Community advisory boards (CABs) are an established approach to ensuring research reflects community priorities. This paper examines two CABs that are part of the HEALing Communities Study which aims to reduce overdose mortality. This analysis aimed to understand CAB members’ expectations, experiences, and perspectives on CAB structure, communication, facilitation, and effectiveness during the first year of an almost fully remote CAB implementation. Current literature exploring these perspectives is limited.

We collected qualitative and survey data simultaneously from members (n = 53) of two sites’ CABs in the first 9 months of CAB development. The survey assessed trust, communication, and relations; we also conducted 32 semi-structured interviews. We analyzed the survey results descriptively. The qualitative data were analyzed using a deductive codebook based on the RE-AIM PRISM framework. Themes were drawn from the combined qualitative data and triangulated with survey results to further enrich the findings.

CAB members expressed strong commitment to overall study goals and valued the representation of occupational sectors. The qualitative data described a dissonance between CAB members’ commitment to the mission and unmet expectations for influencing the study within an advisory role. Survey results indicated lower satisfaction with the research teams’ ability to create a mutually beneficial process, clear communication, and sharing of power.

Building a CAB on a remote platform, within a study utilizing a community engagement strategy, still presents challenges to fully realizing the potential of a CAB. These findings can inform more effective operationalizing of community-engaged research through enhanced CAB engagement.

News media play an important role in raising public awareness, framing public opinions, affecting policy formulation, and acknowledgment of public health issues. Traditional qualitative content analysis for news sentiments and focuses are time-consuming and may not efficiently convey sentiments nor the focuses of news media.

We used descriptive statistics and state-of-art text mining to conduct sentiment analysis and topic modeling, to efficiently analyze over 3 million Reuters news articles during 2007–2017 for identifying their coverage, sentiments, and focuses for public health issues. Based on the top keywords from public health scientific journals, we identified 10 major public health issues (i.e., “air pollution,” “alcohol drinking,” “asthma,” “depression,” “diet,” “exercise,” “obesity,” “pregnancy,” “sexual behavior,” and “smoking”).

The news coverage for seven public health issues, “Smoking,” “Exercise,” “Alcohol drinking,” “Diet,” “Obesity,” “Depression,” and “Asthma” decreased over time. The news coverage for “Sexual behavior,” “Pregnancy,” and “Air pollution” fluctuated during 2007–2017. The sentiments of the news articles for three of the public health issues, “exercise,” “alcohol drinking,” and “diet” were predominately positive and associated such as “energy.” Sentiments for the remaining seven public health issues were mainly negative, linked to negative terms, e.g., diseases. The results of topic modeling reflected the media’s focus on public health issues.

Text mining methods may address the limitations of traditional qualitative approaches. Using big data to understand public health needs is a novel approach that could help clinical and translational science awards programs focus on community-engaged research efforts to address community priorities.