Content Access via Resource Sharing Early in the COVID-19 Pandemic: Findings from Nine Health Science Libraries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.024 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.026 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.020 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it