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.

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.

Clinical Research Coordinators (CRCs) are key members of research teams who ensure rigorous conduction of clinical studies and quality standard compliance. Yet, their roles and responsibilities are still not well defined, and formal recognition of their professional profile is lacking in Italy. This survey of Italian healthcare institutions collected data on centers’ research activities number of CRCs and tasks they performed and explored factors associated with CRC employment.

Cross-sectional study using a brief questionnaire. Data were analyzed by means of graphical representations, histogram, scatter, and polar plots. Multivariable linear regression was specified to test the association between the number of CRCs and a subset of factors.

Data collection took place from February to December 2020. 62/143 institutions (43.4%) responded. Median number of ongoing studies reported by centers was 65 (IQR 29–205); of these, median of sponsored and interventional studies was 32 and 35, respectively. Median number of CRCs employed at each center was 6 (IQR 2–9). The frequency with which activities were reported to be performed by CRCs overlapped with those of Data Managers. Linear multivariable regression analysis revealed a statistically significant association between the number of employed CRCs and the number of sponsored studies (P = 0.01), but not with the total number of studies, geographical location, or institution type.

The association between industry funding and the number of CRCs observed in this study should be further explored to understand the direction of this relationship and to verify whether this may influence compliance with quality standards.