Amanda D. Boyd

There is a critical need to strengthen science–society relationships—especially with historically marginalized communities—if we are to better and more equitably manage complicated, intertwined, global challenges at the intersection of environment, health, equity, and well-being. Community-engaged science, which focuses on shared leadership and mutual benefit in scientific partnership with communities, has the potential to transform science, communities, and even society. Despite this promise, however, community-engaged science is not always transformative. Presentations and discussions at the 5th National Academies Science Communication Colloquium demonstrated the importance of creating structures, practices, and a culture of science engagement that prioritizes listening and learning from communities. Based on presentations at the colloquium, research publications, and our own experiences, we share a set of evidence-informed principles that are common to successful community-engaged science across many contexts: respect, humility, listening, reciprocity, mutuality, and reflexivity. We also offer steps the scientific community can take to advance and improve the transformative practice of community-engaged science as part of a productive ecosystem of scientific activities: evolving norms and culture, integrating community science into current systems, building incentives and structures to support community-engaged science, developing a workforce skilled in community engagement, and investing in a coordinated research-to-practice agenda.

Background: Developing optimal health communication strategies about COVID-19 and vaccines requires an understanding of the health literacy level of populations, trusted sources of information, and perceptions of COVID-19 messaging. We examined how health literacy is related to knowledge about COVID-19 among American Indian and Alaska Native (AI/AN) peoples. Methods: We surveyed 788 AI/AN peoples at five Tribal health organizations between January and May 2021. We assessed health literacy using a 4-item health literacy index, and knowledge of COVID-19 in three domains: protection against contracting COVID-19; contracting COVID-19, and clinical manifestations of contraction. Questions also included rating trust of COVID-19 information sources and perceptions of messaging. Results: Participants had a high level of knowledge about COVID-19; however, health literacy was not significantly associated with any domain of COVID-19 knowledge. Participants perceived the most trusted source of COVID-19-related health information to be health professionals, followed by health clinics. Social media was the least trusted source of information. Tribal sources of information were rated on average higher quality than non-tribal sources. Conclusions: AI/AN peoples had high levels of accurate knowledge about COVID-19 regardless of assessed health literacy. Information sources should include Tribal-led organizations and health professionals to increase uptake of COVID-19 messaging.

Background: American Indian and Alaska Native (AI/AN) communities in the United States experience significant health disparities, compounded by socioeconomic challenges, systemic racism, limited health information access, and mistrust of health research, mainstream healthcare, and federal health organizations. Misinformation on digital media and variable digital health literacy further hinder informed decision-making. Objective: Explore digital health literacy, digital technology use for health information-seeking, and the perceived influence of misinformation on health decision-making among AI/AN populations. Methods: In 2022, 540 AI/AN individuals aged 18 and older were surveyed at two large cultural events. Cross-sectional data were collected on health decision-making, influence of misinformation on decisions, digital health literacy, and perceptions of misinformation on specific health topics. Paired t -tests examined differences in perceived effects of misinformation on health decisions. Results: Although 62.7% of respondents felt confident evaluating online health information, only 35.5% used online information for health decisions. The perceived impact of misinformation on one's own health decisions was significantly lower for oneself (16.2%) than for friends and family (26.9%, p < 0.001) or other AI/AN individuals (25.9%, p < 0.001). Participants also believed AI/AN peoples were less vulnerable to misinformation than others (25.9 vs. 28.7%, p < 0.001). Conclusions: Addressing misinformation and digital barriers in AI/AN communities requires targeted public health initiatives. Strategies should focus on improving access to culturally relevant health information, integrating digital literacy education, and strengthening digital infrastructure with supportive policies. Collaborating with trusted community leaders can enhance culturally aligned health communication and information dissemination.

Representativeness in randomized clinical trials remains a critical concern, affecting the external validity of trial results, equitable access to the risks and benefits of research participation, and public trust in clinical research. Although representative participation by members of groups traditionally underrepresented in clinical trials is just a surrogate for true diversity, equity, inclusion, and belonging in clinical trials, it can be quantified, allowing stakeholders to add empirical rigor to diversity, equity, inclusion, and belonging efforts. Multiple ways to measure representativeness have been proposed, including the participation-to-prevalence ratio, raw participation proportions or numbers for relevant subgroups, and enrollment fraction for relevant subgroups. These methods have strengths and weaknesses and may be appropriate to report in certain circumstances, depending on why stakeholders seek to assess representativeness. Stakeholders—including regulatory agencies, journal editors, clinical trial investigators, and trial sponsors—may use quantitative measures of representativeness to establish trial enrollment standards, monitor equitable participation in ongoing trials, and condition funding or drug or device approval on achieving specific representativeness targets. However, using quantitative measures of representativeness in this way could have unintended consequences, including researchers “gaming” recruitment strategies to meet target numbers, overlooking nuanced variations within communities, and potentially incentivizing problematic and exploitative recruitment strategies. Although no single method of measuring representativeness offers a comprehensive solution for increasing diversity, equity, inclusion, and belonging in all randomized clinical trials, a carefully designed, multifaceted approach to measuring representativeness may provide stakeholders with useful perspectives for measuring progress in increasing the diversity of clinical trial participation. For stakeholders seeking a single number to assess the representativeness of a trial enrolling patients with a disease state with well-delineated demographics, the participation-to-prevalence ratio is ideal; however, for a more nuanced view of representativeness, the combination of enrollment fraction in subgroups of relevance plus a full report of the demographics of patients approached for enrollment may be more appropriate.

Artificial intelligence (AI) is changing industries globally. It is also having both positive and negative effects on society. People often learn about technology through media, and so news about AI could have significant impacts on public perceptions of AI. Employing agenda-setting theory, we explore associations between attention to AI news and attitudes about AI. Results of a survey suggest attention to AI news content is associated with perceived economic risks associated with AI. Higher levels of perceived economic risk were associated with greater fear tied to AI. Higher levels of fear were associated with holding more negative perceptions of AI but only among people with lower incomes. We found a negative indirect association between attention to AI news content and negative perceptions of AI through perceived AI risk and fear in serial mediation. This negative indirect association was stronger among people with lower incomes. We discuss the potential effects of media on the public understanding of AI.

Patients, health professionals, and communities use social media to communicate information about health determinants and associated risk factors. Studies have highlighted the potential for social media to reach underserved populations, suggesting these platforms can be used to disseminate health information tailored for diverse and hard-to-reach populations. Little is known, however, about the use of social media among American Indian and Alaska Native populations. The objective of this cross-sectional study is to better understand the use of social media platforms to disseminate health information in this population. We surveyed 429 American Indian and Alaska Native adults attending cultural events in Washington State on their use of various types of social media. We used logistic regressions to assess participant use of Twitter, Snapchat, Facebook, and Instagram as related to participant demographics, including age, gender, education, and residence (either reservation, rural area but not a reservation, or large metropolitan area). Facebook was used by more participants (79%) than other platforms, followed by Instagram (31%); Nearly half of participants used only one social media platform (48%). Age was negatively associated with using Instagram (0.8 OR, 95% CI: 0.7, 0.9) and Snapchat (0.6 OR, 95% CI: 0.5, 0.7). College education was associated with a higher odds of using an additional social media platform compared to those without any college education (2.0 OR, 95% CI: 1.1, 3.6). Most participants used social media platforms, suggesting that they may be a useful tool in disseminating health and health risk information to American Indian and Alaska Native people. Further research should document how social media can be used to effectively disseminate risk and health information across the life course and assess whether it can influence health knowledge and behaviors among this populations.

Lead ammunition is commonly used to hunt waterfowl and other wildlife in the Arctic. Hunting with lead is problematic because the toxicant can be transferred to the consumer. Therefore, it is critical to evaluate perceptions and awareness of the risks associated with using lead ammunition among Arctic populations. Results of the Nunavik Child Development Study (a longitudinal health study gathering information on health and well-being among Inuit in Nunavik, Canada) included advice to eliminate the use of lead ammunition in hunting practices. We surveyed 112 Nunavik residents (93 women; 18 men) about their awareness of lead related messages, use of lead ammunition and risk perceptions about contaminants. Sixty-seven participants (59.8%) reported there was an active hunter in their household. We found that only 27% of participants had heard or seen the messages about reducing lead ammunition. After participants viewed the Nunavik Child Development Study messages about lead, 44% stated they would stop using lead ammunition. However, 28% indicated that they would continue using lead ammunition. We conclude that, while messages had an overall positive effect, further study is required to understand why people continue to use lead ammunition.

The COVID-19 pandemic disproportionately affected Native American people and communities across the United States. Despite unequal losses during the pandemic, Native Americans have high vaccination rates. We provide insight into perspectives of COVID-19 and vaccinations through in-depth interviews with Native Americans. Through this research, we provide a holistic view of how Native Americans perceive vaccines by pairing Indigenous perspectives of risk and the Health Belief Model. We discuss the importance of tribal sovereignty in developing health communication strategies, and the need for messaging that is trusted and culturally appropriate.

Developing artificial intelligence (AI) equitably necessitates understanding how nonexperts conceptualize and share news about technoscientific risk. We examine a model predicting AI information sharing online from an interaction of framing strategies, through psychological proximity to the impacts of AI and perceived AI risk. A panel of N = 412 participants were exposed to either a control message; or one of four manipulated messages related to AI risks. Contrary to expectations, thematically framed explicit risk news primed psychological proximity compared to both a control message and episodic frame condition. Meanwhile, episodic explicit risk frames did not prime psychological proximity over a control message. These results contest the notion that episodic frames should be associated with psychological proximity to a risk over more general framing strategies. Our results support prior research suggesting that where risk-news primes psychological proximity, the decrease in distance is in-turn associated with greater risk perception and increased likelihood of news sharing online.