A rapid evidence‐based service by librarians provided information to answer primary care clinical questions
Bibliographic record
Abstract
BACKGROUND: A librarian consultation service was offered to 88 primary care clinicians during office hours. This included a streamlined evidence-based process to answer questions in fewer than 20 min. This included a contact centre accessed through a Web-based platform and using hand-held devices and computers with Web access. Librarians were given technical training in evidence-based medicine, including how to summarise evidence. OBJECTIVES: To describe the process and lessons learned from developing and operating a rapid response librarian consultation service for primary care clinicians. METHODS: Evaluation included librarian interviews and a clinician exit satisfaction survey. RESULTS: Clinicians were positive about its impact on their clinical practice and decision making. The project revealed some important 'lessons learned' in the clinical use of hand-held devices, knowledge translation and training for clinicians and librarians. CONCLUSIONS: The Just-in-Time Librarian Consultation Service showed that it was possible to provide evidence-based answers to clinical questions in 15 min or less. The project overcame a number of barriers using innovative solutions. There are many opportunities to build on this experience for future joint projects of librarians and healthcare providers.
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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 | 0.013 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.112 | 0.043 |
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".