Building capacity for librarian support and addressing collaboration challenges by formalizing library systematic review services
Bibliographic record
Abstract
BACKGROUND: Many health sciences librarians are noticing an increase in demand for systematic review support. Developing a strategic approach to supporting systematic review activities can address commonly reported barriers and challenges including time factors, methodological issues, and supporting student-led projects. CASE PRESENTATION: This case report describes how a health sciences library at a mid-sized university developed and implemented a structured and defined systematic review service in order to build capacity for increased librarian support and to maximize librarians' time and expertise. The process also revealed underlying collaboration challenges related to student-led systematic reviews and research quality concerns that needed to be addressed. The steps for developing a formal service included defining the librarian's role and a library service model, building librarian expertise, developing documentation to guide librarians and patrons, piloting and revising the service model, marketing and promoting the service, and evaluating service usage. CONCLUSIONS: The two-tiered service model developed for advisory consultation and collaboration provides a framework for supporting systematic review activities that other libraries can adapt to meet their own needs. Librarian autonomy in deciding whether to collaborate on reviews based on defined and explicit considerations was crucial for maximizing librarians' time and expertise and for promoting higher quality research. Monitoring service usage will be imperative for managing existing and future librarian workload. These data and tracking of research outputs from librarian collaborations may also be used to advocate for new librarian positions.
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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 | 0.491 | 0.572 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.023 | 0.017 |
| Science and technology studies | 0.018 | 0.016 |
| Scholarly communication | 0.038 | 0.045 |
| Open science | 0.016 | 0.083 |
| Research integrity | 0.014 | 0.018 |
| Insufficient payload (model declined to judge) | 0.045 | 0.030 |
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".