Benchmarking study of hospital libraries
Bibliographic record
Abstract
Objectives: To assess the current landscape of hospital libraries by collecting benchmarking data from hospital librarians in the U.S. and other countries. Since the last MLA benchmarking survey in 2002 hospital libraries have faced significant changes including downsizing, position and library elimination, and hospital mergers. This survey will provides information to inform the development and implementation of effective advocacy for hospital libraries. Methods: A web-based, anonymous survey was designed to collect information from hospital librarians representing stand-alone hospitals and hospital systems. The 57-question survey was distributed via select list servs, targeting the US and Canada but open to any country. The topic areas covered hospital/health system, library, and library staff demographics; library characteristics and scope of service; interlibrary loan and document delivery; library funding; and library budget. Hospital library benchmarking surveys, including the previous MLA surveys, were reviewed and applicable questions were added. Results: There were a total of 180 respondents but the total number of responses for each question varied. Select results are as follows: of the responding libraries, 67.2% were part of a hospital system; 24.4% had merged with or were bought by another hospital or health system and, of those, 77.1% had acquired 1-5 hospitals in the last 10 years; 77.9% were not for profits; over half (55.2%) had <5,001 FTE in the organization; 56.9% had one library; 47.7% had 1 FTE librarian, 34.9% had 2-5; 82.1% did not or were not able to use social media; 60.7% didn’t have strategic plans; 66.1% belonged to a consortium; 48.2% provided up to 250 search requests a year; 66.3% did not receive funding outside of their organization; 32.5% had budgets for print books totaling less than $1,000; 30.1% had budgets, excluding salaries, of less than $100,000 and 9.7% had budgets over $1M. Conclusions: These findings contribute to the field’s knowledge of hospital library demographics as well as the services provided. The results suggest implications for hospital librarians regarding staffing levels and the depth of services within their unique settings, especially within the context of rapidly expanding health systems.
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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 | 0.026 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".