Using the Short Graph Literacy Scale to Predict Precursors of Health Behavior Change

Okan, Yasmina; Janssen, Eva; Galešić, Mirta; Waters, Erika A.

doi:10.1177/0272989x19829728

Cited by 62 publications

(48 citation statements)

References 63 publications

(140 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We measured graph literacy using the Short Graph Literacy Scale. 27 It contains 4 items that assess comprehension of health-related information presented in graphical formats. The graph literacy score represents the sum of correct answers.…”

Section: Methodsmentioning

confidence: 99%

When Does the Incremental Risk Format Aid Informed Medical Decisions? The Role of Learning, Feedback, and Number of Treatment Options

et al. 2020

View full text Add to dashboard Cite

Background. Informed medical decisions require understanding the benefits and risks of treatments. This entails comparing treatment outcomes to a control group. The incremental risk format has been recommended as it directly visualizes the differences between treatment and control group in 1 graph, whereas they have to be calculated from 2 separate graphs in the total risk format. We investigated when the incremental risk format aids understanding. Methods. In 2 experiments, participants received information about medical treatments, either as incremental or total risk format. We assessed verbatim knowledge (precise quantitative knowledge), gist knowledge (knowledge of essential meaning), and evaluations of the formats. Study 1 ( N = 99) consisted of only 1 trial with medical information and also assessed recall. Study 2 ( N = 222) assessed learning across multiple trials and also varied the presence of feedback and the number of treatment options. Results. In study 1, the incremental risk format (v. total risk format) led to worse knowledge, recall, and evaluations. In study 2, participants learned to understand the incremental risk format over time, resulting in comparable verbatim knowledge and evaluations as in the total risk format, as well as in even better gist knowledge. Feedback and number of treatment options did not moderate the effect of risk format. Limitations. The studies were conducted with nonpatient samples, and study 2 employed hypothetical treatments. Conclusions. The incremental risk format was initially less understandable than the total risk format. After a short learning period, however, the incremental risk format resulted in better gist knowledge and was comparable otherwise, which suggests that participants had to get used to that format. This has important implications for the study of new formats.

show abstract

Section: Methodsmentioning

confidence: 99%

When Does the Incremental Risk Format Aid Informed Medical Decisions? The Role of Learning, Feedback, and Number of Treatment Options

et al. 2020

View full text Add to dashboard Cite

show abstract

“…(4) Graph comprehension & completion. Finally, we assessed the graph literacy of all participants using the Short Graph Literacy (SGL) scale [44]. The SGL is a validated questionnaire consisting of four questions -each including a visual graph.…”

Section: Participants and Proceduresmentioning

confidence: 99%

Effect of Information Presentation on Fairness Perceptions of Machine Learning Predictors

Berkel

Gonçalves

Russo

et al. 2021

Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

View full text Add to dashboard Cite

The uptake of artifcial intelligence-based applications raises concerns about the fairness and transparency of AI behaviour. Consequently, the Computer Science community calls for the involvement of the general public in the design and evaluation of AI systems. Assessing the fairness of individual predictors is an essential step in the development of equitable algorithms. In this study, we evaluate the efect of two common visualisation techniques (text-based and scatterplot) and the display of the outcome information (i.e., ground-truth) on the perceived fairness of predictors. Our results from an online crowdsourcing study (N = 80) show that the chosen visualisation technique signifcantly alters people's fairness perception and that the presented scenario, as well as the participant's gender and past education, infuence perceived fairness. Based on these results we draw recommendations for future work that seeks to involve non-experts in AI fairness evaluations. CCS CONCEPTS• Human-centered computing → Human computer interaction (HCI); Collaborative and social computing; Empirical studies in collaborative and social computing.

show abstract

“…__________ % will get the infection.'' Graph literacy 43,74 ''The graph below shows the percentage of people who die from different types of cancer. About what percentage of people who die from cancer die from cancer B, cancer C, and cancer D combined?''…”

Section: Analysis Planmentioning

confidence: 99%

Risk Ladder, Table, or Bulleted List? Identifying Formats That Effectively Communicate Personalized Risk and Risk Reduction Information for Multiple Diseases

et al. 2020

Self Cite

View full text Add to dashboard Cite

Background Personalized medicine may increase the amount of probabilistic information patients encounter. Little guidance exists about communicating risk for multiple diseases simultaneously or about communicating how changes in risk factors affect risk (hereafter “risk reduction”). Purpose To determine how to communicate personalized risk and risk reduction information for up to 5 diseases associated with insufficient physical activity in a way laypeople can understand and that increases intentions. Methods We recruited 500 participants with <150 min weekly of physical activity from community settings. Participants completed risk assessments for diabetes, heart disease, stroke, colon cancer, and breast cancer (women only) on a smartphone. Then, they were randomly assigned to view personalized risk and risk reduction information organized as a bulleted list, a simplified table, or a specialized vertical bar graph (“risk ladder”). Last, they completed a questionnaire assessing outcomes. Personalized risk and risk reduction information was presented as categories (e.g., “very low”). Our analytic sample ( N = 372) included 41.3% individuals from underrepresented racial/ethnic backgrounds, 15.9% with vocational-technical training or less, 84.7% women, 43.8% aged 50 to 64 y, and 71.8% who were overweight/obese. Results Analyses of covariance with post hoc comparisons showed that the risk ladder elicited higher gist comprehension than the bulleted list ( P = 0.01). There were no significant main effects on verbatim comprehension or physical activity intentions and no moderation by sex, race/ethnicity, education, numeracy, or graph literacy ( P > 0.05). Sequential mediation analyses revealed a small beneficial indirect effect of risk ladder versus list on intentions through gist comprehension and then through perceived risk ( bIndirectEffect = 0.02, 95% confidence interval: 0.00, 0.04). Conclusion Risk ladders can communicate the gist meaning of multiple pieces of risk information to individuals from many sociodemographic backgrounds and with varying levels of facility with numbers and graphs.

show abstract

Using the Short Graph Literacy Scale to Predict Precursors of Health Behavior Change

Cited by 62 publications

References 63 publications

When Does the Incremental Risk Format Aid Informed Medical Decisions? The Role of Learning, Feedback, and Number of Treatment Options

When Does the Incremental Risk Format Aid Informed Medical Decisions? The Role of Learning, Feedback, and Number of Treatment Options

Effect of Information Presentation on Fairness Perceptions of Machine Learning Predictors

Risk Ladder, Table, or Bulleted List? Identifying Formats That Effectively Communicate Personalized Risk and Risk Reduction Information for Multiple Diseases

Contact Info

Product

Resources

About