Results of a Survey to Benchmark Canadian Health Facility Libraries
Bibliographic record
Abstract
Introduction: A benchmarking survey of Canadian health facility libraries was conducted to provide statistical data to support health librarians in the anagement of their libraries. The objectives were to determine the status of hospital libraries in Canada and to evaluate whether libraries meet the 2006 CHLA/ABSC Standards for Library and Information Services in Canadian Healthcare Facilities. Methods: An online survey of 63 questions, with headings of institutional profile, administration, staffing, environment, resources, and services, was created and distributed to 250 heads of hospital libraries and to Canadian library email listservs. Results: Many libraries are meeting some aspects of the Standards for administration and organization, management, traditional promotion, and accessibility. Areas of improvement include services, nontraditional promotion, library environment, and staffing. Discussion: There are no current benchmarking data available for Canadian hospital libraries and there have been many political, economic, and technological changes in past years that have had a substantial impact on libraries. Anecdotal data suggest that librarians have responded to these changes through library closures, mergers, consortial affiliations, and modifications to services. Librarians will be able to use the collected data to compare services, establish best practices, make management decisions, and prepare self-studies for accreditation purposes.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".