151: A LOOK AT THE EDUCATION AND PRACTICE OF EVIDENCE BASED LIBRARIANSHIP (EBL)
Bibliographic record
Abstract
Background and aims: The aim of this study was to find out the educational Model of Evidence Based Library and information Practice in the world. It will answer to the following questions: What are the reputed field names for EBL in other countries? In which levels (course or lesson) it is teaching, currently? Which universities are admitting for EBL? In which degrees EBL is supported? What is the prospect for Iranian Ministry of Health to establish this field of study? Methods: The study used citation and library method to find and describe the situation and quality of faculties offering the EBL as course or lesson. Reputed citation databases were searched for evidence and the Google search engine was employed to find and review the websites of universities which are delivering EBL. Results: The finding of this study shows that since the emergence of evidence based librarianship in the literature it has witnessed plenty names and currently is known as Evidence Based Library and Information Practice in the literature. It is offered by United States; Canadian and UK based universities in both the course and lesson level of education. It is almost offered in post graduate, post master and post-doctoral model. It has potential to be delivered as essential post master course for every discipline with research base. Conclusion: This study suggests a master of evidence based information management for Iran with Ministry of Health and Medical Education license. The best model for Iran would be interdisciplinary model of education by Medical Library and Information Science Department and Iranian Center of Evidence Based Medicine joint execution.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".