Political affiliation moderates subjective interpretations of COVID-19 graphs

We examined the relationship between political affiliation, perceptual (percentage, slope) estimates, and subjective judgements of disease prevalence and mortality across three chart types. An online survey (N = 787) exposed separate groups of participants to charts displaying (a) COVID-19 data or (b) COVID-19 data labeled ‘Influenza (Flu)’. Block 1 examined responses to cross-sectional mortality data (bar graphs, treemaps);results revealed that perceptual estimates comparing mortality in two countries were similar across political affiliations and chart types (all ps > .05), while subjective judgements revealed a disease x political party interaction (p < .05). Although Democrats and Republicans provided similar proportion estimates, Democrats interpreted mortality to be higher than Republicans;Democrats also interpreted mortality to be higher for COVID-19 than Influenza. Block 2 examined responses to time series (line graphs);Democrats and Republicans estimated greater slopes for COVID-19 trend lines than Influenza lines (p < .001);subjective judgements revealed a disease x political party interaction (p < .05). Democrats and Republicans indicated similar subjective rates of change for COVID-19 trends, and Democrats indicated lower subjective rates of change for Influenza than in any other condition. Thus, while Democrats and Republicans saw the graphs similarly in terms of percentages and line slopes, their subjective interpretations diverged. While we may see graphs of infectious disease data similarly from a purely mathematical or geometric perspective, our political affiliations may moderate how we subjectively interpret the data.

Environmental Considerations for Effective Telehealth Encounters: A Narrative Review and Implications for Best Practice.

Introduction: Due to the reduction in-person visits, the COVID-19 pandemic has led to expansions in the use of telehealth technology to provide patient care, yet clinicians lack evidence-based guidance on how to most effectively use video communication to enhance patient experience and outcomes. Methods: A narrative review was conducted to describe environmental factors derived from research in social psychology and human-computer interaction (HCI) that may guide effective video-based clinician-patient telehealth communication. Results: Factors such as nonverbal cues, spatial proximity, professionalism cues, and ambient features play an important role in patient experience. We present a visual typology of telehealth backgrounds to inform clinical practice and guide future research. Discussion: A growing body of empirical evidence indicates that environmental cues may play an essential role in establishing psychological safety, improving patient experience, and supporting clinical efficacy in these virtual experiences. Conclusion: The expanded use of telehealth visits suggests the need for further research on the relative effects of these environmental factors on patient experience and outcomes.