How developing a point of need training tool for evidence synthesis can improve librarian support for researchers
Bibliographic record
Abstract
Medical and health sciences librarians who are involved in evidence synthesis projects will know that systematic reviews are intensely rigorous, requiring research teams to devote significant resources to the methodological process. As expert searchers, librarians are often identified as personnel to conduct the database searching portion and/or are approached as experts in the methodology to guide research teams through the lifecycle of the project. This research method has surged in popularity at our campus and demand for librarian participation is unsustainable. As a response to this, the library created self-directed learning objects in the form of roadmap to assist researchers in learning about the knowledge synthesis methodology in an expedient, self-directed manner. This paper will discuss the creation, implementation and feedback around our educational offering: Systematic & Scoping Reviews: Your Roadmap to Conducting an Evidence Synthesis.
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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 | 0.445 | 0.717 |
| Meta-epidemiology (narrow) | 0.002 | 0.005 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.021 | 0.012 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.026 | 0.053 |
| Open science | 0.008 | 0.022 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.041 | 0.027 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".