Search

Institutional discovery environments now serve as central resource databases for researchers in the academic environment. Over the last several decades, there have been numerous discovery layer research inquiries centering primarily on user satisfaction measures of discovery system effectiveness. This study focuses on the creation of a largely automated method for evaluating discovery layer quality, utilizing the bibliographic sources from student research projects. Building on past research, the current study replaces a semiautomated Excel Fuzzy Lookup Add-In process with a fully scripted R-based approach, which employs the stringdist R package and applies the Jaro-Winkler distance metric as the matching evaluator. The researchers consider the error rate incurred by relying solely on an automated matching metric. They also use Open Refine for normalization processes and package the tools together on an OSF site for other institutions to use. Since the R-based approach does not require special processing or time and can be reproduced with minimal effort, it will allow future studies and users of our method to capture larger sample sizes, boosting validity. While the assessment process has been streamlined and shows promise, there remain issues in establishing solid connections between research paper bibliographies and discovery layer use. Subsequent research will focus on creating alternatives to paper titles as search proxies that better resemble genuine information-seeking behavior and comparing undergraduate and graduate student interactions within discovery environments. [ABSTRACT FROM AUTHOR]

Discovery environments are ubiquitous in academic libraries but studying their effectiveness and use in an academic environment has mostly centered around user satisfaction, experience, and task analysis. This study aims to create a quantitative, reproducible framework to test the relevancy of results and the overall success of Washington State University's discovery environment (Primo by Ex Libris). Within this framework, the authors use bibliographic citations from student research papers submitted as part of a required university class as the proxy for relevancy. In the context of this study, the researchers created a testing model that includes: (1) a process to produce machine-generated keywords from a corpus of research papers to compare against a set of human-created keywords, (2) a machine process to query a discovery environment to produce search result lists to compare against citation lists, and (3) four metrics to measure the comparative success of different search strategies and the relevancy of the results. This framework is used to move beyond a sentiment or task-based analysis to measure if materials cited in student papers appear in the results list of a production discovery environment. While this initial test of the framework produced fewer matches between researcher-generated search results and student bibliography sources than expected, the authors note that faceted searches represent a greater success rate when compared to open-ended searches. Future work will include comparative (A/B) testing of commonly deployed discovery layer configurations and limiters to measure the impact of local decisions on discovery layer efficacy as well as noting where in the results list a citation match occurs. [ABSTRACT FROM AUTHOR]

This article discusses possible future directions for academic libraries in the post Web/Library 2.0 world. These possible directions include areas such as data literacy, linked data sets, and opportunities for libraries in support of digital humanities. The author provides a brief sketch of the background information regarding the topics and describes how a convergence of technologies and policies has made these directions more evident than in the past. [ABSTRACT FROM PUBLISHER]

Purpose - Internet sites about geographical locations attempt to articulate and convey "sense of place", a concept that relates to the unique identity and meaning attached to place. This paper aims to review the variety of resource types, metadata sources, and navigation features that are used by "sense of place" web sites to communicate with and involve the user. Assessment of place-based digital collection sites is discussed. Design/methodology/approach - Current place-based sites were reviewed to show the diversity and range of content as well as metadata options, applications for users, and web features that make the most of location-specific foci. Findings - Place-based sites present many types of resources and use geospatial, interactive, customization, and other tools to enhance the content, assist the user in finding resources, and develop "sense of place". Assessment of such digital collections is being done but could be used more extensively to improve the sites. Practical implications - Identifying and discussing place-based digital collections will serve to highlight a specialized type of site and collection. Attention to the enhancement of "sense of place" internet sites could further geographical and geospatial interests, education, and web applications. Social implications - "Sense of place" sites provide information about places that have unique history, environmental sensitivity, or special meaning. Digital content is readily available for educational purposes and can be a contribution point for shared history and experience. Originality/value - This paper will be of value to those interested in digital collections primarily focused on a geographical location and how web applications can build on the content to convey "sense of place" to users of the site. [ABSTRACT FROM AUTHOR]

Objective: To compare rates and risk factors of severe COVID-19-related outcomes between American Indian/Alaska Native (AI/AN) and non-Hispanic White people (NHW). Methods: Aggregate Social Vulnerability Index (SVI), COVID-19-related risk factor, hospitalization, and mortality data were obtained from 16 states for January 1, 2020-March 31, 2021. Generalized estimating equation Poisson regression models calculated age-adjusted cumulative incidences, incidence ratios (IR), and 95% confidence intervals (CI) comparing AI/AN and NHW persons by age, sex, and county-level SVI status. Results: Race data were missing for 42.7% of COVID-19 cases, 24.7% of hospitalizations, and 10.1% of deaths. Risk of AI/AN COVID-19 mortality was 2.6 times that of NHW persons (IR 2.6, 95% CI: 1.7 - 3.4); risk of COVID-19-related hospitalization among AI/AN persons was 3.5 times that of NHW (IR: 3.5, 95% CI: 2.7 - 4.3). Severe COVID-19 outcomes were significantly higher for AI/AN persons compared to NHW persons across all age and sex groups. There was no statistically significant difference in COVID-19 outcomes by SVI status. Associations between severe COVID-19 outcomes and co-morbid risk factors were inconsistent. Conclusions: Results describe increased risk of severe COVID-19 outcomes for AI/AN persons compared to NHW persons despite quality issues in public health surveillance data. Data linkages and improved ascertainment reduce race/ethnicity misclassification and improve data quality. COVID-19-related health burdens among AI/AN persons warrant improved access for AI/AN communities to medical countermeasures and healthcare resources. [ABSTRACT FROM AUTHOR]