An Exploration of Journals Requested by Health Sciences Libraries Through DOCLINE Interlibrary Loan During the Early COVID-19 Pandemic
Bibliographic record
Abstract
COVID-19 challenged information exchange globally, including interlibrary loan (ILL). This project explored DOCLINE ILL borrowing data from 15 academic, hospital, and association health sciences libraries before and during the pandemic to understand gaps in ILL coverage. We reviewed aggregate filled and unfilled borrowing data from March to August in 2019 and 2020. We compared these time periods to each other and to system-wide fill rates. We normalized journal titles, added journal price and language, calculated descriptive statistics and odds ratios, and conducted 2-proportion z-tests of differences. In our sample of 14,891 requests, the odds of requests being unfilled were 2.7 times higher in 2020 than in 2019. While the proportion of non-English language content requested did not change, a significantly higher proportion went unfilled in 2020. The rate of unfilled requests for older items also rose significantly between 2019 and 2020. Our findings support the conclusion that the COVID-19 pandemic significantly influenced ILL article request fulfillment in health sciences libraries. Libraries should consider collection development strategies to increase the accessibility of articles held only in print, and those with specialized print collections may want to prioritize digitization of older materials. Future research on the availability, utility, and expense of the materials more likely to remain unfilled should inform publisher backfile prioritization as well as consortial and individual library collection development practices.
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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 | 0.017 | 0.098 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.011 | 0.021 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".