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.

Network graph representation of COVID-19 scientific publications to aid knowledge discovery.

INTRODUCTION: Numerous scientific journal articles related to COVID-19 have been rapidly published, making navigation and understanding of relationships difficult. METHODS: A graph network was constructed from the publicly available COVID-19 Open Research Dataset (CORD-19) of COVID-19-related publications using an engine leveraging medical knowledge bases to identify discrete medical concepts and an open-source tool (Gephi) to visualise the network. RESULTS: The network shows connections between diseases, medications and procedures identified from the title and abstract of 195 958 COVID-19-related publications (CORD-19 Dataset). Connections between terms with few publications, those unconnected to the main network and those irrelevant were not displayed. Nodes were coloured by knowledge base and the size of the node related to the number of publications containing the term. The data set and visualisations were made publicly accessible via a webtool. CONCLUSION: Knowledge management approaches (text mining and graph networks) can effectively allow rapid navigation and exploration of entity inter-relationships to improve understanding of diseases such as COVID-19.

Science as collaborative knowledge generation.

The COVID-19 pandemic points to the need for scientists to pool their efforts in order to understand this disease and respond to the ensuing crisis. Other global challenges also require such scientific cooperation. Yet in academic institutions, reward structures and incentives are based on systems that primarily fuel the competition between (groups of) scientific researchers. Competition between individual researchers, research groups, research approaches, and scientific disciplines is seen as an important selection mechanism and driver of academic excellence. These expected benefits of competition have come to define the organizational culture in academia. There are clear indications that the overreliance on competitive models undermines cooperative exchanges that might lead to higher quality insights. This damages the well-being and productivity of individual researchers and impedes efforts towards collaborative knowledge generation. Insights from social and organizational psychology on the side effects of relying on performance targets, prioritizing the achievement of success over the avoidance of failure, and emphasizing self-interest and efficiency, clarify implicit mechanisms that may spoil valid attempts at transformation. The analysis presented here elucidates that a broader change in the academic culture is needed to truly benefit from current attempts to create more open and collaborative practices for cumulative knowledge generation.