'There were some clues': a qualitative study of heuristics used by parents of adolescents to make credibility judgements of online health news articles citing research

Empowering Third-Year Medical Students to Detect Bias and Medical Misinformation Online via Experiential Learning of "Lateral Reading," A Fact-Checker's Technique

Problem: Misleading health information is detrimental to public health. Even physicians can be misled by biased health information; however, medical students and physicians are not taught some of the most effective techniques for identifying bias and misinformation online. Intervention: Using the stages of Kolb's experiential learning cycle as a framework, we aimed to teach 117 third-year students at a United States medical school to apply a fact-checking technique for identifying bias and misinformation called "lateral reading" through a 50-minute learning cycle in a 90-minute class. Each student's concrete experience was to independently read a biased article and rate its credibility, demonstrating their baseline skills at identifying bias. Students were given structured opportunities for reflective observation through individual and large group discussion. Students were guided through abstract conceptualization to determine techniques and frameworks utilized by fact checkers, specifically "lateral reading"-utilizing the internet to research the background of the author, organization, and citations using independent sources before exploring the article itself in depth. Students' active experimentation included re-rating the credibility of the same article and discussing further implications with classmates and instructors. Context: In January 2020, sessions were offered to third-year medical students during their required, longitudinal transition-to-residency course. Impact: Compared to baseline, when using lateral reading, students deemed the article less credible. Students' active experimentation changed whether they identified the organization and sources behind the article as credible. Notably, 86% (53/62) of students who viewed the organization positively pre-intervention did not describe the organization positively post intervention. Similarly, 66% (36/55) of students who cited the sources as positive pre-exercise changed their assessment after the exercise. While three students mentioned the author negatively pre-intervention, none of the 21 students who described the author in a negative fashion post-intervention described the author negatively pre-intervention. Positively describing the organization, author, or sources pre-intervention correlated with differences in credibility rating after the intervention. These findings indicate that teaching students to read laterally may increase their ability to detect bias in online medical information. Lessons Learned: Further research is needed to determine whether students who learned lateral reading via experiential learning will apply this skill in their education and career. Additionally, research should assess whether this skill helps future physicians counter bias and misinformation in ways that improve health.

Lessons Learned From Interdisciplinary Efforts to Combat COVID-19 Misinformation: Development of Agile Integrative Methods From Behavioral Science, Data Science, and Implementation Science

Background

Despite increasing awareness about and advances in addressing social media misinformation, the free flow of false COVID-19 information has continued, affecting individuals' preventive behaviors, including masking, testing, and vaccine uptake.

Objective

In this paper, we describe our multidisciplinary efforts with a specific focus on methods to (1) gather community needs, (2) develop interventions, and (3) conduct large-scale agile and rapid community assessments to examine and combat COVID-19 misinformation.

Methods

We used the Intervention Mapping framework to perform community needs assessment and develop theory-informed interventions. To supplement these rapid and responsive efforts through large-scale online social listening, we developed a novel methodological framework, comprising qualitative inquiry, computational methods, and quantitative network models to analyze publicly available social media data sets to model content-specific misinformation dynamics and guide content tailoring efforts. As part of community needs assessment, we conducted 11 semistructured interviews, 4 listening sessions, and 3 focus groups with community scientists. Further, we used our data repository with 416,927 COVID-19 social media posts to gather information diffusion patterns through digital channels.

Results

Our results from community needs assessment revealed the complex intertwining of personal, cultural, and social influences of misinformation on individual behaviors and engagement. Our social media interventions resulted in limited community engagement and indicated the need for consumer advocacy and influencer recruitment. The linking of theoretical constructs underlying health behaviors to COVID-19-related social media interactions through semantic and syntactic features using our computational models has revealed frequent interaction typologies in factual and misleading COVID-19 posts and indicated significant differences in network metrics such as degree. The performance of our deep learning classifiers was reasonable, with an F-measure of 0.80 for speech acts and 0.81 for behavior constructs.

Conclusions

Our study highlights the strengths of community-based field studies and emphasizes the utility of large-scale social media data sets in enabling rapid intervention tailoring to adapt grassroots community interventions to thwart misinformation seeding and spread among minority communities. Implications for consumer advocacy, data governance, and industry incentives are discussed for the sustainable role of social media solutions in public health.

'A platform for goodness, not for badness': The heuristics of hope in patients' evaluations of online health information

Patient advocates and activists are increasingly relying on online health information that can assist them to manage their health condition. Yet once online, they will confront diverse information whose veracity and utility are difficult to determine. This article offers a sociological analysis of the practical methods, or heuristics, that patient advocates and activists use when making judgements about the credibility and utility of online information. Drawing on the findings from interviews with fifty Australian patient advocates and activists, it is argued that these individuals' use of these heuristics reflects their hopes that information can help them manage their condition which may, in some cases, override fears and uncertainties that arise during searches. The article identifies the common 'rules-of-thumb'-or what we call the 'heuristics of hope'-that patient advocates/activists may use to make judgements and highlights the dangers of over-reliance on them, especially regarding clinically unproven, potentially unsafe treatments. Analyses of the heuristics of hope, we conclude, can assist in understanding the dynamics of decision-making and the role that affect plays in online patient communities which is crucial in an age characterised by the rapid circulation of emotionally charged messages, often based on hope.