Evidence‐based librarianship:a personal perspective from the medical/nursing realm
Bibliographic record
Abstract
Purpose Seeks to spread the concept of evidence‐based practice beyond the health sector. Design/methodology/approach The authors have worked with physicians and nurses and offer their observations from being part of the development of the skills and tools of evidence‐based practice. Findings Librarians need to increase their reliance on sound evidence to support their programs and services. They also need to become more active in producing and publishing evidence for their peers and others outside the profession. The authors feel that librarians have the abilities to do this, especially if supported by their organizations and institutions with respect to education and resources. Originality/value The paper should be of use in the development of evidence‐based librarianship.
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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 | 0.063 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.010 | 0.030 |
| Scholarly communication | 0.030 | 0.020 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 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".