Background: A request for consumer health information training for public librarians led to the development of a specialized consumer health reference and health literacy training program by professional consumer health librarians from an academic medical center. Professional consumer health librarians created an interactive presentation aimed at improving public librarians’ ability to respond to consumer health questions and provide vetted health resources.Case Presentation: Building on professional expertise, librarians at Weill Cornell Medicine developed a live class demonstration accompanied by a representative subject LibGuide to support public librarians who assist patrons with health questions. Skills involved in effectively communicating with patrons who are seeking consumer health information include conducting reference interviews, matching patrons’ needs with appropriate resources, teaching useful Internet search methods, assessing health information, and understanding health literacy issues. Originally envisioned as two in-person live demonstrations, the team proactively adapted the program to respond to the stay-at-home social-distancing order put in place in response to the coronavirus disease 2019 (COVID-19) pandemic.Conclusions: The team successfully led an in-person live training session followed by an adapted online training experience, the latter designed to complete the curricula while complying with city and state orders.
Background Laboratory parameters and the associated clinical outcomes have been an area of focus in COVID-19 research globally. Purpose We performed a scoping review to synthesize laboratory values described in the literature and their associations with mortality and disease severity. Methods We identified all primary studies involving laboratory values with clinical outcomes as a primary endpoint by performing data searches in various systematic review databases until 10th August, 2020. Two reviewers independently reviewed all abstracts (13,568 articles) and full text (1126 articles) data. A total of 529 studies involving 165,020 patients from 28 different countries were included. Investigation of the number of studies and patients from a geographical perspective showed that the majority of published literature from January-March 2020 to April-June 2020 was from Asia, though there was a temporal shift in published studies to Europe and the Americas. For each laboratory value, the proportion of studies that noted a statistically significant (p < 0.05) correlation with adverse clinical outcomes (e.g., mortality, disease severity) was tabulated. Results and conclusion Among frequently reported laboratory values, blood urea nitrogen was the most often reported predictor of mortality (91%); neutrophil-to-lymphocyte ratio was the most frequent statistically significant laboratory parameter in predicting disease severity (96%). This review highlights the temporal progression of laboratory value frequencies, as well as potentially distinct utilities of different markers for clinical outcomes of COVID-19. Future research pathways include using this collected data for focused quantitative meta-analyses of particular laboratory values correlated with clinical outcomes of mortality and disease severity.
BACKGROUND Creating credible and engaging health communication materials is knowledge- and labor-intensive. OBJECTIVE We propose a low-cost alternative by classifying lay health articles by relevance and topic using natural language processing (NLP). METHODS With postpartum depression as a case study, we conducted a manual review of online lay articles to classify articles on their relevance to pregnancy and topics. To scale the classification process on relevance and topics, we built models using Bidirectional Encoder Representations from Transformers (BERT), Generative Pre-trained Transformer model (ChatGPT), and Random Forest. RESULTS The gold standard corpus included 392 articles. A BERT-based model performed best (F1= 0.974) in an end-to-end classification of relevance and topics. In a two-step approach, given articles already classified as related to pregnancy, ChatGPT was best (F1 = 0.972) in classifying topics. CONCLUSIONS With NLP, we may repurpose lay reading materials as low-cost and easily accessible health education and communication sources.
Background: The Weill Cornell Medicine, Samuel J. Wood Library’s Systematic Review (SR) service began in 2011, with 2021 marking a decade of service. This paper will describe how the service policies have grown and will break down our service quantitatively over the past 11 years to examine SR timelines and trends. Case Presentation: We evaluated 11 years (2011-2021) of SR request data from our in-house documentation. In the years assessed, there have been 319 SR requests from 20 clinical departments, leading to 101 publications with at least one librarian collaborator listed as co-author. The average review took 642 days to publication, with the longest at 1408 days, and the shortest at 94 days. On average, librarians spent 14.7 hours in total on each review. SR projects were most likely to be abandoned at the title/abstract screening phase. Several policies have been put into place over the years in order to accommodate workflows and demand for our service. Discussion: The SR service has seen several changes since its inception in 2011. Based on the findings and emerging trends discussed here, our service will inevitably evolve further to adapt to these changes, such as machine learning-assisted technology.
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