Content Access via Resource Sharing Early in the COVID-19 Pandemic: Findings from Nine Health Science Libraries
Bibliographic record
Abstract
Abstract Objective COVID-19 challenged information exchanged globally, including interlibrary loan (ILL) procedures and processes. This research focused on resource-sharing networks used by Health Sciences Libraries (HSL) before and during the COVID-19 pandemic to identify changes in ILL and Document Delivery (DD) processes both in lending and borrowing. Methods From nine academic and association HSL who had participated in a prior study of DOCLINE usage, researchers requested institutional-level de-identified data on ILL and DD during the early pandemic period March-August 2020 and the comparison period of March-August 2019. We compared the journal article request data with previously reported findings from DOCLINE aggregated data. Results Regarding the number of requests from the nine institutions, five saw a decrease, while four saw an increase. The average rate of journal borrowing decreased by 67.1% (standard deviation (SD) 31.7%) per library, and lending decreased on average by 44.7% (SD 68.2%) per library. Document delivery, on average, decreased by only 1.9%, though this varied widely (SD 45.5%). For the data on monographs loaned during the pandemic, there was a predominance of single request titles unfilled across 2019 and 2020 (n = 1631; 93.5%). Conclusion The predominance of single request titles unfilled during the pandemic when libraries limited their sharing of physical materials argues for a deeper exploration of controlled digital lending of materials held in print. The findings across this study and its related investigations (Lloyd et al., 2022; Bakker et al., 2023) on the impact of the pandemic on resource sharing can inform and enhance preparedness planning, future resource sharing workflows and messaging, budgeting, evidence-based collection development, and dialog with content copyright holders about digitization priorities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".