Federal Library Utilization of LibGuides to Disseminate COVID-19 Information
Bibliographic record
Abstract
Objective – In winter 2019-2020, the world saw the emergence of coronavirus disease (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). More than a year later, the pandemic continues with the U.S. death toll surpassing 550,000. Over the last decade, librarians have increased their roles in infectious disease outbreak response. However, no existing literature exists on use of the widely-used library content management platform, LibGuides, to respond to infectious disease outbreaks. This research explores how Federal Libraries use LibGuides to distribute COVID-19 information throughout the ongoing COVID-19 pandemic. Methods – Survey questions were created and peer-reviewed by colleagues. Survey questions first screened for participant eligibility and collected broad demographic information to assist in identifying duplicate responses from individual libraries, then examined the creation, curation, and maintenance of COVID-19 LibGuides. The survey was hosted in Max.gov, a Federal Government data collection and analysis tool. Invitations to participate in the survey were sent via email to colleagues and listservs and posted to personal social media accounts. The survey was made publicly available for three weeks. Collected data were exported into Excel to clean, quantify, and visualize results. Long form answers were manually reviewed and tagged thematically. Results – Of the 78 eligible respondents, 42% (n = 33) reported that their library uses LibGuides to disseminate COVID-19 information; 45% of these respondents said they spent 10+ hours creating their COVID-19 LibGuide, and 60% of respondents spent <1 hour a week on maintenance and updates. Most LibGuides were created in early spring 2020 as the U.S. first saw an uptick in COVID-19 cases. For marketing purposes, respondents reported using web/internal announcements (75%) and email (50%) most frequently. All respondents reported inclusion of U.S. Government resources in their COVID-19 LibGuides, and a majority also included guidelines, international websites, and databases to inform their user communities. Conclusion – Some Federal Libraries use LibGuides as a tool to share critical information, including as a tool for emergency response. Results show libraries tend to start from scratch and share the same resources, duplicating efforts. To improve efficiency in LibGuide curation and use of library staff time, one solution to consider is the creation of a LibGuides template that any Federal Library can use to quickly set up and adapt an emergency response LibGuide specifically for their users. Additionally, findings show that libraries are uncertain of archiving and preservation plans for their guides post-pandemic, suggesting a need for recommended best practices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.381 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".