Cost-effective ways of delivering enquiry services: a rapid review
Bibliographic record
Abstract
BACKGROUND: In the recent times of recession and budget cuts, it is more important than ever for library and information services to deliver cost-effective services. OBJECTIVES: This rapid review aims to examine the evidence for the most cost-effective ways of delivering enquiry services. METHODS: A literature search was conducted on LISA (Library and Information Sciences Abstracts) and MEDLINE. Searches were limited to 2007 onwards. RESULTS: Eight studies met the inclusion criteria. The studies covered hospital and academic libraries in the USA and Canada. Services analysed were 'point-of-care' librarian consultations, staffing models for reference desks and virtual/digital reference services. CONCLUSIONS: Transferable lessons, relevant to health library and information services generally, can be drawn from this rapid review. These suggest that 'point-of-care' librarians for primary care practitioners are a cost-effective way of answering questions. Reference desks can be cost-effectively staffed by student employees or general reference staff, although librarian referral must be provided for more complex and subject-specific enquiries. However, it is not possible to draw any conclusions on virtual/digital reference services because of the limited literature available. Further case analysis studies measuring specific services, particularly enquiry services within a health library and information context, are required.
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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 | 0.011 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".