel of 9 international experts who revised and expanded the topics covered in the 2013 review working in groups of 2 to 3 to update the evidence, based on their expertise and targeted searches of the literature. The full panel then reviewed and provided additional revisions, reaching consensus on the final version. Results Five of the 10 topics addressed more complex task-specific issues. We found strong evidence for using independent event rates and/or incremental absolute risk differences for the effect size of test and screening outcomes. Simple visual formats can help to reduce common judgment biases and enhance comprehension but can be misleading if not well designed. Graph literacy can moderate the effectiveness of visual formats and hence should be considered in tool design. There is less evidence supporting the inclusion of personalized and interactive risk estimates. Discussion More complex numeric information. such as the size of the benefits and harms for decision options, can be better understood by using incremental absolute risk differences alongside well-designed visual formats that consider the graph literacy of the intended audience. More research is needed into when and how to use personalized and/or interactive risk estimates because their complexity and accessibility may affect their feasibility in clinical practice.

Dissociating Representations of Time and Number in Reinforcement-Rate Learning by Deletion of the GluA1 AMPA Receptor Subunit in Mice

Theories of learning differ in whether they assume that learning reflects the strength of an association between memories or symbolic encoding of the statistical properties of events. We provide novel evidence for symbolic encoding of informational variables by demonstrating that sensitivity to time and number in learning is dissociable. Whereas responding in normal mice was dependent on reinforcement rate, responding in mice that lacked the GluA1 AMPA receptor subunit was insensitive to reinforcement rate and, instead, dependent on the number of times a cue had been paired with reinforcement. This suggests that GluA1 is necessary for weighting numeric information by temporal information in order to calculate reinforcement rate. Sample sizes per genotype varied between seven and 23 across six experiments and consisted of both male and female mice. The results provide evidence for explicit encoding of variables by animals rather than implicit encoding via variations in associative strength.

Provide Evaluative Meaning and Direct Attention

This chapter, “Provide Evaluative Meaning and Direct Attention,” links earlier chapters about the habits of the highly numerate to evidence-based communication solutions that especially help the less objectively numerate. In particular, Chapter 17 provides techniques to assist decision makers when they are unable to evaluate the good or bad meaning of numeric information. These techniques range from providing numeric comparisons to carefully using evaluative labels and symbols, more imaginable data formats, and emotion. Evidence exists that emotional communications also facilitate communication by grabbing and holding attention. Other methods that allow the less numerate access into these attentional habits of the highly numerate include ordering information based on its importance, highlighting the meaning of only the most important information, and increasing the visual salience of key numbers. The chapter concludes with a brief summary of some of the challenges that communicators face to presenting information well.

Provide Numbers but Reduce Cognitive Effort

This chapter, “Provide Numbers but Reduce Cognitive Effort,” challenges the notion that numbers mislead people and should be avoided. This chapter recommends instead that communicators provide numeric information but reduce how much effort is required from consumers and patients to use it. In particular, the chapter discusses five ways that providing numeric information is useful for decision makers. Then it summarizes evidence-based methods to present such numeric data to decrease effort and increase numeric comprehension and use. The methods include providing fewer options and less information, presenting absolute risks, keeping denominators constant, doing any needed math operations for them, and using appropriate visual displays. Concrete examples are explained in plain language.

The Highly Numerate Understand the Feel of Numbers

This chapter, “The Highly Numerate Understand the Feel of Numbers,” discusses the critical importance to decision making of good and bad feelings derived from numeric information. Decisions are hard to make sometimes because we don’t have a feel for what an important number means. The highly numerate, however, appear to compare numbers more, derive more number-related affect, and use those feelings to guide their decision making. This process is believed to underlie part of the highly numerate’s ability to be more sensitive to numeric data in judgments and decisions. This same process, however, can result in the highly numerate overusing or making faulty interpretations of numbers. This chapter continues to explain the rules and principles followed by the highly numerate.

Thinking Harder with Numbers

This chapter “Thinking Harder with Numbers,” is the first of four chapters focusing on how more objectively numerate people think harder and, as a result, judge and decide better when numeric information is involved. First, they attend to and search for numeric information more than the less numerate do. Second, they think more with numbers by (1) thinking longer in numeric decisions; (2) performing more numeric operations, for example by transforming numeric information from one format to another; and (3) understanding better what they know and do not know. These processes generally result in the highly numerate making better decisions. The highly numerate not only understand numbers better, but they also have better habits for dealing with numeric and non-numeric information in judgment and choice.