The Moderating Effect of Vaccine Hesitancy on the Relationship between the COVID-19 Vaccine Coverage Index and Vaccine Coverage.

To examine COVID-19 vaccination barriers in the US, this study drew on publicly available county-level data (n = 3130) to investigate the impact of vaccine hesitancy on the relationship between county-level social/structural barriers and vaccine coverage. A hierarchical regression was performed to establish the relationship between the COVID-19 Vaccine Coverage Index (CVAC) and vaccine coverage, assess the moderating effect of vaccine hesitancy on this relationship, and explore the influence of ethno-racial composition on vaccine coverage. A significant, negative relationship (r2 = 0.11, f2 = 0.12) between CVAC and vaccine coverage by county was established (step 1). When vaccine hesitancy was introduced as a moderator (step 2), the model significantly explained additional variance in vaccine coverage (r2 = 0.21, f2 = 0.27). Simple slopes analysis indicated a significant interaction effect, whereby the CVAC-vaccine coverage relationship was stronger in low hesitancy counties as compared with high hesitancy counties. Counties with low social/structural barriers (CVAC) but high hesitancy were projected to have 14% lower vaccine coverage. When county-level ethno-racial composition was introduced (step 3), higher proportions of white residents in a county predicted decreased vaccination rates (p < 0.05). Findings indicate that CVAC should be paired with vaccine hesitancy measures to better predict vaccine uptake. Moreover, counties with higher proportions of white residents led to decreases in vaccine uptake, suggesting that future intervention strategies should also target whites to reach herd immunity. We conclude that public health leaders and practitioners should address both social/structural and psychological barriers to vaccination to maximize vaccine coverage, with a particular focus on vaccine hesitancy in communities with minimal social/structural barriers.

Analyzing Twitter for Community-Level Public Health Messaging.

Funded in 2021 by the Centers for Disease Control and Prevention, Communities RISE Together (RISE) aims to increase the reach and effectiveness of messages to address vaccine hesitancy to further health equity. Twitter is a predominant social media source used by communities to share messaging and factual local information with constituents. We looked at the Twitter accounts of the organizations in 10 regional communities to examine social media communication patterns to guide how to increase messaging engagement. Specifically, we focused on Twitter post content, likes, and retweets. Our findings identified certain words-such as "food," "older adults," "equity," and "covid"-that were most associated with increased likes and retweets on the platform. However, the strongest predictor of receiving likes and retweets is the number of followers. Sentiment was a significant, but not meaningful, predictor of tweet engagement.

Examining the concept of equity in community psychology with natural language processing.

Large amounts of text-based data, like study abstracts, often go unanalyzed because the task is laborious. Natural language processing (NLP) uses computer-based algorithms not traditionally implemented in community psychology to effectively and efficiently process text. These methods include examining the frequency of words and phrases, the clustering of topics, and the interrelationships of words. This article applied NLP to explore the concept of equity in community psychology. The COVID-19 crisis has made pre-existing health equity gaps even more salient. Community psychology has a specific interest in working with organizations, systems, and communities to address social determinants that perpetuate inequities by refocusing interventions around achieving health and wellness for all. This article examines how community psychology has discussed equity thus far to identify strengths and gaps for future research and practice. The results showed the prominence of community-based participatory research and the diversity of settings researchers work in. However, the total number of abstracts with equity concepts was lower than expected, which suggests there is a need for a continued focus on equity.

5335 days of Implementation Science: using natural language processing to examine publication trends and topics.

INTRODUCTION: Moving evidence-based practices into the hands of practitioners requires the synthesis and translation of research literature. However, the growing pace of scientific publications across disciplines makes it increasingly difficult to stay abreast of research literature. Natural language processing (NLP) methods are emerging as a valuable strategy for conducting content analyses of academic literature. We sought to apply NLP to identify publication trends in the journal Implementation Science, including key topic clusters and the distribution of topics over time. A parallel study objective was to demonstrate how NLP can be used in research synthesis. METHODS: We examined 1711 Implementation Science abstracts published from February 22, 2006, to October 1, 2020. We retrieved the study data using PubMed's Application Programming Interface (API) to assemble a database. Following standard preprocessing steps, we use topic modeling with Latent Dirichlet allocation (LDA) to cluster the abstracts following a minimization algorithm. RESULTS: We examined 30 topics and computed topic model statistics of quality. Analyses revealed that published articles largely reflect (i) characteristics of research, or (ii) domains of practice. Emergent topic clusters encompassed key terms both salient and common to implementation science. HIV and stroke represent the most commonly published clinical areas. Systematic reviews have grown in topic prominence and coherence, whereas articles pertaining to knowledge translation (KT) have dropped in prominence since 2013. Articles on HIV and implementation effectiveness have increased in topic exclusivity over time. DISCUSSION: We demonstrated how NLP can be used as a synthesis and translation method to identify trends and topics across a large number of (over 1700) articles. With applicability to a variety of research domains, NLP is a promising approach to accelerate the dissemination and uptake of research literature. For future research in implementation science, we encourage the inclusion of more equity-focused studies to expand the impact of implementation science on disadvantaged communities.

A practical implementation science heuristic for organizational readiness: R = MC2.

There are many challenges when an innovation (i.e., a program, process, or policy that is new to an organization) is actively introduced into an organization. One critical component for successful implementation is the organization's readiness for the innovation. In this article, we propose a practical implementation science heuristic, abbreviated as R= MC2 . We propose that organizational readiness involves: 1) the motivation to implement an innovation, 2) the general capacities of an organization, and 3) the innovation-specific capacities needed for a particular innovation. Each of these components can be assessed independently and be used formatively. The heuristic can be used by organizations to assess readiness to implement and by training and technical assistance providers to help build organizational readiness. We present an illustration of the heuristic by showing how behavioral health organizations differ in readiness to implement a peer specialist initiative. Implications for research and practice of organizational readiness are discussed.

Practical implementation science: developing and piloting the quality implementation tool.

According to the Interactive Systems Framework for Dissemination and Implementation, implementation is a major mechanism and concern in bridging research and practice. The growing number of implementation frameworks need to be synthesized and translated so that the science and practice of quality implementation can be furthered. In this article, we: (1) use the synthesis of frameworks developed by Meyers et al. (Am J Commun Psychol, 2012) and translate the results into a practical implementation science tool to use for improving quality of implementation (i.e., the Quality Implementation Tool; QIT), and (2) present some of the benefits and limitations of the tool by describing how the QIT was implemented in two different pilot projects. We discuss how the QIT can be used to guide collaborative planning, monitoring, and evaluation of how an innovation is implemented.