Dataset Information

Identifying the Knowledge Structure and Trends of Outreach in Public Health Care: A Text Network Analysis and Topic Modeling.

ABSTRACT: Outreach programs are considered a key strategy for providing services to underserved populations and play a central role in delivering health-care services. To address this challenge, knowledge relevant to global health outreach programs has recently been expanded. The aims of this study were to analyze the knowledge structure and understand the trends in aspects over time and across regions using text network analysis with NetMiner 4.0. Data analysis by frequency, time and region showed that the central keywords such as patient, care, service and community were found to be highly related to the area, target population, purpose and type of services within the knowledge structure of outreach. As a result of performing topic modeling, knowledge structure in this area consisted of five topics: patient-centered care, HIV care continuum, services related to a specific disease, community-based health-care services and research and education on health programs. Our results newly identified that patient-centered care, specific disease and population have been growing more crucial for all times and countries by the examination of major trends in health-care related outreach research. These findings help health professionals, researchers and policymakers in nursing and public health fields in understanding and developing health-care-related outreach practices and suggest future research direction.

SUBMITTER: Park S

PROVIDER: S-EPMC8431096 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:This study investigated the emergence and use of Twitter, as of July 2023 being rebranded as X, as the main forum for social media communication in parasitology. A dataset of tweets was constructed using a keyword search of Twitter with the search terms 'malaria', 'Plasmodium', 'Leishmania', 'Trypanosoma', 'Toxoplasma' and 'Schistosoma' for the period from 2011 to 2020. Exploratory data analyses of tweet content were conducted, including language, usernames and hashtags. To identify parasitology topics of discussion, keywords and phrases were extracted using KeyBert and biterm topic modelling. The sentiment of tweets was analysed using VADER. The results show that the number of tweets including the keywords increased from 2011 (for malaria) and 2013 (for the others) to 2020, with the highest number of tweets being recorded in 2020. The maximum number of yearly tweets for Plasmodium, Leishmania, Toxoplasma, Trypanosoma and Schistosoma was recorded in 2020 (2804, 2161, 1570, 680 and 360 tweets, respectively). English was the most commonly used language for tweeting, although the percentage varied across the searches. In tweets mentioning Leishmania, only ∼37% were in English, with Spanish being more common. Across all the searches, Portuguese was another common language found. Popular tweets on Toxoplasma contained keywords relating to mental health including depression, anxiety and schizophrenia. The Trypanosoma tweets referenced drugs (benznidazole, nifurtimox) and vectors (bugs, triatomines, tsetse), while the Schistosoma tweets referenced areas of biology including pathology, eggs and snails. A wide variety of individuals and organisations were shown to be associated with Twitter activity. Many journals in the parasitology arena regularly tweet about publications from their journal, and professional societies promote activity and events that are important to them. These represent examples of trusted sources of information, often by experts in their fields. Social media activity of influencers, however, who have large numbers of followers, might have little or no training in science. The existence of such tweeters does raise cause for concern to parasitology, as one may start to question the quality of information being disseminated.

Dataset Information

Identifying the Knowledge Structure and Trends of Outreach in Public Health Care: A Text Network Analysis and Topic Modeling.

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