Informing interlibrary networking and document supply in the English National Health Service: a comparison of models from five countries and a Caribbean network
Bibliographic record
Abstract
Purpose To identify the issues associated with the introduction of desk top document supply to workers in the UK National Health Service (NHS). Design/methodology/approach This paper assesses network models from five countries: Australia, the USA, Italy, Iceland, and Canada, and BIREME, a Latin American and Caribbean network. Factors considered will include library types involved, organizational structure of library systems, length of system implementation, formats of documents involved, and efficiency of the system. Funding and pricing structures, where information is available, are described. Findings Complementary collections are necessary for the widest, most cost‐effective access to information. Access to electronic resources does not alleviate the need for remote document supply. Automation of library systems should improve the user experience, but does not necessarily replace the need for the involvement of library services and staff. Using software that conforms to the ISO ILL protocol and other industry standards such as Z39.50 allows for coordination of and improved efficiency of remote document supply (RDS) processes. Centralization of RDS does not guarantee an efficient service for users. Originality/value Provides insights into current thinking in the NHS for delivering material electronically directly to end users.
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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 | 0.015 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".