Bibliographic record
Abstract
Background: This article is the second part of a two-part series reporting a study of the role of the clinical librarian (CL) in the UK.Methods: A qualitative method of semistructured interviews was used to explore in-depth the role of the CL.The interviews provide a rich source of data and give insight into this new and emerging role as practised in the National Health Service (NHS).Similarities and differences are examined between the CL population and reported within themes, specifically personal qualities and skills required, training for the CLs, marketing the CL service, working in the clinical environment, monitoring and evaluation, and the acceptance of the CL in the NHS.Results: A common understanding of the skills and knowledge required to undertake the CL role was shared by the respondents.However, practice differed as this was often dictated by local circumstances.The study confirmed the need for the CLs to work with clinical colleagues in the clinical setting to enhance patient care.Conclusion: The importance of using best evidence to support patient care is a message that is slowly becoming the norm in the NHS and the CL role in this practice is demonstrated by this study.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.278 | 0.137 |
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".