Beyond traditional library spaces: the practicalities of closing hospital libraries and opening a virtual library
Bibliographic record
Abstract
Introduction: The closure of hospital libraries is a noteworthy trend taking place across North America. A Canadian university and its affiliated health authority chose to close eight hospital libraries and merge them into one virtual library service based on changing use of library services, technology and budgetary concerns. This case study describes the processes and considerations both for closing library spaces and transitioning to a new virtual library service. Description: Project management processes efficiently guided the project to completion. These processes included stakeholder consultation, project proposal, timeline, work breakdown structure and project risk analysis. These along with context specific concerns such as closing physical spaces, communication, staffing and licencing issues impacted the successful completion of the project. The hospital libraries were closed and transitioned to a virtual library service within a six-month period. The new virtual library service launched in January 2018 offering document delivery, literature searching, online training and access to electronic resources licensed for health authority staff. Outcomes: Lessons learned during the transition to a virtual library service are shared to provide support for others considering, planning or actively undergoing a similar transition. Discussion: No librarian wants to close one library let alone several. Budgetary factors pressure health sciences libraries to adapt to new fiscal realities. In the health sciences, online availability and patrons desire for access at the bedside result in the need for libraries to respond to patron driven needs. A virtual library service is one response to the alignment of these factors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.042 | 0.027 |
| Scholarly communication | 0.025 | 0.018 |
| Open science | 0.006 | 0.020 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".